The problem we're solving
Every life event in India — changing jobs, buying a vehicle or home, having a child, getting married — triggers a spread of government obligations across EPFO, Income-Tax, RTOs, UIDAI and state departments. Today citizens must know the service names themselves, find portals, order steps on their own, and re-enter the same documents everywhere.
This prototype asks “What happened in your life?” and turns the answer into one ordered, dependency-aware journey of tasks — with documents autofilled from consented data, deadlines on a calendar, and an escalation path when a department goes silent.
The complete citizen journey
- 1You describe a life event in plain words (“I bought a second-hand car yesterday”). Multiple events live side-by-side as tabs — a job change and a car purchase can both be in flight.
- 2The assistant detects the event and builds a task graph — in dependency order, with parallel lanes, and only tasks relevant to YOU (vehicle work hidden if you don’t own a vehicle).
- 3The chat is not a FAQ bot. Ask “what should I do next?” and you get actionable buttons under the reply — tap one to open that exact form without hunting for it.
- 4Every task carries an official application form, prefilled instantly from your authorized documents (name, DOB, PAN, bank, RC…). If a field isn’t on file — like an insurance policy number — tap “Ask AI where to find it”, hop into the chat, and the answer comes back grounded; your draft is kept while you’re away.
- 5You verify, consent, and submit — receiving a reference number. The filed slip is saved back to your DigiLocker, and the SLA deadline lands on your Government Calendar alongside every other pending application.
- 6If an application drifts past its expected decision date, one click drafts a fully-contextual grievance (references, department, dates) — ready to file, nothing re-typed. Benefit matches and a full calendar review are one header-click away.
What is real vs. what is mocked
You should never be able to guess which is which from the interface — hence this page.
✓ Real — the product logic
- Life-event detection & entity extraction
- Dependency-aware task graphs (DAG), validated against real process rules
- Deterministic eligibility engine with explainable “why”
- Urgency scoring, SLA calendar and overdue detection
- The citizen consent + verify flow
🟡 Mocked — the outside systems
- DigiLocker document store (synthetic citizen docs)
- EPFO / RTO / Income-Tax / state portals & statuses
- OTP, payments and e-sign steps
- CPGRAMS grievance filing (returns a mock reference)
No real Aadhaar/PAN/OTP/payment/health data is used anywhere. Offerings are built with mock data and are clearly not an official government product.
Built on consent, ready to scale
- 🔐 Documents are pulled only per-purpose, after explicit consent, and only for the fields that form needs — mirroring the existing DigiLocker consent model.
- 🔁 Exchanging DigiLocker/EPFO/CP Data for their real, documented APIs is a drop-in: the engines only speak to lightweight interfaces, so a production adapter swaps in without rewriting the journey logic.
- 🧩 No LLM decides what you're owed. Every journey, dependency, eligibility and grievance is generated from curated, versioned, human-verified rules — the LLM only understands language and routes to those rules.
How it was built
The task-graph engine, journey templates, eligibility matcher, calendar and all mock integrations were built with Codex (an OpenAI agent). The two conversational moments — detecting a life event from free text and generating grounded grievance text — are driven by a generative model (Gemini) through its OpenAI-compatible API. Department data and systems (DigiLocker, EPFO, CPGRAMS) are simulated; the pattern they follow mirrors how the real integrations dock in.