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AI Engineering & Automation
Add AI to Your Product and Operations—Measured in Hours Saved
Copilots, workflow automation, and AI agents integrated into the software you already use. Start with one workflow, prove ROI, then scale.
✓ Fixed-scope pilots · ✓ No training on your data · ✓ Human review gates
Who This Is For
✓ Strong fit when you:
- • Re-type data between systems or answer the same questions repeatedly
- • Want AI features inside your SaaS (support, search, drafting)
- • Need a pilot on one workflow before company-wide rollout
- • Require approval points—AI suggests, humans decide
Start elsewhere if:
- • You need a full product built → MVP development
- • Your AI-built app breaks in production → Prototype hardening
Pilots
Typical AI Pilot Timeline & Investment
Pilot timeline
3–6 weeksOne workflow, measured baseline
Typical investment
$15K – $40KFixed price after opportunity review
Run cost
Quoted in scopeModel + volume dependent
What changes
AI that pays its way
Three outcomes we look for. We do not publish a savings figure, because it depends on the baseline we agree.
01
Fewer hours on repetitive work
02
Faster answers for customers and staff
03
AI features your users actually use
Is this you?
When this is the right service
A defined process, data you can use, and a person who can review the output.
- Staff repeat the same reading, drafting, routing or search work.
- The answers already live in documents people cannot find.
- You want the AI inside software you already use.
- You want to know if AI is a poor fit before anyone builds it.
Good fit
- Reading documents, drafting, routing or searching knowledge.
- A process someone can describe step by step.
- A person available to review the output.
Not a fit
- Decisions that need full accuracy with no review.
- Tasks with no data.
- Processes nobody has defined.
Services
What we build
Each item is scoped. The model and the systems are chosen during the opportunity review.
AI applications
Software where the AI feature is part of the product, not a separate chat stuck on the side.
AI agents
Multi-step work across your tools, with approval points where judgement matters.
AI agent developmentGenerative AI integration
Drafting, reading and search inside the tools you already use. The model is chosen during scoping.
Workflow automation
Routing and repetitive steps on one defined process, measured against a baseline before rollout.
Copilots and assistants
Drafts and answers a person approves before they are used.
AI inside existing software
The feature lives in the product or system you have, when that system can be connected.
Enterprise AI
Access controls and audit logs when the scope needs them. What gets logged is agreed in the review.
Honest AI
Where AI helps, and where it doesn't
We will say no when the work is a poor fit. That decision is part of the opportunity review.
| Good fits | Poor fits |
|---|---|
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How it works
From one workflow to a measured rollout
Five steps. Each one ends with something you can read or try.
- 01
AI opportunity review
We look at one workflow and say whether AI belongs there.
You get: a written view of that workflow, including where AI is a poor fit.
- 02
Pilot on one workflow
We build the pilot for the workflow you approved, with a person reviewing the output.
You get: a fixed-scope pilot. The price is quoted after the review.
- 03
Measure against a baseline
We compare the pilot with the baseline agreed in the review.
You get: the before-and-after on that baseline, not a generic return claim.
- 04
Roll out
We put the pilot into the tools your team already uses, for the scope you approved.
You get: the workflow live for the people and systems named in the scope.
- 05
Monitor and improve
We watch for failures and adjust what the scope includes.
You get: a way to see when it fails, and a next change that is quoted before we build it.
Built for production
How we make it dependable
The same six practices, applied to a workflow rather than only to an app release.
Testing
Real cases from the workflow, including cases the model gets wrong.
Code review
A senior engineer reviews every change, including AI-generated code.
Security checks
Access, data handling, and the tool terms, confirmed before a pilot.
Monitoring
A way to see when the workflow fails after rollout.
Documentation
What the workflow does, and where a person must review the output.
Handover
You own the code and the accounts. We hand over how to run it.
Technology
Tools we use, chosen per project
Nothing here is a fixed stack. If a tool is not named, we choose it during scoping.
Proof
Work in this area
Client names stay private. Studies are published on Work when they are written.
AI feedback for learners
Pilot workflow with human review before AI suggestions reach students.
See workDocument & ticket automation
Integrations with existing CRM/helpdesk tools—measure hours saved on one workflow first.
Request AI reviewWays to work together
How to work with us
The recommended start is a fixed-price review, then a pilot if the workflow is a fit.
Recommended
Fixed-price pilot
Best for one workflow, from the opportunity review through a measured pilot.
How it works The review comes first. The pilot is quoted after that review. No price is listed on this page.
AI opportunity review
Start with one workflow
Tell us the process and the tools you use today. We reply about where AI helps, and where it does not.
What happens next
FAQ
Common questions
Answers from the review, the pilot and the baseline. Figures that depend on your workflow are quoted during scoping.
The model is chosen during scoping. It depends on the workflow, the data, and where a person needs to review the output. This page does not name a model.
We only use tools under business terms that don't train on your code. For the documents and records in the workflow, we confirm the same kind of terms during the opportunity review, before a pilot.
We agree a baseline in the opportunity review, run a pilot on one workflow, and measure against that baseline before we roll it out. We do not publish a return figure, because it depends on that baseline.
Yes, when those systems can be connected. The review names the systems. Generative features, copilots and automation are built into the software you already use when the scope says so.
It is quoted during scoping. It depends on the model, how often the workflow runs, and the volume. There is no monthly figure on this page.