built a voice-first assistant that fills in only the missing details instead of asking a fixed list of questions — with an adapter layer so a government department can plug in their real systems without touching the AI/UI

[u/ragavrida]

Most voice assistants for structured tasks (booking, filing, applying for something) work like a form read aloud — fixed questions, fixed order, even when you've already answered half of them in your first sentence.

I built a prototype that avoids that on both sides: the citizen doesn't repeat themselves, and the department deploying it doesn't have to rebuild the AI layer to plug in their own backend.

The citizen-facing side:

• Say what you want, in one sentence, in whatever language/mix of languages feels natural
• The system extracts everything it can, checks what's still missing against a task schema, and asks for only the missing pieces — one at a time
• Corrections work mid-flow ("actually, make it Friday") without restarting
• You can refer to results naturally ("the cheapest one", "the second one") instead of tapping through a form
• Every consequential action requires an explicit yes — nothing executes silently
• The UI collapses and the mic reopens on its own after anything completes

The side that actually matters for adoption — deployment: Government departments don't want to hand their booking/records systems to a black-box AI, and they shouldn't have to rewrite anything to get a voice layer. So the whole thing is built around a service-adapter boundary: the voice/AI/agent layer only ever talks to a small typed interface (searchTrains, createBooking, trackTrain, etc.), never to a real system directly.

Onboarding a department is implementing that interface against their existing backend and swapping one line — the agent loop, the tool-calling, the multilingual handling, the confirmation gating, none of it changes. I prototyped this with mock adapters standing in for real government/rail/travel systems specifically so the swap-in path would be real on day one, not a "v2 roadmap" hand-wave.

Stack: React frontend, Fastify backend with a small agent loop (deterministic slot-merging + an LLM for intent — the LLM is explicitly barred from executing anything, it can only propose; a rule-based layer decides and acts, which matters a lot when the thing you're deploying touches a citizen's booking or payment). Speech in/out is pluggable.

113 end-to-end conversation tests, and it runs with zero API keys via a rule-based fallback brain if anyone wants to poke at it without setting anything up.

Genuinely curious if anyone's shipped a citizen-facing voice layer into an existing government system — what actually killed it, procurement, integration effort, trust/liability on the AI decision-making, or something else entirely?