docs/ROADMAP.md

ROADMAP — From v1 Vertical Slice to v2 Platform

Field	Value
Project	Insurance Sales Portfolio Expert
v1 status	Shipping in <24h for Sarvam AI assignment
v2 status	This document

0. Purpose

v1 is a vertical slice: 10 insurers × Health × ~80 policies × voice-first advisor. The architecture is built so v2 is a data/config change, not a rebuild. This document maps the path.

1. What v1 ships

Working product:

• Voice-first chat advisor over a curated corpus of Indian health insurance policies (~76 PDFs from 10 insurers, ingested into Chroma + DuckDB)
• Multi-language: English + Hindi/Hinglish via Sarvam Saarika STT + Sarvam Bulbul TTS
• Brain router: Sarvam-M primary, Groq Llama-3.3-70B / OpenRouter DeepSeek-V3 fallback
• 4-gate hallucination defense + auditable refusal log
• 48-field structured extraction per policy
• Clean Next.js + Tailwind frontend
• FastAPI backend deployed on Render; frontend on Vercel
• 8 design / decision documents totaling ~30 pages

Eval signal:

• Gold Q&A harness (~300 pairs targeted) + automated grader (Groq Llama)
• eval/results.md versioned table per run
• Live audit log logs/hallucinations.jsonl for every blocked claim

Documented limits:

• Star Health corpus blocked by CDN — 0/11 policies (workaround in v2 with Playwright)
• IRDAI regulatory corpus blocked by Akamai — deferred to v2 (D-017)
• Pricing is illustrative only (D-007)
• Single-user demo (no auth, no multi-tenant)

2. v2 — the path to "platform"

v2.1 — Corpus expansion (target Q1 2027)

Goal: Move from 10 insurers Health → all major Indian insurers × all categories.

Component	v1 → v2 change
Insurer adapters	10 hand-curated adapter files → automated rag/adapters/<slug>.py per insurer (template + override)
Categories	Health only → Health + Life + Motor + Travel + Critical-illness specific (schema already supports it; data-only change per Doc 02 §7 commitment #2)
Policy count	76 PDFs → ~500 PDFs
Refresh cadence	One-time → cron-pulled weekly with diff detection (F-11)
Star / Akamai workaround	Manual / blocked → Playwright-driven download per insurer (already MCP-installed)
IRDAI corpus	Deferred

Engineering effort: ~2 weeks. Schema/code already supports it. The work is per-insurer adapter + scheduling.

v2.2 — Pricing realism (target Q2 2027)

Current state (v1): Illustrative bands only (D-007) — buyer-facing disclaimer. v2 path:

Step	What	Why
1	Partnership with one or two insurers for real-quote API	Authoritative pricing, B2B integration
2	Until then: scheduled scrape of comparison portals (PolicyBazaar / InsuranceDekho) at session start	Real bands, refreshed daily
3	Quote disclaimer: "actual quote varies; final by underwriting"	Compliance, sets expectations

v2.3 — Production deployment (target Q1 2027)

Layer	v1	v2
Compute	Render free tier (cold-start spinup)	Render Standard + keep-warm OR migrate to AWS Fargate for B2B SLA
State	Single-tenant DuckDB + Chroma local	Postgres + Pinecone OR managed Chroma for multi-tenant + auth-scoped data
Auth	None (single-user demo)	OAuth + per-insurer-tenant isolation
Observability	JSONL turn log	OpenTelemetry → Grafana/Datadog dashboards
Eval cron	None	Nightly synthetic + 1-5% live-traffic spot grading via Playwright
Rate limiting	None	Per-tenant + per-user quotas

v2.4 — Voice interface upgrade (target Q3 2027)

Current state (v1): Push-to-talk via MediaRecorder API (record-then-send). v2 path:

Stage	Approach	Latency target
1	VAD auto-cutoff via AudioWorklet	2-3s perceived latency
2	Streaming STT via Sarvam Saarika WebSocket	<1.5s perceived latency
3	Full-duplex realtime (user interruptable)	<500ms TTFB

v2.5 — Recommendation engine (target Q2 2027)

Current state (v1): Rule-based pre-filter + LLM-reasoned justification with citations. v2 path:

Step	What
1	Add a learned ranker trained on (profile, policy, conversion) data once we have telemetry
2	Multi-turn refinement: bot proposes 3, user reacts, bot re-proposes — Bayesian update on profile
3	Premium-sensitive routing: if buyer is price-anchored, route to lower-premium-band recommendations even if features are weaker

v2.6 — Compliance posture (target H1 2027)

Need	v2 work
Audit log retention	7 years per IRDAI policyholder-records retention rules (D-017 reading)
PII handling	All buyer profile data encrypted at rest + per-tenant key
Mis-selling flags	Flag any session where the LLM-judge flags an unsupported claim
Grievance redressal	Built-in escalation path: chat → human → ombudsman; persisted handoff context
Regulatory updates	Cron-pulled IRDAI circulars → re-ingest → re-run eval; alert if regulation conflicts with corpus

3. Cost projection v1 → v2

Phase	Cost	Why
v1 (this build)	< $1	Free tiers across the stack
v2.1 corpus expansion (one-time)	~$50	Voyage embeddings for ~500 PDFs + LLM extraction
v2 monthly run-rate, 1k DAU	~$300-500	Sarvam STT/TTS/LLM volume + Render Standard + Postgres
v2 enterprise (5 insurers × 100k users)	TBD	Pricing depends on Sarvam volume contract

4. What does NOT change between v1 and v2

The point of disciplined v1 architecture is that these things are stable across the transition:

1. 48-field structured schema (rag/schema.py) — data-only change to add v2 categories
2. Provider abstraction (backend/providers/base.py) — swap STT/TTS/LLM via config
3. Faithfulness verifier (backend/faithfulness.py) — same 4 gates, possibly more
4. Persona prompt + citation grammar — same, refined
5. Eval methodology (docs/03-eval-plan.md) — same harness, more gold data

The "c-readiness commitments" in Doc 02 §7 are the contract. Every v2 feature is a commitment honored.

5. The honest tradeoffs in v1

Choice	Why we made it	What we sacrificed
Streamlit → Next.js mid-build	Production polish for a BFSI reviewer	2 extra hours of scaffolding
Voyage embeddings → BGE local	Voyage 3 RPM rate limit blocked ingestion	Slightly lower retrieval quality (~3pp) for full corpus access
IRDAI corpus deferred	Akamai bot protection; 4-gate faithfulness already refuses regulatory questions cleanly	Bot can't ground answers in IRDAI text — refuses instead of citing
Push-to-talk over streaming	Risk of broken realtime > demo latency	2-3s perceived latency vs <1s
No auth	Out of scope per Doc 01	Single-user demo only
Hand-curated 5-node fact-find	Auditable + testable	Less natural than LLM-driven
Pipeline A templated gold Q&A	Scales for free; covers single-field lookups	Doesn't test multi-clause reasoning — Pipeline B + C handle that

Every tradeoff is in decisions.md with a "revisit at scale" note.