Capabilities
Three disciplines that meet in one system.
Systems integration
Many vendors, one design.
We architect, connect and test the whole, and take single-point accountability for how it behaves together.
What we do
- Map what you run today: components, owners, interfaces and the handoffs between them.
- Design the target architecture across infrastructure, networks, platforms and applications, with the trade-offs written down.
- Choose components for fit, from any vendor, and explain the choice.
- Connect and configure what exists, and build the interfaces that are missing.
- Test the system as a whole: load, failure, recovery and permissions.
What you get
- One team accountable for how the whole system behaves.
- An architecture you can read: diagrams, decisions and owners, kept current.
- Interfaces with clear contracts and tests that run on every change.
- A documented way to replace or upgrade any component later.
What we do not do
- Steer you toward a vendor because of a commercial arrangement.
- Hand over a system and leave.
- Replace the team that will own the system. We work alongside them.
AI systems
Models and agents built into the system, not beside it.
Retrieval grounded in your data, tool access with least privilege, and evaluation that runs before every release.
What we do
- Decide where AI is the right answer for a task, and where a rule, a query or a person is better.
- Build retrieval over your own sources, with access controls that match the systems they come from.
- Give agents the narrowest set of tools and permissions the task needs, and log what they do.
- Build evaluation sets from real cases and run them before every release.
- Watch quality, drift and cost in production, alongside the rest of the system.
What you get
- AI components with named owners, documented limits and a tested fallback.
- An evaluation suite your team can read, extend and run.
- A written record of what each agent can see and do, and why.
- Cost measured per task, so it is known before the invoice arrives.
What we do not do
- Add AI where a simpler method does the job.
- Release an agent without an evaluation set and a way to switch it off.
- Give a model wider access than its task requires.
Workflow engineering
We start from the work, not the demo.
We map how a result moves from a model to a decision to an action, and where a person should stay in the loop.
What we do
- Observe the work as it is done today, including the workarounds.
- Map each decision: what it needs, who makes it, and what happens next.
- Decide where a person approves, reviews or takes over, and design those handoffs.
- Connect results to the systems where action happens, so output does not stop at a screen.
- Measure the workflow, not only the model: time, rework and exceptions.
What you get
- A workflow map that the people doing the work recognize as accurate.
- Clear points where a person stays in the loop, with what they need to decide.
- Exception paths that are designed in advance.
- Changes released in steps the people doing the work can absorb.
What we do not do
- Automate a process nobody has looked at closely.
- Remove human review to make a demo look faster.
- Judge success by model accuracy alone.
Most systems need some of each.
Describe what you run and where it hurts. We will start with questions, and suggest a place to begin once we understand the system.