We don't sell a buzzword grab-bag. Two specialisms run the engagement: multi-agent orchestration when one model isn't enough, and custom LLM fine-tuning when prompts can't reliably express what you need. Backed by the surrounding craft to put either into production.
Your problem probably rhymes with one of these. On the discovery call we'll tell you which, and whether it's worth building at all.
Agent graphs with explicit roles, handoffs, and verification. Built for the work that's too long, too branched, or too consequential for a single prompt.
When prompts can't reliably express what you need. We fine-tune open-source or hosted models on your domain, your voice, and your edge cases, with the eval harness to prove it.
Retrieval pipelines that ground models in your proprietary data. Versioned, evaluated, and observable.
Extraction, classification, summarisation, and domain reasoning where accuracy has a hard floor.
Recognition, inspection, and document understanding. Translating pixels into structured decisions.
Forecasting, optimisation, and anomaly detection where a classical model still beats an LLM on cost and interpretability.
The plumbing that turns a prototype into something on-call at 3am: CI for prompts, eval gates, cost guardrails, rollback.
The right question isn't which model. It's whether the shape of your problem is a graph, a fine-tune, or something a well-written prompt already solves.
All fixed-scope. All quoted in writing before work begins.
Validate the approach on real data before committing to the full build. You get a working prototype, an eval harness, and a side-by-side benchmark.
End-to-end build of one offering: orchestration system or production fine-tune. Deployed to your cloud with observability and handoff.
Both specialisms across several workflows. Shared retrieval, eval, and observability layer. Internal team training included.
Every solution is custom. Pricing depends on the data, the model choice, and what infrastructure you're bringing in. We quote in writing on the discovery call, before any work begins.
Before committing to a custom AI build. The rest happen on the call.
Ask on a discovery callWhen the work spans different skills (research vs writing vs verification), needs human handoffs at specific points, or has long-horizon steps where a single prompt would lose context. We start every engagement by checking if a single well-designed agent works first. If it does, we ship that.
Fine-tuning earns its keep when prompts can't express the pattern reliably (your tone, your taxonomy, your edge cases), when latency or cost matters (a smaller fine-tuned model often beats a large prompted one), or when domain quality has a hard floor that off-the-shelf models can't hit. We benchmark before recommending it.
Open-source first (Llama 3, Mistral, Qwen, Gemma) when you want to own the weights and run inference yourself. OpenAI, Anthropic via Bedrock, or Google when you want hosted endpoints. We pick based on your data sensitivity, latency targets, and ops budget.
Eval harness from week one. We co-build a test set with you that captures your real edge cases, then run every model and prompt change against it. Quality is a number you can watch, not a feeling.
Fully. Source code, prompt graphs, orchestration logic, fine-tuned weights, and infrastructure config are all yours. We deploy to your cloud, your accounts, your provider keys, and hand over everything at launch.
Let's skip the hype and build AI that transforms your business.