Retrieval-augmented search (RAG)
Natural-language answers over your docs, help center, or product data, with links back to the source.
AI integration for the product you already have.
AI integration means adding language-model features to software that already has users, data, and a codebase: search that answers questions, an assistant inside the app, extraction from documents, or a voice interface. Velvy works inside your existing stack, ships behind a gradual rollout, and measures quality with evals before a feature reaches every user.
Spec/services/ai-integration
Best-fit engagement
AI Feature Sprint
4–6 weeksBuilt with
- OpenAI API
- Anthropic API
- TypeScript
- React
- Next.js
- Node.js
- Python
- PostgreSQL
- Supabase
- Firebase
Working
Deployed to production
Tested
Automated tests and evals
Observable
Logs, traces, and alerts
What we build
LLM features your users notice on day one.
In-app assistants
An assistant that understands the user’s account and context, and takes actions through your existing API.
Document and data extraction
PDFs, emails, forms, and transcripts turned into structured records, validated against a schema before they touch your database.
Classification and routing
Tickets, leads, and submissions tagged, prioritized, and routed so the right person sees them first.
Drafting inside workflows
Replies, summaries, and reports drafted for a person to review and send, built into the screens they already use.
Voice features
Spoken input and voice responses where typing is the bottleneck, built with Vapi and Twilio.
How we build it
Fit the codebase, not the other way round.
The stack
- OpenAI API
- Anthropic API
- TypeScript
- React
- Next.js
- Node.js
- Python
- PostgreSQL
- Supabase
- Firebase
01
Start in your repo
We read your codebase and data model first, so the feature fits your architecture, auth, and deploy process.
02
Prototype on real data
A rough version runs against a sample of your actual data early, so the hard cases show up in week one, not week five.
03
Roll out gradually
Staged release, usage and cost tracking, and a fallback for when the model is slow or unavailable.
04
Evaluate and tune
An eval set built from real queries, so every prompt, retrieval, or model change is measured before release.
How to engage
Start with AI Feature Sprint.
An AI Feature Sprint is built for exactly this: one LLM feature taken from your backlog to production, with evals, inside the product you already run.
We start with paid discovery, never an unpaid test project, so the plan rests on your real requirements.
AI Feature Sprint
4–6 weeksAn LLM feature added to a product you already run: RAG, an assistant, extraction, or voice.
Also fits
Fractional engineering
2–3 days a weekGot a roadmap of AI features rather than one? Fractional engineering keeps the same engineer on them week after week.
Common questions
AI integration, answered.
Something else on your mind? Ask us directly at hello@velvy.ai, or book the intro call below.
What is RAG, and do we need it?
Retrieval-augmented generation looks up the relevant passages from your own content and hands them to the model along with the question, so answers rest on your data rather than the model’s memory. You need it when answers must reflect your documents, products, or policies. Tasks like extraction or classification usually do not.
Do you need access to our whole codebase?
We need enough to build and deploy the feature properly: usually the relevant repository, a staging environment, and representative data. We work in your repo with your review process, and access can be scoped to what the feature touches.
How do you handle our data?
Data handling is part of the design. We review the model provider’s current API data-use terms with you, send only the fields the feature needs, and document the data flow for your security review.
How do you control cost and latency?
By choosing the smallest model that passes the eval, caching repeated work, streaming responses, and tracking cost per request from the first deploy. Cost is measured on real traffic before a full rollout.
Can you work alongside our engineers?
Yes. We follow your conventions, open pull requests for review, and pair with your team, so they can own the feature after handoff.
Related services
All servicesGet started
Bring the messy problem. Leave with a plan.
Book an intro call. Tell us what you’re trying to build or automate, and we’ll tell you plainly whether we’re a fit and what a first engagement would look like.
Prefer email? hello@velvy.ai