In-product copilots
Assistants scoped to a workspace with tenant-aware permissions, streaming UI and suggested-action affordances.
Custom AI Integration
Custom AI Integration. AI inside your product, not bolted onto it.
Copilots, semantic search, recommendations and generative features embedded in your own application — with your data and your latency budget.
Overview
Adding AI to a live product is a systems problem: streaming responses, token cost, caching, tenancy isolation, permissions and a UI that sets the right expectations. We build features your users trust — copilots that cite sources, search that understands intent, models that respect who is allowed to see what.
Typical outcomes
-61%
Inference cost after tuning
+27%
Feature activation vs baseline
100%
Answers with source citations
<800ms — Time to first token in-app
Capabilities
What custom ai integration covers with us.
Pick the parts you need. We will tell you honestly which ones you do not.
Semantic & hybrid search
Vector plus keyword retrieval with filters, faceting and relevance tuning against your own click data.
Model fine-tuning & distillation
Task-specific fine-tunes and smaller distilled models to cut latency and unit cost without losing quality.
Multimodal features
Vision, speech-to-text, transcription, summarisation and document understanding as first-class product surfaces.
Cost & latency engineering
Prompt caching, model routing, batching and streaming so unit economics work at your pricing, not just in a demo.
Self-hosted & private models
On-premise or VPC deployment with vLLM and quantised open models for regulated and air-gapped environments.
Deliverables
Exactly what lands in your hands.
Every engagement ends with artefacts your team can use without us in the room.
- Feature specification with UX patterns for AI behaviour
- Reference implementation in your codebase
- Prompt and retrieval layer under version control
- Cost model per user, per tenant and per request
- Safety, abuse and rate-limit controls
- Engineering enablement so your team can extend it
Technology we use
Model access
OpenAIAnthropicBedrockVertex AIAzure OpenAIvLLM
Tuning
LoRA / QLoRAPyTorchHugging FaceUnslothDSPy
Serving
FastAPINode.jsgRPCRayModalTriton
Data
pgvectorRedisKafkadbtSnowflake
Process
How a typical engagement runs.
Timelines vary with scope, but the sequence and the checkpoints do not.
Feature framing
We define the user job, the acceptable error rate and the interface that makes uncertainty visible.
Spike
A throwaway prototype to prove feasibility, latency and cost before committing to architecture.
Integrate
Built into your repository, behind a feature flag, with tests and observability from the first commit.
Tune
Retrieval and prompt iteration against real usage, plus distillation once quality is locked.
Enable
Documentation, internal workshop and pairing sessions so your engineers own it afterwards.
FAQ
Custom AI Integration questions.
Can this run entirely in our own infrastructure?
Yes. We deploy open-weight models in your VPC or on-premise with vLLM, including GPU sizing, autoscaling and a fallback plan for capacity spikes.
How do you stop one tenant seeing another tenant's data?
Permission filters are applied at retrieval time, not in the prompt, and we include automated tests that attempt cross-tenant access on every build.
Which model should we use?
Usually more than one. We route by task complexity so cheap models handle the volume and frontier models handle the hard cases, behind a single interface you can swap later.
Pairs well with
Services teams usually combine with this.
Next step
Need custom ai integration? Let us scope it.
Send a short brief and a senior specialist will reply within one business day with questions, an approach and an honest cost range.