AI and automation
AI automation for the workflows your team handles every day.
We design and build AI workflows that read your systems, prepare the next action, and hand consequential decisions to a person. Each one has defined permissions, an evaluation plan and an estimated operating cost that we check against real usage.
Which tasks suit AI automation?
Good candidates are frequent, rely on information spread across systems, and have a reviewable output.
- Support triage: classify tickets, gather account context, draft a reply for review.
- Operations: read incoming documents, extract fields, and prepare records for approval.
- Knowledge work: answer internal questions from your documents, with sources shown.
- Sales and account work: summarize history before a call and draft follow-ups.
Start with a discovery engagement
Workflow map
Who does the task today, which systems they open, and where time goes.
Feasibility assessment
Which steps AI can do reliably, which need a person, and which should stay manual.
Evaluation criteria
A test set from your real cases and the thresholds for correctness, source support and safety.
Integration plan
Systems to connect, permissions per tool, approval points, and fallback to manual handling.
Prioritized implementation scope
The first workflow to build, its expected operating cost, and how we will measure it.
From discovery to operation
- 01
Discover
Map the workflow and collect real examples to evaluate against.
- 02
Prototype and evaluate
Build the workflow on your data and score it against the agreed criteria before anyone relies on it.
- 03
Integrate with approvals
Connect your systems with scoped permissions. Consequential actions wait for a reviewer.
- 04
Operate and improve
Monitor quality, failures and cost per task; review results with you monthly.
Architecture, integrations, evaluation, ownership
Permissions
Each tool the model can call has a written scope. Write actions such as refunds or account changes require approval.
Retrieval and provenance
Answers show the records they used. Retrieval respects the user's own access rights.
Evaluation
A fixed test set checks correctness, source support, unauthorized actions, handling time and cost before each release.
Failure handling
Missing context, low confidence or failed checks send the case back to manual handling, with the reason logged.
FAQ
What determines the cost of AI automation?
Separate discovery and build costs from model usage, hosting, human review and maintenance. Workflow volume, exception rates and system access often matter more than the model price. Our AI automation cost guide explains the inputs to a useful estimate.
Do we need automation or an AI feature in our product?
Choose automation when the goal is a better process across people and systems. Choose AI integration when the product and workflow already exist and need a specific AI capability.
Which models do you use?
We choose per workflow based on the evaluation results, data requirements and cost. The design keeps the model replaceable.
What does it cost to run?
Discovery estimates the cost per task from your expected volumes, model use, integrations and human review. After launch, we check the real cost alongside quality. How to plan AI automation costs.
Where does our data go?
We agree data handling before the build: which data the model sees, which provider processes it, and retention settings. Sensitive fields can be excluded or masked.
Is this the same as AI-assisted delivery?
No. AI automation is a system we build for your business. AI-assisted delivery describes how our engineers use AI while writing software.
Next step
Have a workflow like this?
Tell us which systems your team uses and where manual work slows the process. We will suggest whether AI fits.