Build an AI App People Buy (and keep): A Practical Playbook

Summary

• Pick jobs where money already flows to humans. Aim to assist, replace, or make previously unthinkable work possible.
• Build like a pro does the job. Map the exact steps, then turn them into code and prompts.
• Win on reliability. Create objective evals and grind until you’re ≥95–99% accurate on real tasks.
• Price outcomes, not features. Charge like a service when you deliver a service.
• De-risk adoption. Head-to-head pilots, human-in-the-loop where needed, and hands-on onboarding.

AI makes it possible for small teams to ship products that used to take years and huge budgets. This post is a straight-shot playbook: how to pick a high-value idea, build a reliable product (not just a flashy demo), and take it to market so customers actually pay and stay.

1) How to Choose the Right AI Idea

Stop guessing what people want. Follow the money already spent on human labor.

Three proven angles

1. Assist a professional
   Make them 10–100× faster at research, drafting, review, categorization, triage, support replies, claims prep, etc.
2. Replace a service
   Deliver the outcome end-to-end (e.g., “AI contract review,” “AI bookkeeping,” “AI tax prep”) with optional human QA.
3. Do the unthinkable
   Work no human team would attempt (read every doc in an archive, re-index a decade of tickets, translate a whole catalog with glossary rules).

Why this is big

Seat-based SaaS priced per user. Service replacement and “unthinkable” work are priced by value delivered. That’s 10–1000× the revenue ceiling of seat software.

2) Build So It Works Outside a Dem

Most AI products fail because they’re cool once, not reliable always. Reliability wins sales, renewals, and expansions.

Step A: Learn the job for real

• Shadow experts doing the task end-to-end.
• Hire or partner with domain pros.
• Write the exact steps, questions they ask, checks they run, how they judge “done.”

Example (legal research): clarify scope → design a research plan → run targeted searches → read sources → discard noise → take cited notes → draft → fact-check every claim.

Step B: Turn steps into software

• Use code wherever deterministic (parsing, filtering, math, schema validation).
• Use prompts only for reasoning (relevance scoring, synthesis, plan selection, verification). Keep prompts scoped and machine-gradable.
• If the path is fixed, build a workflow (A → B → C).
• If the path depends on context, add a simple controller/agent that chooses the next step. Start minimal.

Step C: Add guardrails and observability

• Source-bounded answers, citation checks, schema validators.
• Log prompts, outputs, models, latency, and cost per task.
• Fallbacks: a second model or a human review route for edge cases.

3) Evaluations: Your Moat Against “Cool Demo” Syndrom

You won’t get reliability by luck. You get it by tests.

What good looks like

• Convert tasks into objective outputs: True/False, integers (0–7), exact spans, JSON with strict keys.
• Create evals for each micro-step and for the end-to-end flow.
• Start with 20–50 tests → grow to 100+ → maintain a blind holdout set.

The grind (this is where you win)

• Iterate prompts until step-level accuracy is ≥95–99% on real data.
• Harvest every production miss as a new test. Your live test suite should dwarf your lab set.
• Re-run on new models; switch when evals and cost say so.
• Track diffs per prompt change. Roll back if a change hurts.

Bar to ship a beta: ≥99/100 on core end-to-end tests with explainable misses. Add human-in-the-loop for sensitive domains.

4) Pricing and Packaging

You’re not selling a feature; you’re selling an outcome.

Models that work

• Per service unit (contract reviewed, claim processed, ticket resolved).
• Per seat (when buyers want predictable budgets; price high if each seat controls high-value work).
• Hybrid (platform fee + usage or success fee).

Where to anchor

• Value share: If you save $5M or unlock $100M, 10–20% is reasonable.
• Human benchmark: If a human service costs $1,000, landing at $300–700 looks like a win.

Ask buyers how they prefer to pay. Many will choose a higher flat price over cheaper usage if it simplifies budgeting.

5) Go-to-Market That Builds Trust

Enterprises want AI wins but won’t jump without proof.

De-risk adoption

• Head-to-head pilots against the current process. Measure accuracy, speed, cost.
• Human-in-the-loop for early reliability; remove it as evals improve.
• Deployed engineers / success managers to set up data flows, validate outputs, and drive adoption.
• Onboarding that teaches by doing: sample tasks, guided flows, safe sandboxes.

Avoid the pilot trap

• Define exit criteria before the pilot.
• Plan the scale path (team → department → org).
• Track weekly active use, not just licenses.

6) Market Selection (Fast Filters)

• Follow outsourcing. If companies already offshore or contract a job, AI can likely assist or replace it.
• Go where pain is horizontal. Support, finance ops, procurement, compliance ops, document processing.
• Don’t overvalue competitors. Markets are huge; most products are weaker than they look once you build.
• Leverage access. Domain expertise and inside data shorten the road to reliability.

7) Execution Checklist (First 90 Days)

Weeks 1–2: Scope

• Pick a job (assist/replace/unthinkable).
• Talk to 5–10 practitioners. Map the exact workflow.
• Define objective success for tasks and micro-tasks.

Weeks 3–5: Prototype

• Code deterministic parts.
• Add prompts for reasoning.
• Build 20–50 evals per step + end-to-end. Hit ~90%+.

Weeks 6–8: Beta-ready

• Add guardrails, logs, admin.
• Harvest failures from 3–5 design partners; reach 100+ evals.
• Hit ≥95–99% on core flows; add human review if required.

Months 3–4: Pilot

• Set price and clear exit criteria.
• Run head-to-head; publish accuracy/speed/cost.
• Convert to production when criteria are met.

Month 5+: Scale

• Replace manual checks with verified prompts or code.
• Add a cheaper backup model for non-critical steps.
• Keep adding evals from real edge cases.

8) Prompt Patterns That Improve Accuracy

• Machine-gradable outputs: integers, enums, strict JSON schemas.
• Source-bounded generation: must cite only provided docs; fail closed when unsure.
• Few-shot with positives and negatives: show both good and bad examples.
• Hard constraints: “If uncertain, return NEEDS_REVIEW” beats guessing.
• Two styles to test: checklist prompts vs expert role prompts. Keep whatever passes evals.

9) Common Pitfalls (and Fixes)

• Demo works; production fails. You skipped evals.
  • Fix: Build a harness; convert every miss into a test; grind until ≥95–99%.
• Costs balloon. Too many prompts or a single frontier model for everything.
  • Fix: Move logic to code; cache; batch; use smaller models for cheap steps; switch models when evals say it’s safe.
• Pilots don’t convert. No defined success criteria.
  • Fix: Pre-agree on metrics and thresholds; secure an executive sponsor.
• Users don’t adopt. New flow fights muscle memory.
  • Fix: In-product guides, safe sandboxes, and hands-on deployment support.

10) Defensibility Beyond “It Calls a Model”

Real moats come from:

• Deep workflow capture (the real way pros do the job).
• Private data integrations (systems, schemas, permissions).
• A massive eval suite grown from real failures.
• Operational playbooks for onboarding, monitoring, and scaling.
• Trust capital (measured wins, references, audits).

These take time. That’s precisely why they defend you.

Conclusion: Build an AI app

You don’t need a magic trick to build an AI app that’s a valuable product. You need a job with real spend, a faithful map of how experts do it, code plus tight prompts, ruthless evals, outcome-based pricing, and a go-to-market motion that makes buyers feel safe.
Do those things, consistently, and you’ll have more than a neat demo. You’ll have a product customers rely on, renew, and recommend.

Do those things, consistently, and you’ll have more than a neat demo. You’ll have a product customers rely on, renew, and recommend.