AI Integration
Make your existing product smarter
Add LLM-powered features — search, chat, drafting, automation — to the product you already run, without a rebuild.
Most teams do not need a new AI product — they need their current one to do a few things automatically that today take a person. We add those capabilities: natural-language search over your own data, a support assistant grounded in your docs, auto-drafted replies, document summarisation, or classification and routing that clears a manual queue.
We work against your existing stack and connect to whichever model fits — OpenAI, Anthropic, or an open model you host — with a thin abstraction so you are never locked to one vendor's pricing. Features ship behind flags, start on a slice of traffic, and expand once the quality and cost numbers hold up in production.
Engagements are deliberately small and fast: a first useful feature usually lands in 3 to 5 weeks. We instrument token spend and latency from day one, so you can see exactly what each feature costs per user before you roll it out wide.
Find the highest-value slice
We look at where your team spends repetitive effort and pick the one or two AI features with the clearest payback, then define what 'good enough to ship' actually means in measurable terms.
Prototype against real data
We build against your actual content and edge cases, not a demo dataset, and put an evaluation harness in place so we can compare prompts and models on quality and cost objectively.
Ship staged and measured
The feature goes live behind a flag on a small traffic slice with cost and latency tracked per request. We widen the rollout only once the numbers hold.
Will our data be used to train someone's model?
No. We use enterprise API tiers that contractually exclude your data from training, and for sensitive workloads we can run open models on infrastructure you control so nothing leaves your environment.
How do you stop the AI from making things up?
We ground responses in retrieval over your own content, constrain outputs with structured prompts and validation, and add fallback behaviour for low-confidence cases. We also ship an evaluation set so you can measure accuracy rather than trust a vibe.
What will this cost to run every month?
That depends on usage and model choice, which is exactly why we instrument token spend per feature from day one. You get real per-user cost numbers during the pilot, so the production bill is a decision you make with data, not a surprise.
READY TO BUILD?
LET'S SCOPE IT.
Tell us what you're building. We reply within 4 hours — no sales fluff, just a straight answer.