Use cases
Build AI assistants with AI-assisted engineering
Ship an assistant that answers from your data, respects your permissions and escalates to a human — built as a real application, not a demo chatbot.
Ciao is an AI-assisted engineering platform for building AI assistants — chat and task interfaces grounded in your own content — as real React, TypeScript and Supabase applications. Unlike standalone chatbot builders, Ciao assistants run inside governed apps: permission-aware retrieval, conversation history in your database, human escalation, a multi-provider model ladder with fallback, zero-retention inference, and own-LLM options for regulated environments.
Published 2026-07-03 · Last updated 2026-07-03
From demo chatbot to production assistant
An AI assistant is a chat or task interface that answers questions and drafts work from your organization's data: a support assistant that resolves the easy half of the queue, a policy assistant that knows the travel rules, a sales assistant that answers security questionnaires from your own documentation. Getting a demo working takes an afternoon. The distance between that demo and something you would put in front of customers is the actual project.
That distance is made of unglamorous requirements: the assistant must only see what the signed-in user is allowed to see; answers must be grounded in current, approved content and show their sources; conversations must be stored in your systems, not a vendor's; there must be a clean handoff to a human; and someone has to watch usage, cost and answer quality after launch.
Ciao builds the assistant as part of a real application, so those requirements are application features — built, tested and governed like everything else — rather than afterthoughts bolted onto a chat widget.
What a production assistant actually requires
- Model access with fallback — A multi-provider model ladder so one vendor's outage or price change is a configuration detail, not an incident.
- Grounded retrieval with citations — Answers drawn from your approved content — help docs, policies, tickets — with sources shown, so users can verify.
- Permission-aware retrieval — The assistant retrieves only what the signed-in user may see. An employee asking about salaries gets the policy, not the payroll table.
- Conversation history in your database — Transcripts stored in your Supabase backend for quality review, escalation context and retention control.
- Human escalation — A defined handoff — to a ticket, a queue or a live person — with the conversation attached, for the questions it cannot or should not answer.
- Response boundaries — Scoped topics, refusal behavior and formatting rules, versioned in code where they can be reviewed and tested.
- Feedback capture — Ratings and corrections on answers, feeding a review loop that improves content and prompts.
- Usage and cost analytics — Who asks what, what it costs, where the assistant fails — visible from launch, not reconstructed later.
How an assistant build runs on Ciao
1. Define the job and its limits
What the assistant answers, what it refuses, and where it hands off. Narrow assistants that do one job well beat broad ones that guess.
2. Connect the content
Help docs, policies, product data and ticket history wired in as retrieval sources, with permissions mapped to your roles.
3. Build the interface
A chat surface inside your portal, product or internal tool — real React components, styled to your brand.
4. Wire escalation and history
Handoffs create tickets with transcript context; conversations persist in your database under your retention rules.
5. Test beyond the happy path
QA replays scripted conversations — including permission boundaries and refusal cases — before every publish.
6. Govern the AI surface
Prompt templates, retrieval scope and model configuration are code; Guardrails records review when they change.
7. Launch, watch, tighten
Feedback and usage analytics show where answers fail; fixes ship through the same governed loop.
Security and governance checklist
- ✓ Inference under zero-retention model contracts
- ✓ Customer code and content never used to train models
- ✓ Permission-aware retrieval scoped to the signed-in user
- ✓ Own-LLM options for regulated environments
- ✓ Conversation data stored in your own backend, under your retention rules
- ✓ Prompt and retrieval changes reviewed through Guardrails before merge
- ✓ QA replays covering refusal and permission-boundary cases
- ✓ Append-only audit trail across prompts, merges, deploys and admin actions
Assistant variations teams build
Customer support assistant
Answers from help docs and account context, deflects the routine half of the queue, escalates the rest with transcripts.
Internal policy assistant
Travel, expenses, IT and HR policy answers with citations to the source document — fewer pings to HR.
Sales enablement assistant
Answers security questionnaires and product questions from approved documentation, drafting responses reps verify.
Document intake assistant
Reads submitted documents, extracts structured fields and flags gaps for human review.
Onboarding assistant
Guides new hires or new customers through setup, answering from your own guides and checking progress.
Data Q&A assistant
Plain-language questions over defined datasets, answering with the query it ran shown alongside the result.
Assistant requirements, covered
| Requirement | How Ciao covers it |
|---|---|
| Model dependency risk | Multi-provider model ladder with fallback |
| Data protection | Zero-retention inference; content never used for training |
| Right answers for the right user | Permission-aware retrieval tied to your roles |
| Regulated environments | Own-LLM options; private VPC and on-prem deployment |
| Wrong-answer handling | Grounded citations, scoped topics, human escalation paths |
| Change control on prompts | Prompt and retrieval config versioned and reviewed via Guardrails |
| Quality after launch | Feedback capture and usage analytics built into the app |
Frequently asked questions
Which models do Ciao assistants use?
Ciao runs a multi-provider model ladder with fallback, which reduces dependency on any single model vendor. Enterprises can also bring their own models — see the own-LLM options for regulated setups.
Will our data be used to train models?
No. Customer code is not used to train models, and inference runs under zero-retention model contracts. Conversation history lives in your own backend under your retention rules.
How do you deal with wrong answers?
By design, not promises: answers are grounded in your approved content with citations, the assistant's scope is limited to defined topics, refusal cases are tested in QA replays, and anything uncertain escalates to a human with the transcript attached. Feedback capture then shows you where answers fail so content and prompts improve.
Can the assistant respect our existing permissions?
Yes. Retrieval is scoped to what the signed-in user may see, using the same role model as the rest of the application, and access-control probes test those boundaries against the live app.
Can we embed the assistant in our existing product?
Yes. The assistant is standard React and TypeScript, embeddable in your product or portal, and custom sandbox images let the surrounding integration work happen against Rails, Java, Go, Python and Node backends.
What does a production assistant cost?
Prototype self-serve with credits. Customer-facing assistants with governance, escalation and analytics are production programs — those start at USD 10,000 per year, and sales can scope model usage costs with you.