AI agents and automation that survive contact with real work
Agents that do a specific job inside a real workflow, with the failure cases handled — not a demo that works once on stage.
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Five shapes of agent work, each one owning a single job
None of these is a platform. Each is one job with a defined handoff to a person for the cases the model should not decide alone.
- Workflow agents
- An agent that owns one job end to end — triaging a ticket queue, drafting a reply, extracting terms from a document — with the handoff to a human defined rather than assumed.
- Document and data extraction
- Turning invoices, leases, claims and clinical notes into structured records, with confidence scores and an exception path for the cases the model should not decide alone.
- Retrieval over your own content
- Answering from your documents with citations back to the source, so an answer can be checked rather than trusted.
- Evaluation harnesses
- A test set built from your real cases, run on every prompt or model change, so a change that quietly makes things worse is caught before it ships.
- Human-in-the-loop review
- Queues, approvals and audit trails, because the interesting part of an agent is what happens when it is unsure.
Why most agent projects stall after the demo
None of these is a model problem. Each one is a decision nobody made before the building started.
- The demo runs on the ten clean examples someone picked. Production runs on the messy ones nobody looked at.
- There is no evaluation set, so nobody can tell whether last week’s prompt change helped or hurt.
- The unsure cases have nowhere to go, so the agent either guesses confidently or blocks the whole queue.
- It is built as a chatbot bolted beside the workflow instead of a step inside it, so nobody’s day actually changes.
Four commitments you can check, rather than four values
Each one shows up in something you receive or something written down, not in how we describe ourselves.
Scoped to one workflow
We would rather ship one agent that genuinely removes a task than a platform that half-removes six.
Evaluation before expansion
A test set from your real cases exists before we widen scope, so quality is measured rather than asserted.
Exceptions are designed, not discovered
We decide up front what the agent refuses to do and where that work goes instead.
You get the prompts and the harness
The evaluation set, the prompts and the orchestration code are yours, in your repo, from the first week.
The builds behind this line — and how far each one opens
4 builds, delivered for clients under NDA. What you can open is our own reference build of each, with sample data in place of the client’s: 2 run end to end, 2 are navigable screen sets with no live backend.
Four steps, and you are running software by the third
Every step ends in something you can look at rather than a status update.
- 01
Pick the workflow
One job, one queue, one measurable outcome. We push back on scope that spans three teams.
- 02
Build the evaluation set
Twenty to fifty of your real cases, labelled, including the ones that go wrong. This is the part most projects skip.
- 03
Build and measure
The agent, the review queue and the harness together. Every change is scored against the set.
- 04
Ship behind a human
It runs alongside the current process with a person approving output, and the approval rate decides when it runs alone.
The same four terms hold whatever we build
Scope in writing, code in your repository as it is written, support in business hours IST Monday to Friday, and the builder on the call.
- Scope agreed in writing before we start and a written note the moment something we find changes it.
- Code in your repository as we write it so there is no handover event at the end — you already had everything.
- Support in business hours, Monday to Friday IST with the response window for anything critical agreed in writing. We do not advertise a blanket SLA a team this size could not hold.
- You talk to the person building it with no account manager between the question and the answer.
The five questions that actually decide it
Asked on almost every call, answered plainly. Where the honest answer is “sometimes not”, it says so.
Which models do you use?
Whichever fits the task and the deployment constraints, and we will tell you why. Some work runs on hosted frontier models, some on smaller open models where data cannot leave the client’s environment. The orchestration is written so the model is a swappable component rather than something the whole system is welded to.
What if our data cannot leave our environment?
Then we design for that from the start rather than retrofitting it. That usually means open-weight models running in your infrastructure, and it changes what is realistic on quality and latency. We will be straight with you about that tradeoff before you commit.
How do you stop the agent from making things up?
Three things, none of which is a prompt trick: answers cite the source they came from so a person can check them, the agent has an explicit refusal path for cases it should not decide, and every change is scored against a fixed evaluation set built from your real cases.
Do you use AI to write the code as well?
Yes, and it is reviewed by the person shipping it. We treat model-assisted code the same way we treat any code: it passes review, it typechecks, and someone senior understands every part of it before it goes near production.
What does a first engagement usually look like?
One workflow, scoped and quoted before we start, with the evaluation set as an early deliverable so you can see quality being measured rather than claimed. We agree the scope in writing; we do not bill open-ended hours.
Tell us what you’re building.
Thirty minutes on the problem and what is actually hard about it, then a written note back. No obligation either way.
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