Platform
AI features for serious software delivery
Ship chat over your private data, summarization, extraction and assistants inside the apps Ciao builds — through the same QA, security testing and governance as every other change.
Ciao AI features let teams add AI capabilities to the applications they build: chat over private data, document summarization, structured extraction and task-specific assistants. Unlike bolting a chatbot onto a finished product, AI features on Ciao ship through the full delivery loop — Guardrails review, automated QA, security testing and one-click deployment — as real React, TypeScript and Supabase code you own.
Published 2026-07-03 · Last updated 2026-07-03
AI features usually arrive last and ship worst
Every team wants the same four things from AI right now: chat that actually knows their private data, summaries of documents nobody has time to read, structured fields pulled out of messy inputs, and assistants that carry a task from start to finish. Getting a demo of any of these takes an afternoon. Getting them into a production application — connected to real data, behaving predictably, and cleared by whoever owns security — is where most projects stall.
The gap is rarely the model. It is everything around the model: where the data lives, how retrieval is wired, what happens when an answer is wrong, who reviewed the change, and whether the feature was tested like software or pasted in like a widget. Ciao treats AI features as part of the application, not an attachment to it.
How AI features ship on Ciao
1. Describe the capability
Ask in plain language: "let managers chat with our policy documents" or "summarize each support ticket and extract the product, sentiment and requested action."
2. Ciao wires the backend
The Builder generates real React, TypeScript and Supabase code — storage, retrieval over your private data, and the calls behind the feature — as inspectable code you own, not a black box.
3. Shape behavior in the live preview
The app runs beside the chat while it is built. Try real questions, correct tone and structure, and use inspect-to-prompt to point at exactly the element you want changed.
4. QA and Security test it
QA runs deterministic browser replays and smoke gates before publish. Security runs static scanning, dependency checks and access-control probes, and confirms findings against the live app.
5. Guardrails reviews the change
Guardrails maps the code into business areas, detects risky changes, applies your plain-English policies and records human review before the merge.
6. Deploy and monitor
Publish to Ciao cloud or your own infrastructure. Doctor, a read-only AI SRE, probes the live app, diagnoses root cause and drafts the fix when something drifts.
Why it matters
AI features are the most scrutinized changes in any application, because they touch the data your organization actually cares about. A chat feature over HR policies or customer records will be questioned by security, legal and leadership before anyone praises it. Features that ship outside your delivery discipline become the reason the whole AI program gets paused.
Because AI features on Ciao pass through the same loop as every other change, the hard questions have concrete answers: inference runs under zero-retention model contracts, customer code is not used to train models, every merge carries recorded human review, and the append-only audit trail shows exactly what changed and when.
Who builds AI features on Ciao
- Operations teams — Knowledge-base apps that answer from internal policies, SOPs and runbooks instead of the open internet.
- Agencies — Assistants and document summarization added to client portals as a premium, recurring service.
- SaaS product teams — Extraction and summarization added to an existing product without standing up a separate AI stack.
- Enterprise IT — Support and triage tools that classify tickets, extract fields and route work to the right queue.
- Founders — Assistant-first products where the AI feature is the product, built on infrastructure designed to scale.
Security and governance notes
- ✓ Inference runs under zero-retention model contracts; customer code is not used to train models.
- ✓ Access to private data in generated apps follows role-based access control.
- ✓ Security probes access controls and confirms vulnerabilities against the live app before flagging them.
- ✓ Every AI-feature change passes Guardrails policy review and lands in the append-only audit trail.
- ✓ Enterprise plans can route AI features through your own provider keys or private models.
- ✓ SOC 2 Type II reports are available under NDA for procurement review.
Common AI features and how they ship
| AI feature | What it does | How it ships on Ciao |
|---|---|---|
| Chat over private data | Answers questions from your documents, tickets or records | Retrieval wired into Supabase; access rules enforced; QA replays run before publish |
| Summarization | Turns long documents, threads or transcripts into short, structured briefs | Generated as real backend code you own; behavior tuned in the live preview |
| Extraction | Pulls structured fields out of messy inputs — emails, PDFs, forms | Output schemas defined in code; results land in your database, not a vendor silo |
| Assistants | Carries a multi-step task: triage, drafting, routing, follow-up | Scoped by RBAC; every change reviewed by Guardrails with a recorded audit trail |
Frequently asked questions
What AI features can I add to an app built on Ciao?
Chat over private data, document summarization, structured extraction and task-specific assistants are the most common. Because Ciao generates real React, TypeScript and Supabase code, the feature is ordinary application code you can inspect, extend and export — not a locked widget.
Is our data used to train models?
No. Customer code is not used to train models, and inference runs under zero-retention model contracts. Enterprise plans can also route inference through your own provider keys or private models.
Which models power AI features?
Ciao runs a multi-provider model ladder with fallback, which reduces dependency on any single model vendor and routes each task to a suitable model. On enterprise plans you can bring your own LLM via provider keys or OpenAI-compatible endpoints.
Do AI features go through the same QA and governance as other changes?
Yes. QA runs deterministic browser replays and smoke gates before publish, Security confirms findings against the live app, and Guardrails applies plain-English policies and records human review. AI features get no exemption from the delivery loop.
What does it cost to add AI features?
Individual builders start self-serve with credits, and AI features are part of the platform rather than a separate add-on. Serious production programs start at USD 10,000 per year — talk to sales if you are planning AI features across multiple applications.