Building AI SaaS MVP (Minimum Viable Product)

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VISIT INNOX

Below is a practical, founder‑friendly blueprint to ship an AI SaaS MVP in 4–8 weeks that delivers real outcomes, not just demos—while keeping trust, cost, and reliability under control.

1) Define the wedge and outcome

  • Pick 1 workflow, 1 persona, 1 painful job with clear ROI.
    • Examples: resolve L1 support tickets under caps; reconcile AP exceptions; draft incident mitigations with one‑click actions.
  • Make actions reversible and policy‑bounded so autonomy can grow safely.
  • North‑star metric: cost per successful action (CPSA). Report it weekly alongside reversal rate.

2) Design the MVP as a system of action

  • Retrieval‑grounded reasoning
    • Connect a small tenant knowledge base (docs, FAQs, policies, a few records). Answer with citations and timestamps; refuse when evidence is thin.
  • Typed tool‑calls (never free‑text to production)
    • Define 2–3 JSON‑schema actions that map to the workflow (e.g., refund_within_caps, update_address, create_ticket_note). Validate, simulate diffs/costs, require confirmations, support rollback.
  • Policy‑as‑code
    • Encode eligibility, limits, approvals (maker‑checker), change windows, and egress/residency rules. Block out‑of‑policy at execution.
  • Progressive autonomy
    • Start suggest → one‑click with preview/undo. Consider unattended only after reversal rates are stably low on real usage.

3) Lean reference architecture (MVP scale)

  • Frontend: React/Next.js with inline assistant, “explain‑why” panel (citations, uncertainty, policy checks), and a preview/undo drawer.
  • Backend: Python FastAPI or Node/TypeScript.
  • Data: Postgres for metadata + an object store for blobs.
  • Retrieval: Hybrid search (BM25 + vectors) with tenant ACL filters; small vector index (FAISS or light managed).
  • Model access: A thin gateway enforcing timeouts, quotas, variant caps, budgets; small‑first routing.
  • Orchestration: Simple planner that sequences retrieve → reason → simulate → apply; tool registry with JSON Schemas; idempotency keys and rollback tokens.
  • Observability: OpenTelemetry traces; decision logs linking input → evidence → policy → action → outcome; minimal dashboards for groundedness, JSON/action validity, p95/p99 latency, reversals, and CPSA.

4) Trust, privacy, and safety essentials (day one)

  • Privacy‑by‑default
    • Minimize and redact inputs; tenant‑scoped encrypted storage; “no training on customer data”; short retention; region pinning if needed.
  • Safety gates
    • Refuse on low/conflicting evidence; run simulation with read‑back and rollback before apply; approvals for sensitive actions.
  • Security basics
    • SSO/OIDC if selling to businesses; least‑privilege tool creds; egress allowlists; idempotency and replay protection.

5) Golden evals and SLOs (lean but effective)

  • 30–50 golden cases covering:
    • Grounding/citation coverage, JSON/action validity, refusal correctness, domain accuracy. Include a few fairness/locale cases if relevant.
  • SLOs to publish internally:
    • p95 latency for interactive steps, JSON/action validity ≥ target (e.g., 98%), reversal rate ≤ threshold (e.g., 3–5%), refusal correctness ≥ target.

6) Build sequence (4–8 weeks)

  • Week 1: Scope and scaffolding
    • Pick the workflow and schemas. Set budgets and SLOs. Stand up Postgres + object store, auth, and a minimal KB. Implement decision logs.
  • Week 2: Retrieval and cited drafts
    • Hybrid search with ACLs; cite sources with timestamps; refusal behavior. Ship the UI “explain‑why” panel.
  • Week 3: Actions with simulation/undo
    • Implement 2–3 JSON‑schema tools. Add simulation (diffs, cost, blast radius), read‑backs, idempotency, and rollback tokens. Maker‑checker for sensitive steps.
  • Week 4: Evaluations and dashboards
    • Wire golden evals to CI; add grounding/JSON validity/refusal dashboards; basic cost panel (tokens, minutes, CPSA).
  • Weeks 5–6: Pilot hardening
    • Small‑first routing and caches; variant caps; degrade modes; budget alerts; connector contract tests. Tighten UX (clarifications, confirmations).
  • Weeks 7–8: Enterprise posture (optional)
    • SSO/RBAC/ABAC, audit exports, residency/private inference toggle, DSR automation. Add autonomy sliders if quality is stable.

7) Minimal data model and schemas (templates)

  • Tool schema example (refund within caps)
    • Fields: order_id, amount, currency, reason_code, customer_id
    • Constraints: amount ≤ policy.cap(order_id), currency in {USD, EUR, …}, approver required if amount > threshold
    • Side‑effects: write ledger entry, email customer template
  • Decision log fields
    • correlation_id, actor, input_hash, evidence_citations[], policy_checks_passed[], action_schema, simulation_diff, approver_id, apply_timestamp, rollback_token, outcome, reversal_flag

8) UX patterns that reduce errors

  • Mixed‑initiative prompts
    • Ask for missing slots, confirm normalized values (“Refund 25 USD for order O‑88, correct?”), surface uncertainty.
  • Read‑back + one‑click undo
    • Present a concise confirmation and allow undo for N minutes or until state diverges.
  • Side‑by‑side evidence
    • Show citations with timestamps and jurisdictions; highlight stale sources; allow “view data used.”

9) Cost control (so margins survive pilots)

  • Router mix targets
    • ≥70% small/tiny usage for classify/extract/rank; escalate to larger models sparingly.
  • Context hygiene
    • Trim to anchored snippets; dedupe by content hash; cache snippets/results.
  • Budgets and alerts
    • Per‑tenant/workflow caps at 60/80/100%; degrade to suggest‑only when caps hit; track CPSA weekly.

10) Pilot GTM and proof of value

  • Select 3–5 design partners in one ICP; instrument from day one.
  • Weekly value recap
    • Actions completed, reversals avoided, minutes saved, SLO adherence, spend vs budget, CPSA trend. Include decision‑log snippets and a short demo clip.
  • Pricing for MVP pilots
    • Seats + pooled action quotas with hard caps; optional outcome‑linked bonus for clear wins; free audit export and SLO dashboard during pilot.

11) Common MVP pitfalls (and fixes)

  • Chat without actions
    • Tie every draft to at least one safe action with simulation/undo.
  • Free‑text writes to production
    • Enforce JSON Schemas, policy checks, simulation, approvals, and rollback.
  • Unpermissioned/stale retrieval
    • Apply ACLs pre‑embedding and at query; show timestamps; refuse when stale.
  • No evals or SLOs
    • Add golden evals and publish internal SLOs by week 4; block releases on regressions.
  • Cost spikes
    • Cap variants, route small‑first, cache, and set budgets with degrade modes.

12) Deliverables for investor or buyer demos

  • Live flow: cited draft → simulation with diffs/cost → one‑click apply → visible rollback token.
  • Trust panel: sources with timestamps, policy checks, approver, and outcome.
  • Ops dashboard: groundedness, JSON/action validity, reversals, p95/p99, CPSA trend.
  • Security/privacy sheet: no‑train defaults, residency option, DSR coverage.

Bottom line: An AI SaaS MVP should prove one thing—governed actions that create measurable outcomes safely and cheaply. Build retrieval with citations, a couple of schema‑validated actions with simulation/undo, basic policy gates, and minimal SLO/cost dashboards. Ship in weeks, measure CPSA and reversals, and expand autonomy only as quality stabilizes.

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