Services
AI & workflow automation
We start by working out where a language model genuinely saves time in your product or your operations, then build that with evaluations, guardrails and a fallback for when it gets things wrong.
Pressure to add AI, without a use case
Most requests we get start as add AI to it, which is a technology looking for a job. The projects that work start from a task instead: someone spends six hours a week reading documents and copying fields into a system, or support answers the same forty questions with slightly different wording. Those have a measurable before and after. A chatbot bolted onto a homepage usually does not.
We scope where AI actually saves time in your product or operations, then build it with proper evals, guardrails, and fallback behavior.
What you get
- LLM feature scoping and prototyping
- RAG pipelines and eval harnesses
- Internal workflow automation
- Cost and latency monitoring
How an AI engagement runs
Find the task worth automating
We look for work that is repetitive, high volume and tolerant of review. Then we estimate the hours it currently costs, which becomes the number the project is judged against. If we cannot find a task that clears the bar, we will tell you rather than build something impressive and useless.
Prototype against real data
A quick version tested on your actual documents and messages, not a curated demo set. This is where most AI ideas either prove out or fall apart, and it is much cheaper to find out here.
Build evaluations before scaling
A set of cases with known correct answers, so a change to a prompt or a model can be measured instead of guessed at. Without this you have no way to know whether an update made the system better or quietly worse.
Guardrails and a fallback path
Constrained outputs, a confidence threshold, and a human handoff when the model is unsure. Every feature we ship has a defined answer to what happens when it gets this wrong.
What we work with
- Claude, GPT and open models
- Retrieval augmented generation
- Vector search
- Evaluation harnesses
- Structured output and validation
- Human in the loop review
- Cost and latency monitoring
FAQ
Whichever fits the task, the budget and the data constraints. We benchmark two or three against your evaluation set rather than commit up front, and we keep the integration model-agnostic so switching later is a config change rather than a rebuild.
We use API tiers that exclude your data from training, and where the data is sensitive we look at what can stay inside your infrastructure. This gets decided and written down before anything is built, not after.
Assume it sometimes will. That is why we scope AI to tasks where a wrong answer is recoverable, put a review step where it is not, and monitor outputs in production. Any use case where a silent error is unacceptable and unreviewable is one we will advise against automating.
Have a product that needs to ship?
Tell us where things stand and what you're trying to hit. We'll respond within one business day with next steps.