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AI and intelligent automation services

Most AI pilots stall in the same place: the demo was impressive, nobody could say whether the output was correct, and it never got trusted with real work.

We start from the task, not the model. Pick a process with a measurable cost, define what a good answer looks like, build the evaluation harness alongside the feature, and keep a human in the loop wherever being wrong is expensive.

When to call us

You probably need this if…

  • Staff retype information from documents into systems all day
  • Support agents answer the same questions from scattered internal docs
  • An AI pilot impressed everyone and then quietly stopped being used
  • You need to know how often the system is wrong before you can deploy it

How we work

Our approach to ai & intelligent automation

Use-case selection

We shortlist candidate processes on volume, cost of error and how easily quality can be measured, then start with the one that is high value and safely reversible. Some tasks turn out not to need a model at all, and we will say so.

Assistants and RAG

Retrieval over your own documents, with permissions respected at query time and answers that cite their sources so users can verify them. Retrieval quality, not the model, is what usually determines whether people trust it.

Document and workflow automation

Extraction from invoices, claims, contracts and forms, wired into the systems that consume them, with confidence thresholds that route uncertain cases to a person instead of guessing.

Evaluation and guardrails

A labelled evaluation set, regression runs on every prompt or model change, and monitored accuracy in production. You get a number for how often it is right, which is the difference between a demo and a deployment.

Deliverables

What you actually receive

Artefacts, not activity. Everything here is something you keep, and something you could hand to another supplier.

  • Use-case assessment with expected value and risk per candidate
  • Working assistant or automation on your own data
  • Evaluation harness, labelled test set and accuracy baseline
  • Human-in-the-loop review workflow for low-confidence cases
  • Cost-per-task model and production monitoring

Technologies we use for this

  • Claude
  • OpenAI
  • LangChain
  • Python
  • pgvector
  • Pinecone
  • FastAPI
  • TypeScript
  • Databricks

More on how we choose between them on our technology stack page.

FAQ

AI & Intelligent Automation: common questions

Will our data be used to train someone else's model?

Not under the enterprise API terms we deploy on, and we confirm the specific provider's data-handling terms in writing as part of design. Where the data cannot leave your environment at all, we design for a self-hosted or in-region model instead.

How do you stop the system inventing answers?

Ground answers in retrieved sources and show the citation; refuse rather than guess when retrieval finds nothing relevant; set confidence thresholds that hand off to a human. Then measure how often it happens, because the honest answer is never zero.

What does it cost to run?

We model cost per task before building, including retrieval and review time, so you can compare it against the process it replaces. Ongoing inference cost is usually a small fraction of the salary cost of the manual process.

Talk to us about ai & intelligent automation

Book a free [30] minutes consultation. You will talk to an engineer, not an account manager, and you will leave with a concrete opinion on your options | whether or not you hire us.