AI that ships into your product, not your slide deck.
We have shipped Claude, GPT, and open-source models into production for finance, healthcare, hospitality, and our own SaaS. We build the feature, ship the evals, and stay around to defend the quality numbers.
What we deliver
In-product LLM features
Copilots, summarisation, classification, routing — built around your domain and your data.
Agents and workflows
Multi-step automations with tool use, memory, and human-in-the-loop checkpoints. Hosted in your n8n or ours.
Retrieval over your data
Vector store, retrieval, re-ranking, citations. End-to-end evals before launch.
Eval suite and observability
Quality metrics that catch regressions when a model is updated and budget alarms that catch cost regressions when a customer goes wild.
How we work
Eval-first design
We define what "good" means before we build. If you cannot measure it, we will not ship it.
Spike and measure
Smallest viable LLM workflow, baseline quality and cost recorded, ready to compare against improvements.
Productionise
Caching, fallbacks, prompt versioning, observability — the boring engineering that makes AI features reliable.
Iterate on evals
Quality only improves if you measure it. We hand you a dashboard, not a hope.
Tech stack
Common questions
How do you keep AI costs predictable?
Aggressive prompt caching, model routing (cheaper models for easier tasks), per-feature budget alarms, and a monthly cost review. We have driven 60–80% cost reductions on shipped features.
What about data privacy and model training?
We default to zero-retention API tiers and keep customer data off training datasets. For regulated workloads we run open-source models in your Azure tenant — your data never leaves your perimeter.
Can you take over an LLM feature that someone else built?
Yes — and frequently do. A common starting point is a two-week audit that benchmarks quality, cost, and latency, then a plan to fix what is broken.