Generative AI Integration.
LLMs, RAG, and copilots inside your product.
Add a copilot to your product without lighting your latency and cost budget on fire.
What it is
A "chat with your data" demo takes an afternoon. Shipping one that survives Monday is a different job.
Generative AI Integration is about plumbing LLM capability into a product surface that already exists — not building a new one alongside it. We work inside your codebase, your data model, and your auth story.
The interesting problems aren't the model; they're the retrieval, the guardrails, the streaming UX, the cost per session, the eval regime that stops a silent regression, and the fallback when the provider has a bad day.
You leave with a copilot your team owns end-to-end — including the parts nobody talks about in demo videos.
What you get
6 concrete things, on the SOW.
Every deliverable is written into the statement of work — priced, dated, and signed off by a named engineer at the relevant gate.
- 01Copilot integrated into your existing product
- 02Retrieval layer over your data, with cite-back
- 03Streaming UX components (with cancel/retry)
- 04Rate limiting, quota, and cost telemetry
- 05Prompt injection & jailbreak defence tests
- 06Provider failover and offline degrade paths
Where this shows up
Three shapes of engagement.
Different problems, same method. These are the concrete work shapes we typically deliver under Generative AI Integration.
Inline product copilot
An assistant that understands the state of your app — the selected row, the current filter, the user's role.
Doc / knowledge chat
A grounded chat over your policies, contracts, or wiki, with citations you can click.
Agentic actions
The copilot can DO things — send an email, file a ticket — with confirmation at every side-effect boundary.
The stack
Capabilities, not vendors.
The requirement picks the tool, not the other way round. Naming vendors up front would set the wrong ceiling on what we take on.
- RAG & retrieval
- Streaming UI
- Guardrails
- Telemetry & cost
- Failover
- Red-team suites
How it runs
Seven stages. One signature at a time.
Every Generative AI Integration engagement runs through the same seven-gate Aivora Delivery Engine — each stage run by specialised agents, each ending at a gate a senior engineer must sign.
Frequently asked
Questions people ask before booking.
How do you handle hallucination?
Retrieval-grounded outputs with cite-back, structured extraction where possible, and eval sets that fail the build on unsupported claims. Confidence is instrumented per-response.
What's the impact on our infrastructure cost?
We model cost-per-session before we integrate. Rate limits, caching, prompt compression, and cheaper-model routing are all part of the design — not a follow-up ticket.
Can we swap providers later?
Yes. The integration is written against a thin capability interface, so switching providers is a config change, not a rewrite.
See the work
This expertise, in a product.
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Ready when you are
Bring us the hard bit.
Ninety-minute kickoff. Five-day audit. Fixed quote for Generative AI Integration — in writing, before we build.