Capstone — Investor / Demo Technical Narrative (Component 18)
The story that frames the platform as a product and business — for investors, hiring managers, and demos. Based on Phase 15.09. Fill
<...>.
The 3-minute demo script (before → after → trust → outcome)
- Before (the pain):
<the expensive manual workflow your product replaces — in the customer's words>. - After (on real data): show the product doing the job in seconds, on a realistic example.
- Trust (not magic): point at citations / the human-approval step / the eval score — "it's right because…".
- Outcome + ask:
<time/$ saved>→<pilot / next step>.
Rehearse until flawless; have a recorded fallback. The demo carries the story.
The narrative arc (the pitch)
- Problem:
<acute, expensive pain>(15.01). - Why now:
<the LLM capability/cost shift that makes this newly possible>(15.02). - Solution: the product (shown via the demo) — owns the workflow, not a wrapper (15.03).
- Market:
<beachhead → bottom-up TAM>. - Moat:
<data flywheel / system-of-record / domain / distribution>(15.06). - Unit economics: cost per resolved task →
<gross margin %>; routing/caching levers (15.05). - Traction:
<pilots / usage / retention>. - Team:
<why you win>. - Ask:
<amount → next milestone>.
The two AI killer questions (answer crisply)
- "Can OpenAI build this?" →
<structural moat answer: our data/feedback loop + workflow/system-of-record + domain/trust + distribution; the model is an input we ride for free>. Not "we're better." - "Do the unit economics work?" →
<cost per resolved task $X, price $Y, gross margin Z%; routing/caching protect margin>.
The technical story (why this platform is credible)
"We're not a thin wrapper. We built an OpenAI-compatible gateway (multi-provider routing + fallback + metering + key custody), a RAG layer (hybrid retrieval, reranking, citations, tenant isolation), a safe agent layer (model proposes / app executes, approval gates), an eval harness that gates every change, and cost/observability that lets us route the cheap bulk to small/self-hosted models and the hard tail to frontier APIs. The model is interchangeable; our workflow, data, and platform are the product."
Technical diligence (be ready)
| Question | Your answer |
|---|---|
| Real product or demo? | <live, here's the repo> |
| Architecture & cost? | <gateway + RAG + agents; cost per resolved task $X, margin Z%> |
| Model dependence? | <routing/portability; OpenAI-compatible; can swap/self-host> |
| How do you measure quality? | <golden set + CI regression gate; recall@k, faithfulness, apply-rate> |
| Security/compliance? | <trust boundary, isolation, guardrails, audit; SOC2 status> |
The numbers (your credibility)
p95 latency <ms> · cost per resolved task <$> · gross margin <%> · RAG recall@k <> + faithfulness <> · agent per-step reliability <> · apply-rate <> · a caught eval regression · routing savings <%> · injection red-team <pass>.
Why this wins: "Here's a working startup-grade LLM platform, framed as a product, with the numbers and the moat" — that single artifact proves the entire curriculum and is the most impressive thing you can put in front of an interviewer or investor.