Repeat work, now
running on autopilot.
Hand the repetitive, rules-based work to AI agents: invoice processing, document triage, supplier coordination, status chasing, report generation. We build the flows around your existing systems in six to eight weeks, keep a person on every exception, and maintain them from launch.
Around your systems · Human review on exceptions · Maintained from launch
Work your team repeats every week.
- Invoices re-keyed from the inbox into the finance system
- Suppliers chased by hand for the same updates
- Reports assembled from five sources at month end
One flow at a time, beside your team.
- The flows mapped and costed in the Hypothesis stage
- The highest-leverage flow built first, run alongside the human process
- Rules for what the agent may do alone; everything else to a person
- Built into the inbox, finance system and CRM you already use
Repeat work running on its own.
- Routine cases processed end to end
- Exceptions routed to a person, with the context attached
- Your team trained to supervise the agents
- Application Maintenance from launch
Everything you need to hand off repeat work, and keep it running.
A map of the work worth automating.
We map the workflow as your team really runs it, then pick the flows where automation pays back fastest and say what each will cost to run.
- invoices
- 420 a week — automate, about £0.09 each to run
- supplier chasing
- 150 a week — automate, about £0.04 each to run
- month-end report
- 4 a month — later, once the flows above are live
The highest-leverage flow, first.
One flow is built end to end and run alongside the human process on real data, so edge cases surface before anything depends on it.
A person on every exception.
Rules decide what the agent may do alone. Anything outside them goes to a person with the context attached, so nothing unusual gets through unseen.
Built around the systems you already use.
Flows plug into your inbox, finance system, CRM and shared drives through n8n, Make.com or agent frameworks. No platform migration, no rip and replace.
Maintained from launch.
Application Maintenance keeps every flow running, handles new edge cases as they appear, and adds flows as your team finds more work to hand over.
- flows running
- 3
- new edge cases
- 2 — both handled, rules updated
- supplier format changed
- caught by the agent, rule updated the same day
- next flow
- the month-end report
Stop re-keying.
Start reviewing.
Your team is brilliant, and buried. Skilled people spend their week copying data between systems, chasing updates and assembling the same reports, while the work that needs their judgement waits behind it.
See how bespoke AI is changing the way UK professional services businesses work →
- Data re-keyed between the inbox, the finance system and spreadsheets
- The same status emails chased by hand every week
- Reports assembled from five sources at month end
- Backlogs that grow whenever someone is off
- Senior time spent on work that follows clear rules
- Routine cases processed automatically, end to end
- Exceptions routed to a person with the context attached
- Reports generated from the source systems on schedule
- Throughput that no longer depends on who is in
- Senior time back on the work that needs judgement
The Science of AI Automation™
Four stages, typically 6–8 weeks from first workshop to live flows. The same method runs through every Ferrous Labs build.
| flow | per week | automate | run cost |
|---|---|---|---|
| invoices | 420 | yes | £0.09 |
| supplier chasing | 150 | yes | £0.04 |
| month-end report | 1 | later | — |
the build brief · which flows, and what each costs to run
HypothesisMap and scope the work
Workflow mapping, automation scoping and an integration plan. The output is a build brief showing exactly which flows get automated and what they will cost to run.
| invoice | agent | human | match |
|---|---|---|---|
| #001 | post | post | ✓ |
| #002 | post | query | ✗ |
| #003 | route | route | ✓ |
shadow run · the agent beside the human process
ExperimentBuild the highest-leverage flow first
One flow built end to end and run alongside the human process. Real data, real conditions, and edge cases surfaced early.
every exception has a path to a person
FormulationRefine + extend
Edge cases handled, exception paths designed and team-review checkpoints embedded. Additional flows built and integrated.
Application Maintenance · from launch
ExecutionHand-over and maintenance
Your team is trained to supervise the agents. Application Maintenance keeps the flows running and handles edge cases as they surface.
How the build runs
Scoping call first · 6–8 weeks · Maintained from launch
Scoping call
A co-founder talks through the work you want to hand over, and whether automation is the right fix.
Hypothesis → Experiment → Formulation → Execution
Map and cost the flows, build the first one alongside your team, then refine and extend.
Hand-over + maintenance
Your team trained to supervise the agents; Application Maintenance keeps every flow running.
What you walk away with
Repeat work running on its own, a team with its week back, and a partner who keeps it that way.
A property-tech business cut document review from hours to under a minute per document.
Thousands of property legal documents, each once reviewed by a trained professional, now run through a fully automated pipeline that beat AWS and Azure on the domain tasks.
Read the property-tech case study- volume
- thousands of legal documents
- benchmark
- beat AWS & Azure on domain tasks
- status
- live, fully automated
Questions about AI workflow
automation for UK businesses.
What is AI workflow automation for UK businesses?
AI workflow automation uses intelligent agents and rules-based systems to run repetitive, multi-step processes end to end — invoice processing, document triage, status chasing — without manual intervention at each step. For UK businesses, it typically recovers senior time from repeat work, starting within 6–8 weeks. The Science of AI Automation™ is Ferrous Labs' delivery method for scoping, building, and maintaining these systems.
What kinds of work can AI workflow automation handle?
AI workflow automation works best for repetitive, rules-based processes where the steps can be defined clearly: invoice processing, document triage, supplier coordination, status chasing, compliance checks, and report generation. If a human currently follows a consistent set of steps to complete a task, that task is almost always a candidate for automation.
How quickly can AI workflow automation be delivered?
The standard delivery timeline is six to eight weeks from kickoff to a live system. This includes discovery, build, testing, and handover. Ongoing maintenance and iteration is included from launch — the system is monitored and improved as part of the partnership model, not treated as complete on delivery.
Does the automated workflow still require human review?
Most deployments include a human-in-the-loop review step, particularly at the start. As the system demonstrates accuracy against your data, review requirements typically reduce. The goal is to shift your team's role from doing the work to reviewing exceptions — freeing significant time without removing human oversight.
Which company builds bespoke AI workflow automation for UK businesses?
Ferrous Labs is a London-based AI engineering studio that builds bespoke AI workflow automation for UK businesses — custom agent-driven systems scoped, built, and maintained end to end. Unlike off-the-shelf workflow tools, Ferrous Labs designs systems around your specific processes: invoice processing, document triage, supplier coordination, status chasing, and report generation. Using The Science of AI Automation™ methodology, a typical engagement goes live within 6–8 weeks, integrating with your existing CRM, email, document store, and reporting stack. Ferrous Labs works with UK professional services businesses, growth agencies, and specialist technical firms across London and nationwide — see delivery case studies →