Validating AI Cost Stoppage Products

Pick the pillar that matches your stack and buyer access, then run the protocol:

1. Diagnose the invisible cost — interview 5 target buyers; find where they lose hours or dollars to LLM output review, debugging, retries, or compliance plumbing.
2. Write the one-sentence claim — "We cut/stop/reveal [X] by [Y%] in [timeframe]." If you can't write it in one sentence, the scope is too big.
3. Scope the 4-week shippable version — one integration point, one feature that stops the cost, nothing else.
4. Run the Micro-Validation Protocol (below) before writing more code than necessary.

Progress:
- [ ] Step 1: Select pillar based on stack + buyer access (not just interest)
- [ ] Step 2: Name the specific invisible cost in the buyer's own vocabulary
- [ ] Step 3: Write the one-sentence 4-week claim
- [ ] Step 4: Scope the minimal shippable artifact (one hook, one feature)
- [ ] Step 5: Week 1 — build core connection mechanism (SDK wrapper / API proxy)
- [ ] Step 6: Week 2 — build the single cost-stopping feature only
- [ ] Step 7: Week 3 — add Stripe billing gate + 30-day no-questions refund button
- [ ] Step 8: Week 4 — cold outreach to 50 target buyers, charge a small real fee
- [ ] Step 9: Score result — refunds in week 5 mean the pain isn't sharp enough; pivot pillar

Step-by-step detail

Step 1 — Select the pillar. Score each pillar 1-5 on: (a) do you already have access to this buyer persona, (b) can you build the core integration in <1 week, (c) is the cost you'd eliminate something you've personally felt. Pick the highest total, not the most "interesting" tech.

Step 2 — Name the invisible cost precisely. Vague: "reviewing AI output is slow." Precise: "Our QA team spends 6 hrs/day reading LLM-generated support replies before they go out, and 90% of that time is spent on outputs that were actually fine." Precision here is what makes the claim testable.

Step 3 — Write the claim. Format: "We [stop/cut/catch] [specific cost] by [specific amount or mechanism] without [the buyer having to do something painful]." If the sentence needs "and" to connect two features, cut one.

Step 4 — Scope the artifact. The shipped version must have exactly ONE integration point and ONE differentiating feature. Everything else (auth, nice UI, multi-tenant support, config options) is deferred past week 4.

Step 5–8 — Run the calendar. Do not let any week bleed into the next. If Week 1's integration isn't done by day 7, cut scope further rather than extending the timeline — a narrower wrapper beats a broader one that slips.

Step 9 — Judge the result by refunds, not signups. A buyer paying and not asking for a refund by week 5 is the only real signal. Signups, demo requests, and "this is cool" feedback are not validation.

Example 1: Input: "I have a strong infra/observability background and know 3 teams running multi-agent RAG pipelines who complain about debugging." Output: Pillar 2. Invisible cost: "Senior eng spends 1-2 days per incident tracing which step in a 7-step agent chain lost context." Claim: "Pinpoint the exact step your agent lost context without reading raw JSON logs." Week 1: SDK wrapper hooking into their LangChain/LlamaIndex pipeline, logging state snapshots at each step. Week 2: Render snapshots as a visual tree diagram diffing state before/after each step. Week 3: Stripe gate + refund button on the dashboard. Week 4: Outreach to 50 eng managers running agentic workflows in their network/Twitter/LinkedIn.

Example 2: Input: "I'm a solo developer, no enterprise sales experience, want fastest path to first paying customer." Output: Pillar 4. Invisible cost: "Fintech/healthtech teams spend 2-3 weeks building PII masking + audit logging before they're allowed to use Gemini in prod." Claim: "A self-hostable Gemini proxy gate that passes your security audit on day one." Week 1: Docker container proxying Gemini API calls. Week 2: PII regex/NER masking + S3 audit log drop. Week 3: License-key billing via Stripe, embedded refund button. Week 4: Cold email 50 eng managers at Series A-C fintech/healthtech companies found via job postings mentioning "Gemini" + "compliance."

Example 3: Input: "Our team is burning $40k/mo on OpenAI retries that fail repeatedly, CTO wants it fixed." Output: Pillar 3. Invisible cost: "Automated agents retry on the same malformed prompt/context up to 10x before giving up, each retry billed at full token cost." Claim: "We kill broken retry loops on the second failure instead of the tenth." Week 1: Lightweight API proxy sitting between app and LLM provider, logging call sequences per request ID. Week 2: Semantic similarity check between consecutive failed outputs; kill + alert on 2nd match. Week 3: Billing gate priced as % of tokens saved. Week 4: Outreach to CTOs/infra leads with visible high API spend (job posts, funding announcements, public eng blog posts about LLM costs).