Precision prompt design and fine-tuning that make your AI tools more accurate, consistent and on-brand.
We tighten prompts and fine-tune models against real examples from your business, so outputs are consistent enough to trust in production.
A focused technical and operational audit
A working prototype against real inputs
Human approval points and reliable fallbacks
Documentation your team can own
A measured rollout, not a big-bang launch
Rewriting brittle prompts that work in testing but fail on real, messy production input
Fine-tuning a model on your historical examples so outputs stop needing heavy manual editing
Building evaluation sets so you can measure accuracy before and after every prompt change
Usually, yes — real historical examples from your business are what fine-tuning actually learns from. If you don't have this yet, prompt engineering on a general model is often the better starting point.
No — most accuracy problems are solved with better prompts and guardrails first. Fine-tuning is a second step we only recommend once the evidence shows a general model has hit its ceiling.
Every customer-facing system launches with defined guardrails and an escalation path, and runs in a supervised mode until we have real production data on its accuracy — not just evaluation-set numbers.
Almost always the former. We design the system, prompts and guardrails around an existing frontier model, since that's faster and more reliable for most business use cases. Custom fine-tuning only happens when the evidence says a general model can't hit the accuracy bar.
Tell us where work gets stuck. We’ll come back with a sharper view of what to automate, what to keep human, and what to leave alone.