Diligence report Enterprise AI systems / AI-native operations automation Private unicorn / growth-stage venture-backed company

Distyl AI

Distyl AI Startup Diligence Research Report

Distyl is a high-potential enterprise AI systems company serving Fortune 500 operational workflows through its Distillery platform and forward-deployed teams. The upside case is large AI-native transformation budgets and repeatable high-value deployments; the downside case is opaque financial quality, high-touch services economics, model-provider dependence, and unverifiable customer/impact claims.

Company profile

Distyl AI Startup Diligence Research Report

Distyl AI qualifies as an evidence-backed private unicorn based on public Series B and CB Insights evidence: $175M raised at a $1.8B valuation. The company has credible enterprise AI traction signals, but investability depends on private validation of financials, customer concentration, realized impact, security/compliance, and services-versus-software economics.

Website: distyl.ai
Sector: Enterprise AI systems / AI-native operations automation
Geography: United States (San Francisco, CA; New York, NY presence)
Stage: Private unicorn / growth-stage venture-backed company
Known aliases: Distyl AI Inc., Distyl
Report version: 1.0
Timezone: America/Los_Angeles

Executive summary

Strengths

Public sources support Distyl’s $1.8B private unicorn valuation.
The $175M Series B financing is publicly announced and corroborated by market/news sources.
Founder identities and Palantir-veteran background are supported by multiple public sources.
R&D credibility is supported by research outputs and an OpenAI-referenced BIRD-SQL benchmark result.

Risks

No public operating financials, cap table, burn, or revenue data despite a high valuation.
Customer revenue concentration and top-customer identities are mostly hidden behind anonymized F50 case studies.
Large savings claims are often projected/company-reported and need customer/auditor substantiation.
Strategic dependence on OpenAI/frontier model providers may affect margins, data rights, and continuity.
Enterprise AI competition is intense from consultancies, Palantir, C3 AI, Glean, and others.

Gaps

Audited or reviewed financial statements, management accounts, ARR/revenue, gross margin, burn/runway, backlog, and AR aging are not public.
Fully diluted cap table, preferred terms, investor rights, options/warrants/notes, and liquidation preferences are not public.
Top-15 customers, customer revenue concentration, churn, NRR, renewal dates, and customer references are not public.
OpenAI/model-provider contracts, customer MSAs/SOWs, DPAs/BAAs, and data-rights/liability terms are not public.
SOC 2/HIPAA evidence, security architecture, incident history, and AI governance artifacts are not public.

Recommended next steps

Proceed as a high-interest but high-diligence private unicorn; do not underwrite on public valuation alone.
Prioritize financial/cap-table data-room review and customer-reference calls before valuation work.
Validate realized savings and zero-failure claims with customer attestations, logs, and incident history.
Review model-provider, cloud, customer, and data-processing contracts for dependency and liability risk.
Conduct technical/security diligence on Distillery, SOC/HIPAA claims, evaluation/guardrail systems, and productization economics.

Risk register

high high likelihood

R-007: Competitive intensity from incumbents and AI-native platforms

Consultancies, Palantir, C3 AI, Glean, and other enterprise AI players can compete on executive access, existing contracts, platform breadth, and delivery capacity.

Diligence request: Request win/loss analysis, competitive differentiation evidence, pricing comparisons, customer switching costs, and partner/channel strategy.

high medium likelihood

R-001: Valuation and financial opacity

Distyl has a strong public unicorn valuation signal, but revenue, gross margin, burn, runway, ARR, backlog, debt, tax, and cap table remain private.

Diligence request: Request audited or reviewed financials, monthly management accounts, ARR/revenue bridge, burn/runway, cap table, financing documents, debt schedule, and tax positions.

high medium likelihood

R-002: Customer concentration and anonymized traction

Most large customers are anonymized and revenue by customer is not public, creating uncertainty about concentration, churn, and repeatability.

Diligence request: Request top-15 customer list, ARR by customer, NRR, churn, backlog, renewal dates, concentration, customer references, and permission to validate named accounts.

high medium likelihood

R-003: Outcome and savings attribution risk

Several case studies report projected or forecasted customer savings; attribution, realization, and contractual economics are not public.

Diligence request: Request realized-vs-forecast impact models, customer sign-offs, baseline methodology, fee linkage to outcomes, and post-deployment KPI dashboards.

high medium likelihood

R-004: Frontier model provider dependence

OpenAI and other model-provider relationships are a strategic advantage but may create cost, access, privacy, substitution, and roadmap dependency.

Diligence request: Review model-provider agreements, pricing, SLAs, data-use terms, multi-model fallback, and customer contractual allocation of model-provider risk.

high medium likelihood

R-005: Regulated workflow and AI governance exposure

Healthcare prior authorization and telecom customer interactions can affect regulated decisions, protected data, consumer experience, and audit obligations.

Diligence request: Request model-risk management framework, human-review policies, clinical governance, regulatory counsel memos, DPAs/BAAs, audit logs, and incident history.

high medium likelihood

R-006: Enterprise data security and privacy risk

Distyl claims SOC/HIPAA/security controls, but public evidence lacks actual reports and a dedicated security page returned 404.

Diligence request: Request SOC 2 Type II report, pen-test summary, HIPAA/BAA evidence, data-retention maps, security architecture, subprocessors, and incident log.

high medium likelihood

R-009: Productization versus services margin ambiguity

Distyl markets a proprietary platform, but forward-deployed teams and outcome-based projects may behave economically like high-touch services.

Diligence request: Request software/services revenue split, gross margin by offering, deployment hours, reuse rate, implementation backlog, and unit economics by customer cohort.

Chapter 01

01Financial Information

Public sources strongly verify Distyl’s financing history and unicorn valuation, but core operating financials, projections, backlog, AR, tax, debt, and cap table remain non-public.

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I.A Annual and quarterly financial information for the past three years

not publicly verifiable confidence: high

No audited or management financial statements, sales/gross-profit breakdown, backlog, or AR aging were found in public sources; only financing and qualitative impact claims are public.

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Evidence gaps

Audited/reviewed financial statements, management accounts, ARR/revenue by product/channel/geography, backlog, AR aging, and board packs are missing.

Hidden risks

Revenue, margin, burn, and working-capital quality cannot be assessed from public records.

Follow-up questions

Provide monthly financial statements for the last three fiscal years and YTD, including revenue bridge, gross margin, burn, runway, backlog, AR aging, and budget-vs-actual reporting.

Public financial-information availability matrix
checklist area	public evidence	status	risk implication	needed request
Financial statements and footnotes	None located; company is private/no public ticker signal.	Not publicly verifiable	Cannot evaluate revenue, margin, burn, cash, liabilities, or accounting quality.	Audited/reviewed statements and monthly management accounts.
Budget vs actual and management reports	No board packs or budgets found.	Not publicly verifiable	Cannot test execution discipline against plan.	Board packages, KPI dashboards, budget variance reports.
Sales/gross profit by product, channel, geography	Only qualitative case studies and impact claims.	Not publicly verifiable	Cannot assess software/services mix or geographic exposure.	Revenue and gross margin by product, services/software, customer, channel, geography.
Backlog and accounts receivable aging	No backlog or AR schedule public.	Not publicly verifiable	Potential collection, implementation, and revenue visibility risk.	Backlog by customer/SOW and AR aging with reserves.

I.B Financial Projections

not publicly verifiable confidence: high

Public sources disclose growth narratives, case-study impact, and financing, but no three-year projections, pricing assumptions, capex, working-capital plan, or external-financing assumptions.

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Evidence gaps

Quarterly forecast, pipeline assumptions, bookings, pricing, ACV expansion, churn, capex, and financing assumptions are missing.

Hidden risks

Outcome-based pricing and forward-deployed delivery may create revenue recognition and margin volatility.

Follow-up questions

Provide board-approved three-year model with revenue by product/customer/channel, gross margin by services/software, headcount plan, capex, working capital, and financing assumptions.

Projection driver and assumption diligence matrix
projection driver	public support	missing assumption	risk link
Enterprise AI demand/F500 adoption	Multiple F50/F500 case studies and Series B narrative.	Pipeline conversion, ACV, renewal, expansion, sales-cycle timing.	Customer concentration and GTM scaling.
Outcome-based pricing and customer savings	Case studies cite $220M, $200M+, $23M, +$25M and 47%/80% improvements.	Realized savings, fee share, revenue recognition, collection timing.	Outcome attribution and margin ambiguity.
Platform leverage from Distillery	Platform page claims AI-to-build-AI, audit trails, guardrails, RBAC.	Reuse rate, deployment hours, hosting/model costs, gross margin by cohort.	Productization versus services economics.
External financing	$175M Series B announced.	Runway, burn, future financing need, covenants or investor obligations.	Valuation and financial opacity.

I.C Capital Structure

partially verified confidence: high

Public sources verify $175M Series B at $1.8B valuation and private/unicorn status, but shares, ownership, options, warrants, debt, and off-balance-sheet obligations are not public.

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Evidence gaps

Current cap table, stockholder list, options/warrants/notes, debt, and off-balance-sheet liabilities are missing.

Hidden risks

Preferred equity terms and option-pool expansion could materially change economic ownership and downside protection.

Follow-up questions

Provide fully diluted cap table, charter, investor rights, SAFEs/notes/warrants, option plan, debt instruments, side letters, and liquidation waterfall.

Public financing and valuation chronology
date	event	amount	valuation	investors or sources	diligence note
2023-04-13	Seed funding and OpenAI services alliance announced	$7M	Not public	Coatue and Dell Technologies Capital; OpenAI alliance	Confirm securities issued, terms, and alliance contract.
2024-11-19 / reported prior round	Series A publicly discussed by Lightspeed; Crunchbase reports $20M at $200M valuation	$20M reported	~$200M reported	Lightspeed; Crunchbase News	Confirm exact round size, pre/post-money, and share class.
2025-09-22/23	Series B / unicorn financing	$175M	$1.8B	Lightspeed, Khosla, DST, Coatue, Dell; CB Insights row	Obtain financing documents and liquidation waterfall.
2026-05-16	Public status check	N/A	Private-market valuation only	SEC, Nasdaq Private Market, Forge	No public IPO/ticker evidence found; confirm with company counsel.

Financing terms are public only at headline level.

Capital-structure diligence gaps
item	public status	materiality	request
Shares outstanding and fully diluted ownership	Not public	Determines economic ownership and valuation per share.	Current cap table, option pool, SAFEs/notes/warrants, 409A.
Investor rights and liquidation preferences	Not public	Preferred terms can materially affect common and new-money outcomes.	Charter, investor rights agreement, side letters, liquidation waterfall.
Debt, bank lines, and off-balance-sheet liabilities	No public evidence found	Could change runway and seniority risk.	Debt schedule, bank agreements, leases, commitments, guarantees.
Secondary transactions or transfer restrictions	Private-market pages only	Affects liquidity and current investor basis.	Secondary-sale approvals, ROFR/transfer restrictions, investor basis schedule.

Distyl financing and status timeline Chronological view of public financing and private-company status signals.

Disclosed financing amounts and valuations Bar chart of public financing amounts and reported valuations.

I.D Other financial information

not publicly verifiable confidence: high

Public sources disclose financing history but not tax positions, accounting policies, revenue recognition, debt terms, or basis for current valuation beyond the announced round.

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Evidence gaps

Tax positions, accounting policies, revenue-recognition memos, and financing basis by round are missing.

Hidden risks

Revenue recognition for outcome-based AI projects may be complex if fees depend on milestones or measured savings.

Follow-up questions

Provide tax returns/NOL schedule, revenue-recognition policy, accounting memos, debt/lease commitments, and round-by-round financing documents.

Tax, accounting, and financing-history request list
area	public evidence	priority	request detail
Tax positions and NOLs	None found	High	Tax returns, NOL schedule, state/foreign nexus analysis, sales/use tax review.
Revenue recognition	Outcome-based pricing and project impacts publicly claimed.	High	ASC 606 memo, performance obligations, variable consideration policy, collectability analysis.
Financing basis by round	Seed, Series A, Series B public headlines.	High	Round documents, investor allocations, valuation basis, use-of-proceeds, side letters.
Debt/warrants/other instruments	None found	Medium	Debt instruments, warrants, notes, leases, guarantees, off-balance-sheet obligations.

Chapter 02

02Products

Distyl’s public product story centers on Distillery, AI-native Routines, auditability, guardrails, and forward-deployed systems; case studies support multiple enterprise use cases but technical and economic validation are private.

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II.A Description of each product

partially verified confidence: medium

Distyl describes Distillery as an enterprise AI workflow platform with Routines, evaluation/guardrails, audit trails, RBAC, multi-tenancy, and forward-deployed implementation; public use cases span customer experience, prior authorization, supply chain, analytics, and CPG operations.

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Evidence gaps

Live product documentation, customer admin evidence, security reports, product margins, roadmap, pricing, market share, and cost structure are missing.

Hidden risks

Product may be more services-heavy than platform-heavy; zero-failure and savings claims lack public audit.

Follow-up questions

Provide product demo, architecture/security packet, SOC report, roadmap, deployment inventory, software/services margin split, customer product references, and incident history.

Distillery product capability map
capability	public claim	evidence	verification need
Routines/workflow generation	Generate, refine, and deploy AI-native workflows.	Platform page.	Live demo, customer workflow examples, reuse metrics.
Auditability	Track every input, output, and decision point.	Platform page.	Audit-log samples, retention policy, customer admin reference.
Evaluation and guardrails	Evaluate performance at system/component/LLM task levels.	Platform page and research.	Test-suite coverage, eval thresholds, red-team results, incident response.
Enterprise security	RBAC, multi-tenancy, audit trails, SOC/HIPAA badges.	Platform page.	SOC 2 report, HIPAA/BAA evidence, pen-test, security architecture.

Public case-study impact metrics
case	segment	reported metric	status	diligence focus
Telecom customer experience / AI workforce	F50 telecom	$220M projected customer OpEx savings plus 72% and 82% displayed impact metrics.	Company case study	Actual savings, vendor scope, deployment scale, named customer reference.
Prior authorization decisioning	F50 healthcare payor	$200M+ forecasted annual savings; $2B+ UM spend.	Company case study	Clinical governance, human review, realized savings, compliance.
Supply-chain root cause	F50 hardware manufacturer	80% targeted reduction in root-cause analysis time; built within a quarter.	Company case study	Realized productivity, deployment cost, reuse, customer verification.
AI insight engine	F50 healthcare payor	2,000+ analysts, $23M annual savings, production in <1 quarter.	Company case study	Accuracy, data governance, adoption, savings attribution.
Supply Chain AI Assistant	F50 CPG brand	47% improvement in order-incompletion resolution time and +$25M value.	Company case study	Customer name, value methodology, retention/expansion.

Metrics are reported by Distyl; no independent audit was available.

Product controls and security evidence matrix
control area	public evidence	gaps	priority
SOC/HIPAA	Platform page shows SOC Type 2 and HIPAA certification badges.	Report period, scope, exceptions, BAA template, auditor details.	High
RBAC and multi-tenancy	Platform page claims RBAC and multi-tenancy.	Tenant isolation tests, role model, audit logs, customer admin controls.	High
Guardrails/evaluations	Platform page and Lattice/IFScale research orientation.	Production eval thresholds, false positive/negative rates, red-team reports.	High
Security transparency	/security URL returned 404 during research.	Public trust center, security whitepaper, subprocessors, status page.	Medium

Distillery public architecture map Architecture abstraction based on Distyl’s public platform description.

Reported case-study impact metrics Compares public quantified case-study outcomes across verticals.

Chapter 03

03Customer Information

Public customer evidence is directionally strong but thin: T-Mobile is the clearest named customer signal, while most F50/F500 case studies are anonymized and revenue concentration is undisclosed.

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III.A Top customers by application

partially verified confidence: medium

Public applications include telecom customer experience, healthcare prior authorization, supply-chain root cause, healthcare analytics, and CPG order-resolution workflows; T-Mobile is the only strong named public customer signal found.

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Evidence gaps

Top 15 customers, application ownership, timing of purchases, ARR/revenue by customer, and customer references are missing.

Hidden risks

Anonymized accounts could represent a small number of large customers or pilots rather than durable, diversified recurring revenue.

Follow-up questions

Provide top-customer schedule by revenue and application, signed reference permissions, renewal dates, deployment status, and revenue concentration by customer/vertical.

Public customer/application matrix
customer signal	application	public detail	confidence	follow up
T-Mobile	T-Life AI Assistant / customer experience	Distyl social post says it collaborated; Webby/T-Mobile validate assistant and award/use case.	Medium	Reference call, scope, contract, revenue, KPIs.
F50 telecom provider	AI workforce/customer interactions	Anonymized case with $220M projected OpEx savings.	Medium	Customer identity, actual savings, deployment status.
F50 healthcare payor(s)	Prior authorization and analytics insight engine	Anonymized cases cite $200M+ forecasted savings and $23M annual savings.	Medium	Named references, regulatory signoff, revenue.
F50 hardware manufacturer / CPG brand	Supply chain root cause and order-resolution assistant	Anonymized cases cite 80% targeted root-cause reduction and 47% resolution-time improvement.	Medium	Customer names, adoption logs, margin, retention.

Customer and partner ecosystem map Maps public customer, partner, and supplier signals around Distyl.

III.B Strategic relationships

partially verified confidence: medium

OpenAI is the most material public strategic relationship; investors and model providers appear important, but commercial terms are private.

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Evidence gaps

Revenue contribution, marketing agreements, model-provider contracts, cloud suppliers, and exclusivity terms are missing.

Hidden risks

Strategic dependence on model providers could affect margins, data rights, and differentiation.

Follow-up questions

Provide strategic alliance agreements, model-provider contracts, cloud/vendor spend, data-use terms, co-selling/marketing agreements, and termination rights.

Strategic relationship and supplier matrix
relationship	type	public evidence	diligence need
OpenAI	Model provider / services alliance	BusinessWire alliance announcement; company page says deep partnership.	Contract terms, data rights, pricing, SLAs, exclusivity, termination.
Lightspeed, Khosla, DST, Coatue, Dell	Investors/backers	Series B announcement names participants.	Investor rights, board seats, side letters, strategic obligations.
Cloud/model/data vendors	Top suppliers not public	Inferred from AI platform delivery; not named publicly beyond model-provider context.	Supplier spend, DPAs, BAAs, subprocessors, continuity plans.
Fortune 500 customers	Strategic customer relationships	Public case studies and PR mention Fortune 500/F1000 work.	MSAs, SOWs, renewal terms, customer references, revenue contribution.

III.C Revenue by customer

not publicly verifiable confidence: high

No public revenue-by-customer data was found, and public case studies are generally anonymized.

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Evidence gaps

Revenue by customer, ARR, bookings, NRR, gross margin by customer, and concentration thresholds are missing.

Hidden risks

Enterprise AI consultative deployments can be lumpy and concentrated; a few large accounts may drive most revenue.

Follow-up questions

Provide revenue by customer for the past two fiscal years and current YTD, including ARR/NRR, gross margin, backlog, and accounts over 5% of revenue.

Customer revenue and relationship gap register
area	public status	materiality	request
Top 15 customers and revenue	Not public; most case studies anonymized.	Concentration risk, revenue durability, reference quality.	Top 15 schedule by ARR/revenue, gross margin, application, start date, renewal.
Customer churn/severed relationships	No public disclosure found.	Contradicts production/zero-failure narrative if material.	Churn/cancellation/severed relationship schedule and post-mortems.
Revenue contribution by strategic relationships	Not public.	Alliance and customer dependency risk.	Revenue by partner/customer; model-provider/customer-linked costs.
Vertical revenue mix	Verticals are public; revenue mix is not.	Regulatory and concentration exposure by sector.	Revenue by vertical, product, and geography.

III.D Significant relationships severed within the last two years

not publicly verifiable confidence: medium

No public evidence of severed customer, partner, or supplier relationships was found; absence cannot be treated as confirmation.

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Evidence gaps

Churned customers, terminated SOWs, failed deployments, disputes, and supplier terminations are missing.

Hidden risks

Undisclosed churn or project cancellations could contradict the zero-failure narrative.

Follow-up questions

Provide churn/cancellation log, lost-deal and severed-relationship schedule, dispute notices, and post-mortems for material failed or cancelled projects.

III.E Top suppliers

not publicly verifiable confidence: medium

Top suppliers are not publicly listed; OpenAI/model providers are inferred as critical from the public alliance and platform positioning.

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Evidence gaps

Top suppliers, purchase amounts, cloud/model spend, DPAs, BAAs, and supplier SLAs are missing.

Hidden risks

Model-provider, cloud, data, and security-tool contracts may create margin and continuity risk.

Follow-up questions

Provide top supplier schedule by spend, model and cloud usage, contracts, security terms, subprocessors, and contingency plans.

Chapter 04

04Competition

Distyl competes against large consultancies, operational AI platforms, enterprise AI application vendors, and workplace AI providers; differentiation appears to be forward-deployed enterprise systems plus AI-native methodology.

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IV.A Competitive landscape by market segment

verified confidence: high

Competitive pressure is high because major consultancies and enterprise platforms market similar AI transformation, operational decisioning, and AI-agent capabilities.

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Evidence gaps

Win/loss data, price comparisons, customer switching costs, market share, and analyst rankings are missing.

Hidden risks

Incumbents can bundle AI programs with existing transformation, cloud, ERP, and data relationships.

Follow-up questions

Provide win/loss analysis, competitor battlecards, pricing benchmarks, customer switching-cost evidence, partner/channel map, and defensibility memo.

Competitive landscape by segment
category	representative competitors	public positioning	distyl relative angle
Global consultancies / systems integrators	Accenture, BCG, McKinsey/Deloitte-like peers	Large AI transformation practices; Accenture announced $3B AI investment and 80,000 AI talent target.	More AI-native and focused, but much smaller delivery bench and fewer incumbent relationships.
Operational AI platforms	Palantir AIP	Operational decision-making and enterprise AI platform.	Founder pedigree from Palantir may help, but Palantir has established platform, contracts, and deployment scale.
Enterprise AI applications/platform suites	C3 AI	Enterprise AI applications and platform suite.	Distyl emphasizes custom AI-native workflows and forward deployment over packaged suite breadth.
Work AI / knowledge assistants	Glean	Work AI assistant, agents, and enterprise search.	Distyl focuses on operational workflows and process transformation rather than broad knowledge-search layer.

Basis of competition and diligence tests
dimension	distyl claim or signal	competitor pressure	diligence test
Speed to production	Concept to production in weeks; applications within a quarter.	Consultancies can staff large programs; platforms can sell prebuilt tools.	Compare deployment timeline, hours, reuse, and customer acceptance versus competitors.
Operational depth	Forward-deployed engineers/researchers own outcomes.	Palantir and consultancies have operational integration heritage.	Review reference calls, integration complexity, and post-launch ownership.
Technical credibility	Research outputs and BIRD-SQL benchmark.	Model providers and AI labs may commoditize techniques.	Evaluate proprietary datasets, eval infrastructure, patents/trade secrets, and production accuracy.
Economic model	Outcome-based pricing and billion-dollar initiatives.	Incumbents may bundle, discount, or absorb services margin.	Compare gross margins, payback, contract terms, and customer value share.

Enterprise AI competitive market map Positions Distyl relative to public competitor categories.

Chapter 05

05Marketing, Sales, and Distribution

Distyl’s public GTM appears executive-led and proof-led: case studies, PR, investor network, model-provider partnerships, research credibility, and social/customer references; private sales productivity data is essential.

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V.A Strategy and implementation

partially verified confidence: medium

Public messaging positions Distyl as an AI-native systems partner for Fortune 500 operations with forward-deployed teams, outcome-based pricing, and rapid time-to-value.

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Evidence gaps

Marketing spend, channel economics, international distribution, CAC, and campaign conversion are missing.

Hidden risks

Outcome-based marketing may overstate customer value if savings are projected rather than realized.

Follow-up questions

Provide GTM plan, marketing budget, channel attribution, customer acquisition costs, sales cycle by segment, and realized outcome case-study validation.

Public GTM channel and proof inventory
channel	evidence	strength	gap
Funding and press	PRNewswire Series B, BusinessWire seed, Crunchbase News.	High credibility for valuation/backers.	Does not prove pipeline quality or customer economics.
Case studies	Five F50 case studies with quantified impacts.	Strong use-case storytelling.	Mostly anonymized and company-controlled.
Research/technical proof	Research page, OpenAI benchmark reference, arXiv papers.	Differentiates technical credibility.	Does not show sales conversion.
Named customer/social proof	Distyl post about T-Mobile; Webby/T-Mobile validate assistant and award.	Named customer signal.	Vendor role and economics not disclosed by T-Mobile source.

Public GTM evidence funnel Conceptual funnel from public awareness to customer expansion based on public proof points.

V.B Major Customers

partially verified confidence: medium

T-Mobile provides the clearest named public major-customer signal, while broader major-customer status, trend, and expansion pipeline are private.

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Evidence gaps

Major-customer trend, account plans, pipeline, renewal/expansion forecasts, and customer health scores are missing.

Hidden risks

Public proof may lag or omit customer churn, stalled pilots, or low-margin expansion work.

Follow-up questions

Provide account plans for top customers, pipeline by stage, renewal and expansion forecast, T-Mobile reference, customer health scores, and realized impact proofs.

V.C Principal avenues for generating new business

partially verified confidence: medium

Likely public avenues include investor/board referrals, model-provider ecosystem relationships, research credibility, executive outbound, case studies, PR, and customer references.

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Evidence gaps

Channel mix, sourced pipeline, conversion rates, CAC, referral dependency, and partner economics are missing.

Hidden risks

Dependence on founder/investor networks may limit scalable new-logo acquisition.

Follow-up questions

Provide CRM pipeline exports, lead-source attribution, partner-sourced revenue, founder-sourced revenue, and conversion by funnel stage.

V.D Sales force productivity model

not publicly verifiable confidence: high

No public sales-compensation, quota, sales-cycle, attainment, or sales hiring plan was found.

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Evidence gaps

Quota capacity, compensation, sales headcount, attainment, win rate, and sales cycle are missing.

Hidden risks

Large-enterprise sales cycles could delay revenue conversion and increase burn despite strong customer interest.

Follow-up questions

Provide sales org chart, compensation plans, quotas, attainment, stage conversion, ACV, sales cycle, pipeline coverage, and hiring plan.

Sales productivity and budget unavailable metrics
metric	public status	why needed	request
Sales headcount, quotas, attainment	Not public	Measures capacity to convert Fortune 500 pipeline.	Sales org, quota model, attainment by rep/segment.
Sales cycle and conversion	Not public	Enterprise deployment cycles affect revenue timing and burn.	CRM stage history, conversion rates, cycle length by segment.
CAC/payback and marketing budget	Not public	Tests whether GTM can scale without excessive burn.	CAC, payback, marketing budget, channel attribution.
Delivery capacity per new customer	Not public	Links GTM growth to services staffing and gross margin.	Implementation hours, forward-deployed staffing ratios, utilization, backlog.

V.E Ability to implement marketing plan with current and projected budgets

not publicly verifiable confidence: high

Budget sufficiency cannot be assessed publicly because marketing spend, sales capacity, and operating plan are not disclosed.

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Evidence gaps

Budget, hiring plan, CAC, customer payback, and channel productivity are missing.

Hidden risks

Underfunded GTM or delivery hiring could slow conversion from high-profile proof points to repeatable revenue.

Follow-up questions

Provide current and projected GTM budget, hiring plan, CAC/payback, channel ROI, and scenario analysis for enterprise sales-cycle elongation.

Chapter 06

06Research and Development

Distyl shows credible public R&D output in systems, guardrails, instruction following, and Text-to-SQL; commercial moat depends on integration into Distillery, proprietary customer workflows, and protected know-how.

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VI.A Description of R&D organization

verified confidence: medium

Public research output and team pedigree support a serious R&D function, but internal organization, spend, and personnel roster are private.

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Evidence gaps

R&D org chart, budget, roadmap, key researcher retention, and proprietary datasets are missing.

Hidden risks

Research visibility may not translate into defensible product if competitors can replicate methods with frontier models.

Follow-up questions

Provide R&D org chart, roadmap, spend by project, research-to-product mapping, benchmark reproducibility, and key technical personnel retention plans.

R&D output and technical proof points
output	date or period	topic	public signal	commercial relevance
BIRD-SQL benchmark / GPT-4o fine-tuning	2024-08-20	Text-to-SQL execution accuracy	OpenAI/Distyl reference says Distyl ranked first with 71.83% execution accuracy.	Supports analytics/insight-engine use cases.
End-to-end Text-to-SQL Generation within an Analytics Insight Engine	2024-06-17	Enterprise Text-to-SQL architecture	arXiv technical paper.	Aligned with healthcare insight-engine case study.
GenEdit	2025-03-31	Continuous improvement for enterprise Text-to-SQL	arXiv technical paper.	Supports feedback loops and company-specific knowledge bases.
IFScale / Lattice-related research	2025	Instruction following and guardrails	Research page and arXiv preprint.	Supports reliability/guardrails for production workflows.

Public R&D output count by year Counts notable public research/technical proof points by year from reviewed sources.

VI.B New Product Pipeline

partially verified confidence: medium

The public pipeline centers on AI systems that build/evaluate/improve workflows, guardrails, and enterprise analytics, but timing, cost, and commercialization plans are private.

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Evidence gaps

Product roadmap, development cost, launch timing, proprietary IP, and critical dependencies are missing.

Hidden risks

Pipeline may depend on external model capabilities and shifting benchmark landscapes.

Follow-up questions

Provide roadmap, release plan, R&D budget, critical technology dependencies, patent/trade-secret strategy, and product adoption metrics for recent research outputs.

New product pipeline risk map
pipeline theme	public basis	critical dependency	risk
AI systems to build workflows	Platform page says AI to build AI; Distillery generates Routines.	Customer process extraction and SME feedback loops.	Services effort may remain high if workflow generation is not reusable.
Guardrails and evaluation	Platform page and Lattice/IFScale research themes.	Robust eval datasets and adversarial testing.	Regulated workflows require high reliability and auditability.
Enterprise analytics/Text-to-SQL	BIRD-SQL result, GenEdit, analytics insight engine paper/case.	Schema/context management, accuracy, data access controls.	Benchmarks may not transfer to complex customer environments.
Proprietary moat	Research output and Distillery platform claims.	IP, trade secrets, customer data/workflow corpus.	Competitors and model providers may commoditize methods.

Chapter 07

07Management and Personnel

Founder credentials and headcount are publicly supportable, but detailed org chart, compensation, incentive plans, and turnover remain private.

EC-025 EC-026 EC-027 EC-028

VII.A Organization Chart

partially verified confidence: medium

Public sources identify CEO/co-founder Arjun Prakash, COO/co-founder Derek Ho, and broad engineering/research team pedigree, but no complete reporting org chart was found.

EC-025 EC-026

Evidence gaps

Complete org chart, reporting lines, board/advisor list, and functional headcount are missing.

Hidden risks

Forward-deployed delivery may strain leadership and functional management as customer deployments scale.

Follow-up questions

Provide current and projected org chart by function/location, leadership roles, board/advisor list, and span-of-control review.

Management and team verification table
person or group	role or claim	public evidence	verification status	diligence request
Arjun Prakash	CEO/co-founder; Palantir veteran	BusinessWire, Lightspeed, Crunchbase News.	Verified publicly	Bio, references, employment history, equity/vesting.
Derek Ho	COO/co-founder; Palantir veteran	BusinessWire, Lightspeed, Crunchbase News.	Verified publicly	Bio, references, employment history, equity/vesting.
Broader team	Experience from Palantir, Apple, BlackRock, Citadel, Snorkel AI, labs	BusinessWire and company page.	Partially verified	Current roster, prior-employer verification, key-person retention.
Board/advisors/investors	Backed by top VCs and industry leaders	Series B announcement and company page.	Partially verified	Board minutes, investor rights, advisor agreements, conflicts.

Public management and personnel snapshot Publicly supportable management/personnel snapshot, not a full internal org chart.

Public sources do not provide reporting lines; figure intentionally avoids inferred hierarchy beyond founder roles.

VII.B Historical and projected headcount by function and location

partially verified confidence: medium

LinkedIn shows 149 associated employees, company size 51-200, San Francisco headquarters, and New York presence; no historical or projected breakdown by function/location was public.

EC-027

Evidence gaps

Historical headcount, projected headcount, contractors, attrition, open reqs, and function/location breakdown are missing.

Hidden risks

Rapid hiring may dilute culture or increase burn if revenue conversion lags.

Follow-up questions

Provide payroll roster, contractor list, 24-month headcount history, hiring plan, location breakdown, and open requisitions.

Headcount and personnel data gaps
category	public evidence	missing data	risk
Current headcount	LinkedIn: 149 associated employees; 51-200 company size.	Payroll count, contractors, part-time, geographic/function split.	LinkedIn may over/under-count actual staff.
Locations	LinkedIn: San Francisco headquarters and New York location.	Office leases, remote employees, state payroll nexus.	Employment/tax compliance and talent-market cost exposure.
Compensation and incentive equity	Not public.	Compensation bands, option grants, 409A, benefits.	Retention risk in competitive AI labor market.
Turnover and employee relations	Not public.	Turnover, HR complaints, engagement, retention grants.	Delivery quality and customer continuity risk.

VII.C Senior management biographies

verified confidence: high

Founder identities and relevant Palantir backgrounds are publicly supported, but detailed bios, references, and tenure histories require company disclosure.

EC-025

Evidence gaps

Detailed bios, references, employment dates, board roles, founder vesting, and succession plan are missing.

Hidden risks

Key-person risk may be elevated if founder relationships drive major enterprise deals.

Follow-up questions

Provide management bios, reference checks, founder equity/vesting, employment agreements, invention assignments, and succession plan.

VII.D Compensation arrangements

not publicly verifiable confidence: high

No executive compensation, employment agreements, or benefit-plan details were public.

EC-028

Evidence gaps

Employment agreements, compensation bands, bonuses, benefits, severance, and change-of-control terms are missing.

Hidden risks

Misaligned incentives or uncompetitive compensation could create retention risk in scarce AI talent markets.

Follow-up questions

Provide executive employment agreements, compensation schedule, bonus plans, benefits, severance/change-of-control terms, and payroll compliance review.

VII.E Incentive stock plans

not publicly verifiable confidence: high

No option plan, equity grant schedule, or exercise/vesting terms were publicly available.

EC-028

Evidence gaps

Option pool, grants, vesting, exercise prices, repurchase rights, and 409A valuations are missing.

Hidden risks

Option-pool insufficiency or high strike prices could impair hiring and retention.

Follow-up questions

Provide option plan, grant ledger, vesting schedule, 409A reports, board approvals, and projected option-pool needs.

VII.F Significant employee relations problems, past or present

not publicly verifiable confidence: medium

No public evidence of significant employee-relations problems was found, but public sources are not comprehensive.

EC-028

Evidence gaps

HR complaints, litigation/arbitration, investigations, turnover causes, and compliance audits are missing.

Hidden risks

Undisclosed employee disputes, contractor classification issues, or culture problems could affect scaling.

Follow-up questions

Provide HR complaint log, employee-relations matters, settlements, arbitration claims, contractor classification review, and culture/engagement survey results.

VII.G Personnel Turnover

not publicly verifiable confidence: high

Personnel turnover data and retention-benefit plans were not publicly available.

EC-027 EC-028

Evidence gaps

Voluntary/involuntary attrition, regretted loss, offer acceptance, retention grants, and benefits related to retention are missing.

Hidden risks

High turnover among forward-deployed engineers or researchers could threaten delivery quality and customer continuity.

Follow-up questions

Provide 24-month turnover data by function/location/manager, regretted loss analysis, retention grants, and employee engagement data.

Chapter 08

08Legal and Related Matters

No public litigation signal was found in targeted searches, and a trademark application exists, but comprehensive legal, regulatory, contract, insurance, and security diligence remains incomplete.

EC-029 EC-030 EC-031 EC-032 EC-033

VIII.A Pending lawsuits against the Company

partially verified confidence: medium

CourtListener searches returned no public results for Distyl AI, but legal clearance requires counsel confirmation and broader searches.

EC-029

Evidence gaps

Counsel litigation schedule, state searches, arbitration, demand letters, and threatened claims are missing.

Hidden risks

Sealed, state, arbitration, employment, customer, or IP disputes may not appear in public federal search results.

Follow-up questions

Provide litigation and claims schedule, counsel letter, demand letters, threatened claims, arbitration matters, and state/IP/employment search results.

Public legal-record search and insurance gaps
topic	public result	confidence	remaining gap
Lawsuits against company	CourtListener exact company-name search returned 0 results during research.	Medium	State courts, arbitration, sealed matters, demand letters, counsel confirmation.
Founder/company litigation query	CourtListener Distyl/Arjun query returned 0 results during research.	Medium	Name variants, state/local matters, employment disputes.
Insurance coverage	No public policies or coverage limits found.	High that not public	Cyber, tech E&O, D&O, EPLI, professional liability, exclusions, claims history.
Regulatory agency problems	No public agency problem found in reviewed sources; privacy policy exists.	Inconclusive	Agency correspondence, audits, data maps, customer compliance obligations.

No public-search absence should be treated as definitive legal clearance.

Key diligence risk heatmap Heatmap of material risks identified during public-source diligence.

VIII.B Pending lawsuits initiated by Company

partially verified confidence: medium

No public evidence of lawsuits initiated by Distyl was found in targeted CourtListener searches.

EC-029

Evidence gaps

Company-initiated claims, collections, IP enforcement, and settlement details are missing.

Hidden risks

Undisclosed enforcement actions could involve IP, employees, or customer payment disputes.

Follow-up questions

Provide schedule of claims initiated or threatened by Distyl, including IP, employment, collections, and contract disputes.

VIII.C Environmental and employee safety issues and liabilities

partially verified confidence: medium

As a software/AI services company, traditional environmental exposure appears limited publicly, but employee safety, data-center/cloud indirect risk, and AI safety/regulatory obligations require review.

EC-032

Evidence gaps

Workplace safety policies, AI safety governance, regulatory memos, incident logs, and insurance coverage are missing.

Hidden risks

AI decision-support errors in clinical or customer contexts can create safety, fairness, and consumer-protection exposure.

Follow-up questions

Provide AI safety policy, incident-response procedures, regulatory counsel memos, workplace safety records, cyber/E&O insurance, and customer allocation of liability.

Legal, regulatory, IP, and contract exposure matrix
area	public evidence	status	primary risk	request
Trademark/IP	DISTYL trademark application serial 99611159, new application, classes 041/042.	Partially verified	Brand/IP protection incomplete; patents/trade secrets unknown.	IP schedule, patents/trademarks, invention assignments, OSS/SBOM.
Privacy/security	Privacy policy; platform claims SOC/HIPAA, RBAC, audit trails; security page 404.	Partially verified	Cyber/privacy/HIPAA and enterprise contract compliance.	SOC 2, HIPAA/BAA, DPA, pen-test, incident log, subprocessors.
Regulated AI workflows	Healthcare prior authorization and telecom customer-support use cases.	Partially verified	Clinical/consumer decisioning, fairness, audit, and compliance exposure.	Regulatory memos, model governance, audit logs, human-review policy.
Material contracts	OpenAI alliance announced; customer contracts anonymized/not public.	Not publicly verifiable	Data rights, termination, liability, exclusivity, revenue durability.	Material contracts, MSAs/SOWs, model-provider/cloud contracts, DPAs/BAAs.

VIII.D Material patents, copyrights, licenses, and trademarks

partially verified confidence: medium

A DISTYL trademark application is public; patents, copyrights, software licenses, proprietary datasets, OSS use, and invention assignments were not publicly verified.

EC-030

Evidence gaps

Patent docket, copyrights, trade secrets, OSS SBOM, licenses, assignments, and customer IP ownership terms are missing.

Hidden risks

If core platform IP is mostly know-how and customer-specific workflows, defensibility depends on trade-secret controls and contracts.

Follow-up questions

Provide IP schedule, patent/trademark docket, OSS/SBOM, invention assignments, customer IP clauses, model/data licenses, and trade-secret controls.

VIII.E Insurance coverage and material exposures

not publicly verifiable confidence: high

No insurance coverage information was publicly available; cyber, technology E&O, D&O, employment practices, and professional liability are important exposures.

EC-031 EC-032 EC-033

Evidence gaps

Insurance policies, limits, exclusions, claims history, and customer-required coverage are missing.

Hidden risks

Uninsured AI decisioning, cyber, indemnity, or professional-liability claims could be material.

Follow-up questions

Provide insurance schedule, policies, cyber/E&O/D&O/EPLI limits, exclusions, claims history, and customer insurance requirements.

VIII.F Material contracts

not publicly verifiable confidence: high

Material contracts are not public beyond the OpenAI alliance announcement and anonymized customer case studies.

EC-016 EC-033

Evidence gaps

Customer MSAs/SOWs, model-provider agreements, DPAs/BAAs, cloud contracts, vendor agreements, and investor side letters are missing.

Hidden risks

Unfavorable termination, exclusivity, data-use, indemnity, or outcome-payment terms could materially affect value.

Follow-up questions

Provide material contracts list, top customer MSAs/SOWs, OpenAI/model-provider contracts, DPAs/BAAs, cloud/vendor agreements, and contract-risk summary.

VIII.G Regulatory agency problems

inconclusive confidence: medium

No public regulatory agency problems were found, but Distyl’s healthcare, telecom, privacy, and AI governance exposure warrants specific counsel review.

EC-031 EC-032

Evidence gaps

Regulatory correspondence, audits, compliance policies, DPAs/BAAs, data maps, and agency inquiries are missing.

Hidden risks

Regulatory scrutiny of AI decisioning and data privacy may increase during customer deployment life.

Follow-up questions

Provide regulatory correspondence, privacy/data maps, HIPAA/BAA materials, AI governance policies, model audit logs, and legal opinions on healthcare/telecom deployments.

Evidence

Evidence claims
ID	Claim	Status	Sources
EC-001	Distyl AI appears on a public unicorn database as a United States, San Francisco enterprise-technology company valued at $1.8B with a 2025-09-22 unicorn date.	verified high	SRC-014
EC-002	Distyl announced a $175M financing round at a $1.8B valuation with participation from Lightspeed, Khosla, DST, Coatue, and Dell-related investors.	verified high	SRC-010
EC-003	Distyl announced a 2023 services alliance with OpenAI and $7M seed funding led by Coatue and Dell Technologies Capital.	verified high	SRC-011
EC-004	Public reporting describes a funding path from seed to Series A to Series B, including a prior $20M Series A at approximately $200M valuation and total raised just over $200M.	partially verified medium	SRC-012 SRC-013
EC-005	Distyl appears to remain private, with no public ticker, no matching SEC public-company result in searched records, and private-market profiles rather than exchange listings.	verified medium	SRC-019 SRC-020 SRC-021
EC-006	Audited financial statements, revenue breakdowns, backlog, accounts receivable aging, tax positions, debt instruments, and ownership details were not publicly available in reviewed sources.	not publicly verifiable high	SRC-010 SRC-013 SRC-019 SRC-020 SRC-021
EC-007	Distyl describes Distillery as a proprietary enterprise platform for generating, refining, and deploying auditable AI-native workflows called Routines.	partially verified medium	SRC-002
EC-008	Distyl publicly claims that while the AI failure rate is 95%, its own production record is zero failures.	partially verified low	SRC-001
EC-009	Distyl publishes five anonymized F50 case studies across telecom, healthcare, hardware manufacturing, healthcare analytics, and CPG brand operations.	partially verified medium	SRC-004 SRC-005 SRC-006 SRC-007 SRC-008 SRC-009
EC-010	The F50 telecom case study reports $220M projected customer OpEx savings and other operational metrics for AI workforce/customer-experience work.	partially verified medium	SRC-005
EC-011	The healthcare prior-authorization case reports $200M+ forecasted annual savings against $2B+ annual utilization-management spend.	partially verified medium	SRC-006
EC-012	The hardware-manufacturer case reports a targeted 80% reduction in root-cause analysis time and production application build within a quarter.	partially verified medium	SRC-007
EC-013	The healthcare analytics case reports a production AI insight engine serving 2,000+ analysts, $23M annual savings, and launch in under one quarter.	partially verified medium	SRC-008
EC-014	The CPG brand case reports a 47% improvement in order-incompletion resolution time and +$25M of business value.	partially verified medium	SRC-009
EC-015	T-Mobile is the strongest publicly named customer signal: Distyl says it collaborated on the T-Life AI Assistant; Webby and T-Mobile sources independently validate the assistant and award/use-case context.	partially verified medium	SRC-016 SRC-017 SRC-018
EC-016	Distyl’s platform and delivery model depend materially on frontier model providers and partnerships, especially OpenAI.	partially verified medium	SRC-003 SRC-011 SRC-012
EC-017	Most customer identities, top-customer rankings, revenue by customer, churn, and severed relationships are not publicly disclosed.	not publicly verifiable high	SRC-004 SRC-005 SRC-006 SRC-007 SRC-008 SRC-009 SRC-010 SRC-012
EC-018	Distyl competes in a crowded enterprise AI and services market against AI-native platforms, incumbents, and global consultancies.	verified high	SRC-027 SRC-028 SRC-029 SRC-030 SRC-031
EC-019	Distyl positions itself around forward-deployed teams, rapid time-to-value, outcome-based pricing, and Fortune 500 operational transformation.	partially verified medium	SRC-002 SRC-003
EC-020	Distyl’s public marketing channels include press releases, investor thought leadership, case studies, research publications, LinkedIn/social proof, and customer/award references.	verified medium	SRC-010 SRC-011 SRC-012 SRC-004 SRC-015 SRC-016 SRC-017 SRC-018
EC-021	Sales force productivity, compensation, average quota, sales cycle, new-hire plan, and marketing budget sufficiency are not publicly disclosed.	not publicly verifiable high	SRC-003 SRC-010 SRC-012 SRC-015
EC-022	Distyl has an active research program focused on systems, guardrails, instruction following, and enterprise Text-to-SQL/analytics systems.	verified medium	SRC-033 SRC-034 SRC-035 SRC-036
EC-023	OpenAI referenced Distyl ranking first on the BIRD-SQL benchmark with 71.83% execution accuracy using fine-tuned GPT-4o.	verified medium	SRC-032
EC-024	Distyl’s R&D pipeline appears focused on AI systems that build, evaluate, and improve other AI systems, including guardrails and enterprise analytics copilots.	partially verified medium	SRC-002 SRC-033 SRC-034 SRC-035 SRC-036
EC-025	Public sources identify Arjun Prakash as CEO/co-founder and Derek Ho as COO/co-founder; both are described as Palantir veterans.	verified high	SRC-011 SRC-012 SRC-013
EC-026	Distyl describes its broader team as coming from Palantir, Apple, BlackRock, Citadel, Snorkel AI, and national laboratories.	partially verified medium	SRC-003 SRC-011
EC-027	LinkedIn lists Distyl as a San Francisco company with New York presence, company size 51-200, and 149 associated employees at access date.	verified medium	SRC-015
EC-028	Executive compensation, employment agreements, benefit plans, incentive stock plans, and turnover data are not publicly available.	not publicly verifiable high	SRC-003 SRC-015
EC-029	Public CourtListener searches returned no apparent litigation results for Distyl AI or a combined Distyl/Arjun Prakash query during research.	verified medium	SRC-023 SRC-024
EC-030	A DISTYL trademark application exists for Distyl AI Inc.; the record was initialized as a new application and not assigned to an examiner as of the listed status date.	verified high	SRC-022
EC-031	Distyl has a public privacy policy and product page claims SOC Type 2/HIPAA-related controls, but a dedicated security page URL returned 404 during research.	partially verified medium	SRC-002 SRC-025 SRC-026
EC-032	Healthcare prior authorization, telecom customer AI, and enterprise workflow automation create regulatory, privacy, safety, and fairness obligations.	partially verified medium	SRC-005 SRC-006 SRC-008 SRC-009 SRC-025
EC-033	Material contracts are not public beyond announced OpenAI alliance and anonymized customer relationships.	not publicly verifiable high	SRC-011 SRC-004 SRC-005 SRC-006 SRC-007 SRC-008 SRC-009
EC-034	No public evidence was found that Distyl has completed an IPO, acquisition, shutdown, or other exit as of the research date.	verified medium	SRC-003 SRC-010 SRC-015 SRC-019 SRC-020 SRC-021
EC-035	Distyl publicly claims its systems reach 120M+ end users and have produced hundreds of millions of dollars of operating impact.	partially verified medium	SRC-010 SRC-015
EC-036	Distyl says its clients include healthcare, telecom, manufacturing, and retail; public case studies also include CPG and hardware-manufacturing operations.	partially verified medium	SRC-003 SRC-004

Sources
ID	Publisher	Title	Accessed
SRC-001	Distyl AI	Distyl AI homepage	2026-05-16
SRC-002	Distyl AI	Platform \| Distyl AI	2026-05-16
SRC-003	Distyl AI	Company \| Distyl AI	2026-05-16
SRC-004	Distyl AI	Case Studies \| Distyl AI	2026-05-16
SRC-005	Distyl AI	Telecom Provider \| Distyl AI	2026-05-16
SRC-006	Distyl AI	Healthcare Payor \| Distyl AI	2026-05-16
SRC-007	Distyl AI	Hardware Manufacturer \| Distyl AI	2026-05-16
SRC-008	Distyl AI	Healthcare Payor 2 \| Distyl AI	2026-05-16
SRC-009	Distyl AI	CPG Brand \| Distyl AI	2026-05-16
SRC-010	PRNewswire	Distyl AI Raises $175 Million at $1.8 Billion Valuation to Power the Fortune 500 with AI-Native Systems	2026-05-16
SRC-011	BusinessWire	Distyl AI Forms Services Alliance with OpenAI and Raises $7M in Seed Funding	2026-05-16
SRC-012	Lightspeed Venture Partners	Leading Distyl AI’s Series A	2026-05-16
SRC-013	Crunchbase News	Distyl AI Hits Unicorn Valuation With New Funding Round	2026-05-16
SRC-014	CB Insights	The Complete List Of Unicorn Companies - Distyl AI row	2026-05-16
SRC-015	LinkedIn	Distyl LinkedIn company profile	2026-05-16
SRC-016	LinkedIn / Distyl AI	Distyl AI LinkedIn post about T-Mobile T-Life AI Assistant Webby recognition	2026-05-16
SRC-017	The Webby Awards	T-Mobile AI Assistant \| The Webby Awards	2026-05-16
SRC-018	T-Mobile	T-Life App Gets an AI Assistant and More	2026-05-16
SRC-019	U.S. Securities and Exchange Commission	SEC EDGAR company search for Distyl AI	2026-05-16
SRC-020	Nasdaq Private Market	Nasdaq Private Market profile for Distyl	2026-05-16
SRC-021	Forge Global	Forge Global pre-IPO profile for Distyl	2026-05-16
SRC-022	Justia / USPTO data	DISTYL trademark application, serial 99611159	2026-05-16
SRC-023	CourtListener	CourtListener search for "Distyl AI"	2026-05-16
SRC-024	CourtListener	CourtListener search for "Distyl" and "Arjun Prakash"	2026-05-16
SRC-025	Distyl AI	Distyl AI Privacy Policy	2026-05-16
SRC-026	Distyl AI	Distyl AI security page availability check	2026-05-16
SRC-027	Accenture	Accenture to Invest $3 Billion in AI to Accelerate Clients’ Reinvention	2026-05-16
SRC-028	Palantir	Palantir Artificial Intelligence Platform	2026-05-16
SRC-029	C3 AI	C3 AI Platform	2026-05-16
SRC-030	Glean	Glean work AI homepage	2026-05-16
SRC-031	Boston Consulting Group	BCG Artificial Intelligence services page	2026-05-16
SRC-032	OpenAI	GPT-4o fine-tuning and Distyl BIRD-SQL benchmark reference	2026-05-16
SRC-033	arXiv	How Many Instructions Can LLMs Follow at Once?	2026-05-16
SRC-034	arXiv	GenEdit: Compounding Operators and Continuous Improvement to Tackle Text-to-SQL in the Enterprise	2026-05-16
SRC-035	arXiv	The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models	2026-05-16
SRC-036	arXiv	End-to-end Text-to-SQL Generation within an Analytics Insight Engine	2026-05-16

Disclaimer

This report is a public-evidence diligence snapshot, not investment advice. Important financial, legal, technical, and contractual facts remain non-public and should be verified directly with management and primary documents before any investment decision.