AI solutions and automation
Everyone is being asked what their AI plan is. The useful answer is rarely a chatbot. It is usually a specific, unglamorous job, such as sorting thousands of customer comments, answering questions from your own documents or drafting the weekly report, that a machine does faster and a person can check. We help you find those jobs and build the ones worth building.
Signs you could use this
- Leadership wants an AI plan and nobody can name a first use.
- Your team spends days reading survey answers, reviews or support tickets by hand.
- People paste company data into public chat tools and no rule exists.
- A demo impressed everyone, but nothing reached real use.
- Reports are put together by hand every week or month.
What we do
- Find the right problems
- We list the repetitive, text-heavy tasks in your team, estimate what they cost in hours and pick the one or two where AI is likely to pay back. We also tell you which ideas aren't worth the effort.
- Make sense of your text
- Topic models, classifiers and language models can sort feedback, tickets and survey comments into themes you can count and track. Our founder built one of these for a large home improvement retailer, which removed more than 2,700 hours of manual review.
- Build small, useful tools
- Search and question answering over your own documents and data, summaries, automatic tagging and report drafting. Each tool is built with a person in the loop and a clear way to check its answers.
- Set sensible guardrails
- We help you decide what data may go to outside services, how outputs are checked and how to measure whether the tool helps. Language models can sound sure when they are wrong, so we design for that.
- Measure the result
- Hours saved or errors avoided, before and after, so you know whether to carry on.
What you receive
A scoping workshop and a prototype usually take three to six weeks. Moving a prototype into daily use takes longer, depending on how many systems it touches and how much checking it needs. We agree the scope in stages.
- A ranked shortlist of use cases with effort and payback estimates
- A working prototype on your real data, not a slide
- Test results showing where it works and where it fails
- Guidelines on data privacy, checking and ownership
- Documentation and a handover session
- A plan for what to do next, including stopping
Tools we use: Python, SQL, Language model APIs, Embeddings and vector search, Topic modelling, BigQuery, Airflow, Power BI.
Common questions
Will you send our data to a public AI service?
Only if you are comfortable with it, and never before agreeing the terms. For sensitive data we look at options such as services that don't train on your data, private deployments or approaches that keep everything inside your own systems.
Do we need clean data first?
For some uses, no. Messy text is exactly what these tools handle. For others, such as forecasting, data quality decides everything. We tell you which kind of project you have.
Will AI replace our analysts?
That's not what we build for. The best results we see come from people spending less time on assembly and more on judgement, so we design with a person in the loop.
How do we know it is working?
We agree a measure at the start, such as hours saved or accuracy against a hand-checked sample, and test it before anyone relies on it.