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AI & Automation

Automate the work that shouldn’t require people.

Most useful automation is unglamorous. Someone reads an email, copies figures into a system, checks a document against a rule, writes the same reply for the fortieth time. We find those steps, work out which ones software or a language model can do reliably, and build the automation into the tools your team already uses.

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You might be here because

  • Your team re-types data from PDFs, emails or forms into another system.
  • Leads wait hours for a first reply because someone has to read and qualify each one.
  • Support answers the same questions from the same knowledge base every day.
  • You suspect AI could help but cannot tell a real opportunity from a demo.

What we do

AI agents
Agents that complete a defined task across your tools, with limits on what they may do.
AI workflows
Multi-step pipelines where a model handles the judgement and code handles the rest.
AI integrations
Language-model features added to a product you already have.
RAG applications
Answers grounded in your own documents, with sources shown.
Document processing
Extraction of structured data from invoices, contracts, statements and forms.
Lead qualification
Inbound enquiries read, scored, enriched and routed to the right person.
Customer support automation
Drafted or automatic replies for routine questions, with handoff to a person.
Internal AI tools
Search and assistants over your own knowledge, behind your own access controls.
Business process automation
Rule-based automation where AI is not needed at all.

What this looks like in practice

A monthly report nobody has to compile

Competitor Marketing Watchdog gathers competitor activity and uses an LLM to turn it into a written strategy report each month.

Plans generated from a person’s constraints

Fitness AI Planner generates training and diet plans from a user’s goals, equipment and locally available food.

Inbox to CRM without retyping

A typical first project: read incoming enquiries, extract the details, create the CRM record and draft the reply for approval.

How we approach it

  • We test feasibility on your real data before proposing a build.
  • Every automation has a fallback: when the model is unsure, a person decides.
  • Outputs are logged so accuracy can be measured rather than assumed.
  • If a rule and a script will do the job, we will say so and skip the model.

A defined way to start

For businesses that believe AI could reduce manual work

AI Automation Discovery

We look at how the work is done today and tell you which parts can be automated reliably, and which cannot yet.

  • Workflow analysis
  • Automation opportunities
  • AI feasibility on your own data
  • Proposed architecture
  • Implementation roadmap
Explore an Automation Opportunity

Related work

All case studies

Other services

Have a technical problem you’re trying to solve?

Tell us what you’re building, fixing or automating. We’ll tell you honestly whether we can help.

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