AI put to real work
Where AI pays off inside a company, how we build it into the systems you already run, and how we keep it accurate once it is live.
Where AI earns its place
The best first projects share three traits: the work is repetitive, the inputs are text or documents, and a person can check the result. These are the areas where we see that most often.
Customer support
An assistant that answers common questions from your own documentation, and hands everything else to a person with the context already gathered.
Documents and back office
Invoices, contracts and forms read, checked against your rules and entered into your systems, with anything unclear sent for review.
Sales and CRM
Leads enriched and prioritised, meeting notes turned into CRM updates, and follow-ups drafted for your team to review and send.
Data and reporting
Questions in plain language answered from your own data, and recurring reports written and delivered on schedule. Built on our data and analytics work.
IT operations
Related alerts grouped and summarised, likely causes suggested, and routine runbook steps carried out with approval. Pairs with our 24×7 support.
Software delivery
Code review, test generation and documentation support that help your developers ship faster without lowering the bar.
How a project runs, start to finish
Three typical engagements, described the way we scope them. The details change with every client. The method stays the same.
A support assistant that knows your products
A support team spends most of its day answering the same questions, while complex tickets wait in the queue.
An assistant connected to your help centre, manuals and past tickets. It answers in your tone, cites its sources, and passes anything it cannot resolve to an agent with a short summary.
Inside your chat widget or helpdesk. Answers come only from approved sources, and agents can correct any reply, which feeds the next review.
Share of conversations resolved without a hand-off, first-response time, and accuracy on a sample your team reviews each week.
Supplier invoices processed without retyping
Invoices arrive as PDFs and emails in dozens of layouts, and someone copies each one into the ERP by hand.
A pipeline that reads each invoice, extracts the fields, checks them against purchase orders and your approval rules, and posts clean records to the ERP.
Every document gets a confidence score. Invoices that are unclear or do not match go to a review queue instead of the ledger.
Invoices posted without manual edits, time from receipt to posting, and errors caught before they reach the books.
Faster answers when something breaks at 3 a.m.
On-call engineers are woken by dozens of related alerts and lose valuable minutes working out what actually failed.
An agent that groups related alerts, reads recent changes and logs, and posts a short summary with the likely cause and the matching runbook.
Inside your monitoring and chat tools, alongside our 24×7 team. Any action that changes a system waits for an engineer to approve it.
Alerts per incident, time to identify the cause, and how often the suggested cause turned out to be right.
From first idea to running in production
Discover
We pick one process, map how it works today, and agree on what success means in numbers before any build starts.
Prototype
A working version on your real data, tested against examples your team chooses, so you judge results rather than slides.
Integrate
We connect it to your systems, add access control, logging and review steps, and roll it out in stages.
Run and improve
We monitor quality and cost, review samples with your team, and adjust the system as your processes change.
AI you can put in front of customers
Useful AI is predictable AI. Every system we deliver comes with the same safeguards.
Your data stays yours
We choose providers and settings that keep your data out of model training, and keep sensitive information inside your environment when required.
People stay in charge
Clear rules decide what the system may do on its own and what needs a person to approve it.
Measured, not assumed
Each system ships with test sets and dashboards, so you can follow accuracy and cost over time.
Watched around the clock
Systems we build can be monitored by our 24×7 team, like the rest of your infrastructure.
Before you start
Do we need a lot of data to start?
No. Most projects start from documents, tickets or records you already have. We check quality and gaps during discovery and tell you what, if anything, is missing.
Which AI models do you work with?
We work with Claude, OpenAI and open models, and choose for each use case based on accuracy, cost and where your data needs to stay.
How long until something is live?
It depends on the integrations and review steps your process needs. You get a clear timeline in the proposal, after the discovery step and before any work starts.
What happens when the AI gets something wrong?
Each system has checks, confidence thresholds and human review wherever errors matter. We track mistakes and use them to improve the system.
Can it work with the tools we already use?
Yes. We connect to the helpdesk, ERP, CRM, monitoring and chat tools you already run, instead of asking your team to adopt new ones.
Bring us one process
Tell us the task your team repeats most. We will show you how AI would handle it, and what it would take.
Start the conversation
