The internal source-of-truth for what One Agentic is, why it exists, and how it works. Covers the structural failures in VC due diligence, the product, vision, mission, target users, market size, competitive landscape, and differentiators. Includes the road to launch and open questions still to resolve.
Consumption-based credit pricing model built on 100 production runs. 1 credit = 10,000 LLM tokens ($0.025 raw cost). Covers third-party API credits, observed session distribution (CV% 95%), planning scenarios, tier design (Seed / Series / Max), rollover policy, and TAM under credit pricing.
Breaks down the full token cost model behind One Agentic's pricing — raw LLM API rates, the 3× revenue markup, token split per interaction, and cost per workflow type. Benchmarks ~$2,100/yr for a 5-person fund against Harmonic, PitchBook, Crunchbase, and Clay.
Edit the credit-model variables that drive Pricing Assumptions v2 — LLM API prices, markup, credit unit, Neo Interactions session stats, and TAM fund counts. Saves to this browser; v1-only config keys are preserved automatically.
Edit the variables that drive the Pricing Assumptions document — LLM API prices, markup, token split percentages, workflow counts, and TAM fund estimates. Changes save to this browser and propagate to the Pricing Assumptions document on reload.
Readiness assessment for the 3-year P&L, cash flow forecast, and balance sheet. Documents what's in place (cost model, markup, TAM structure), what's missing (tier prices, churn, headcount plan), the six biggest forecast drivers, and a prioritised action plan. All variable values load live from the config JSON files.
What's genuinely hard to build in a due diligence workflow, and what VCs will actually pay for. Covers trust calibration as the core engineering challenge, the three features with proven willingness-to-pay (speed, conflict detection, consistency), features that sound useful but don't convert, and the build sequence that makes them land correctly.
Cross-document analysis across all nine foundation corpus files. Surfaces contradictions, scope gaps, and structural issues — ranked P1 to P3. Covers the trial credit model conflict, the missing agent name, target user scope creep, onboarding credit burn, and more.
Requirements for the three-task onboarding checklist — from first message with NEO to a running startup screening workflow. Covers all three P0 tasks, completion state, success metrics, and open questions.
Requirements for the free trial experience: 100 credits per user, 14-day window. Covers credit allocation, balance UI, exhaustion states, 6 notification touchpoints, the end-to-end trial journey, and conversion metrics.
Ballpark TAM for multi-stage VC, private equity, family offices, and corporate VC. Uses ARPU multipliers derived from deal volume and team size relative to the average early-stage fund profile.
Analysis of whether growth-stage funds (Series B+) represent a viable near-term TAM expansion. Examines the hypothesis that priced rounds and ARR make evaluation more tractable, where this holds (Series A overlap), and where it breaks down. Includes a stage-by-stage fit assessment and open questions to revisit.
Estimated costs for all 25 data sources listed in §17 — Authorized Data Sources. Covers court and legal record APIs, regulatory databases, licensed commercial data, alternative signal providers, and international sanctions screening. Includes procurement flags: sources with restricted access, acquisition changes, and the biggest budget items to defer until post-launch.