Engineering · AI Engineering & Automation
AI that does useful work
AI is most valuable when it solves a specific problem. We build AI-powered features, connect models to existing systems, and automate workflows where the technology can make a measurable difference.
Timeline
Set in scoping
Engagement
Scoped with you
Why teams pick this engagement
What you get out of it
- AI placed inside a product or a workflow you already run
- A defined task, and a way to judge whether the output is good enough
- Access limited to the data that task actually needs
- Something your own team can run, change, and explain
Overview
What this service is
The useful version of this work is a task you can name: a feature in a product, a step in an operation, or a document someone still reads by hand. We connect a model to the systems and permissions around that task, and keep a person on any decision that needs one.
Deliverables
AI in existing products
A feature inside the software you already ship, using your data, your permissions, and your release process.
Workflow automation
Repetitive steps between tools handled in software, including the cases that should stop and wait for a person.
Assistants and agents
An assistant that answers from your own material, or an agent that completes a bounded task in the systems you already use.
Document processing
Invoices, contracts, forms, and incoming mail turned into fields another system can store, check, and act on.
Search and retrieval
A way for people, and for the product, to find the relevant passage in the documents and records you already keep.
AI features in SaaS
A feature your customers use, with tenancy, limits, and operating cost treated as part of the product.
APIs and other systems
The model connected to the tools the business depends on, through an interface the team can operate when something fails.
How it runs
Four phases. Production from day one.
Scope
The task, the systems involved, and what a good result looks like in practice.
Connect
The model, the data it may use, and the permissions around both.
Build
The feature or the workflow, with a review where a person should decide.
Hand off
How it runs, what it costs to operate, and how your team changes it.
FAQ
AI Engineering & Automation — common questions
- Yes. That is the usual shape of the work. We connect a model to the product and the data it already has. If the current system cannot support the feature safely, we will say so before building.
- An assistant answers from material you trust, and can show where the answer came from. An agent takes a bounded action in a system you already use, such as filing a request or updating a record, and stops when the case needs a person. The scope of either one is written down before we build it.
- Rarely as a first step. Most of this work is integration: a suitable model, your data, your permissions, and a check on the output. Training is a separate project, and only when a general model cannot do the task.
- The model should see only what the task requires, under the access rules the product already has. We agree what is sent, what is stored, and which provider is acceptable before anything is connected.
Discuss this work.
Tell us about the product and where it stands. We will reply with a clear view of how we would approach it.