high high likelihood
R-001: Financial opacity and unverified revenue quality
Audited financials, ARR bridge, margins, burn, cash, debt, and retention are not public; analyst estimates are insufficient for valuation reliance.
Diligence request: Request audited/reviewed financial statements, management accounts, ARR bridge, revenue-recognition memo, deferred revenue, billings, gross-margin breakdown, cash/burn/runway, and debt schedule.
high medium likelihood
R-002: Valuation compression and preference-stack risk
Forge public data imply a reset from $2B in 2023 to $1.24B in 2025; downside protections may change common-equity economics.
Diligence request: Review Series C/D financing documents, liquidation preferences, anti-dilution, warrants, debt, option pool, and 409A.
high medium likelihood
R-003: Customer concentration and contract-quality risk
Public customer logos and case studies do not disclose customer-level ARR, production status, renewal timing, or revenue concentration.
Diligence request: Request top-20 customer schedule, contracts, renewal calendar, NRR/GRR, churn/expansion, implementation status, and reference calls.
high medium likelihood
R-004: Sensitive-data, privacy, security, and AI governance exposure
Instabase processes regulated enterprise documents; public controls need verification against SOC reports, DPAs, subprocessors, model-provider terms, and incidents.
Diligence request: Review SOC 2, HIPAA/BAA scope, DPA, subprocessors, model terms, incident log, cyber insurance, and security remediation evidence.
high medium likelihood
R-006: AI accuracy, drift, and workflow liability risk
Public product controls are positive but unverified; document-processing errors in lending, insurance, banking, or public-sector workflows may cause costly failures.
Diligence request: Run blinded benchmarks, review golden datasets, exception workflows, model-change controls, human review, audit logs, and incident reports.
medium high likelihood
R-005: Competition and commoditization risk
IDP growth attracts specialists, RPA suites, hyperscalers, and LLM platforms; extraction features may commoditize.
Diligence request: Analyze win/loss, product differentiation, pricing trends, customer switching costs, and independent benchmark data.
medium medium likelihood
R-008: Enterprise GTM scalability and implementation burden
Enterprise document automation may require long sales cycles and heavy implementation/support; public hiring and customer stories do not show CAC or services margin.
Diligence request: Review pipeline, CAC payback, implementation hours, services gross margin, quota attainment, sales cycle, and partner channel economics.
medium medium likelihood
R-009: Talent, founder, and R&D execution risk
AI-native enterprise product execution requires scarce technical talent; public leadership/hiring evidence does not reveal attrition, org depth, compensation, or roadmap risk.
Diligence request: Request org chart, headcount plan, attrition, compensation, option pool, key-person plan, R&D roadmap, and engineering metrics.