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.
Deep-research reference on how venture capital actually works — fund mechanics, LP relationships, "2 and 20" economics, power law portfolio construction, why most VCs fail to beat the S&P 500, and the full psychology layer. Plus the founder's guide: deal leads vs. co-investors, pro-rata rights, why VCs ghost, how to get funded without a network, term sheet red flags, and where VC-founder interests structurally diverge.
What makes a startup un-investable — and what separates fundable first-time founders from the rest. Covers the nuanced red flags VCs notice that founders miss, the most common rejection reasons, how to make the most of a brief VC meeting, and how to show determination without desperation. 7 claims survived adversarial 3-vote verification from 21 sources.
Every company in the competitive landscape organized by tier and category — from AI-native deal evaluation (Category A) through VC CRM, institutional data, enrichment tools, and general AI. 17 companies across 6 categories, each with a confirmed link, one-line description, and the specific gap One Agentic fills versus that tool.
Story-driven rebuild for the MVP Lab pitch — follows Faisal, a Riyadh VC analyst drowning in inbound deal flow, from ghosted founders to NEO. Story mode keeps each slide to eight words; More Details expands the full narrative. Pricing, TAM, and tiers derive live from the v2 credit model.
The full pitch narrative as a print-optimized document — every section of the v2 deck with all detail text preserved, formatted for A4 PDF export. Keeps the complete story on record as the slides get trimmed for presentation.
Prepared answers to common investor questions — differentiation, competitive defensibility, market timing, and positioning. Honest, specific, no clichés.
A 5-minute read for potential users. Covers the problem, the solution (NEO), how it works, key differentiators, and the business model — enough for a prospect to understand the product value and take the next step.
A 10–15 minute read for prospects who want the full picture. Covers the structural problem in depth, both failure modes, the full product walkthrough, who it's for, the competitive landscape, differentiators and moat, business model, market opportunity, and current stage.
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.
Requirements for NEO's deep research mode: parallel multi-agent pipeline that streams a structured investment scorecard — signals, red flags, financial health, deal intelligence, and sourcing quality — within a configurable time budget.
Requirements for fund-level thesis configuration — the criteria against which every deal is evaluated. Covers the pre-defined primitive library, GP vs. analyst scope isolation, thesis versioning, and the output schema consumed by the Research Agent.
Side-by-side comparison of the two architectural options for NEO's deep research pipeline. Covers trade-offs in parallelism, state management, and LangGraph topology.
Dynamic map-reduce approach using LangGraph's Send API to spawn research sub-agents at runtime. Maximises parallelism and scales with the number of research dimensions without pre-defining graph branches.
Fixed-topology approach with pre-defined parallel branches in LangGraph. Simpler to reason about and debug; each research dimension is a named node with explicit edges and a deterministic execution graph.
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.