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Services · AI workflow automation

AI workflow automation: fixed steps, AI where judgement is needed.

We take a process your team already runs, keep its triggers and steps, and put AI where it earns its place: reading, classifying, drafting. Anything unsure goes to a person.

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Where the person steps in
Triggernew email,form, ticket1AI stepclassify,extract, draft2Checkconfidentenough?3SystemsCRM, ERP,your tools4sureHuman reviewexceptions onlyunsureapprovedcorrectionsbecome testsRun logEvaluation setMonitoringFixed steps. AI where judgement is needed.
4success criteria set before a legal-operations pilot: accuracy, review time, adoption, exceptions
1high-value workflow proved before widening to a platform
Technologies behind this work
Automation & integration
n8n
Supabase
Zapier
Make
Retool
Temporal
Airtable
HubSpot
GitHub
Models & data
OpenAI
Claude
LangChain
Google Gemini
Mistral
PostgreSQL
Qdrant
Redis
Python
What is included

Three areas, from the process to a system people rely on.

We start with the people who run the process, then choose the model.

Find the workflow

Choose what is worth automating.
  • Map the process with the people who run it
  • Pick the step where AI earns its place
  • A success measure agreed up front

Build

Fixed steps, with AI where judgement is needed.
  • Triggers and fixed steps
  • An AI step that reads, classifies, extracts or drafts
  • An exception queue for a person to review
  • Integration with your systems

Make it reliable

What separates a pilot from a product.
  • An evaluation set and success metrics
  • A run log and monitoring
  • A named owner and an adoption plan
Typical situations

When teams come to us.

Repeat

A team repeats the same decisions every week

We map the process with the people who run it, find the steps worth automating, and build the smallest useful system.

Scattered

People experiment with AI tools, and nothing is controlled

We replace the scattered experiments with one controlled workflow, with measures for accuracy, review time and adoption.

Trust

The demo impressed everyone, production scares them

We add the evaluation set, the review step and the exception handling that let people trust it.

What you get

Deliverables you will keep.

Concrete artifacts, documented and handed over.

A working AI workflow inside your tools
An evaluation set and success metrics
An exception queue and human review
A run log and monitoring
A named owner and an adoption plan
A pilot report with a clear go or no-go
How to start

Three ways to begin.

Larger work starts with a short paid discovery.

All offers
Build

AI Workflow / Internal Product Build

AI and internal tools around real workflows, not demos.

Custom scope
Sprint

Validation Sprint

A short proof and decision checkpoint before you commit more time, money or roadmap.

Usually 2–3 weeks
Discovery

Short paid discovery

Map the system, the risks and the constraints. Ends with a clear plan, not a strategy deck.

A few working days to a few weeks
Proof

Work you can open and check.

See all 11 case studies
4
pilot success criteria
Legal operations AI pilot
Accuracy, review time, adoption and exception handling, defined before the pilot.
Feasible
next gate: a live pilot
AI workflow automation prototype
One high-value workflow proved before expanding into a broad automation platform.
Built
for paid use
AI service for property marketing
Durable processing, tested access and accounting boundaries, and a reviewed security audit.
Where this ends

Not quite what you need?

Want a system that chooses its own steps?AI agent developmentNeed the data underneath first?Data engineeringWant to test the idea before building?Consulting & product design
FAQ

Questions about ai workflow automation.

How is this different from AI agent development?
In workflow automation the path is known in advance: a trigger, fixed steps, and AI doing one hard step such as reading a document. In an agent, the system chooses its own steps toward a goal. If you can draw the flowchart before you start, it is workflow automation.
When do you use n8n or Make, and when custom code?
For simple, low-risk flows a workflow tool is faster and cheaper. When the process is core to the business, handles sensitive data, or needs tests and evaluation, we write code. We say which at the start.
How do you measure value?
We agree the measures up front: accuracy against a reviewed sample, review time per item, adoption by the people who run the process, and how exceptions are handled.

Tell us about the workflow.

Describe who runs the process today and what slows them down. We reply with a clear next step, usually within 24 hours.

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