AI agent development for the work nobody wants to do.
Your people copy data between tabs, chase missing fields and forward the same email ten times a day. We build AI agents that do that part, inside the tools you already use, and hand the judgment calls back to a human.
The problem you're living with.
You've probably felt this. The work isn't hard, it's just endless. And every person you hire to do it ends up doing more of it.
- Someone opens a PDF, reads it, and types the same six fields into two different systems.
- Requests arrive by email, get forwarded around, and nobody is sure who owns them.
- You tried a rules-based bot once. It broke the first time a supplier changed their invoice layout.
- Your best people spend their mornings on data entry instead of the customers who need them.
The real cost isn't the hours. It's the errors that slip through when people are tired, the delay before anyone notices, and the good hires who leave because the job turned into copy-paste.
What we build.
Agents that read, check and route the repetitive work, with your team approving what matters.
Document agents
Read invoices, referrals, claims, contracts and forms. Pull out the fields, check them against your rules, and flag anything that looks off.
Inbox and request triage
Classify incoming email and tickets, draft a reply or a record, and route each one to the right person with the context attached.
Multi-step workflow agents
Agents that look something up, update your CRM or ERP, notify the team and log what they did, step by step, within limits you set.
Human approval steps
A simple review queue where your team approves, edits or rejects what the agent prepared. Nothing risky happens without a person saying yes.
Integrations and MCP servers
Connections to the systems you already run, through their APIs or a Model Context Protocol server, so agents use real data instead of screenshots.
Evaluation and monitoring
A test set drawn from your own examples, accuracy tracked over time, and alerts when the agent starts getting less sure of itself.
- Invoice receivedQueued for processing
- Document read14 fields extracted
- Checked against POAmounts and line items match
- Anomaly scanNo anomalies found
- Human approvalApproved by Finance
- Posted to ERPSynced · audit log updated
- Vendor
- Northwind Supplies
- Invoice
- INV-0142
- PO match
- PO-7781
- Due
- Net 30
How it works, step by step.
No big bang. Small, measured steps with a working demo every week.
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Map one process
We sit with the people who do the work and write down every step, every exception and every system they touch. We pick one process, not ten.
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Measure the baseline
How long it takes today, how often it goes wrong, how much it costs. Without this number nobody can tell you whether the agent helped.
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Build a pilot in weeks
A working agent on real (or realistic) examples, with the approval step in place from day one. You see it running in the first weekly demo.
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Run it next to your team
The agent drafts, your people check. We compare its output with theirs until the numbers say it's ready for more responsibility.
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Scale what works
Once one process is proven, we widen it or move to the next one, reusing the integrations, the evaluation set and the monitoring.
What changes for your team.
What we aim for, measured against how things run today. We don't promise numbers before we've seen your baseline.
Mornings go back to people
The team starts with a queue of prepared work to approve, not a pile of PDFs to type up.
Fewer silent errors
The agent checks every field against your rules every time, and it flags doubt instead of guessing.
A clear audit trail
Every action the agent took, the data it used and who approved it is logged, which your compliance people will like.
Growth without the same hiring curve
More volume doesn't automatically mean more people doing data entry.
What we'd tell a friend about ai agents & automation
Don't start with the most impressive use case. Start with the most boring, highest-volume one you have. That's where an agent pays for itself, and it's where you'll learn how your team feels about working alongside one.
What we won't do
- Let an agent make an irreversible decision, like paying, deleting or diagnosing, without a human approval step.
- Promise a percentage of time saved before we've measured how the process runs today.
- Hide the logic in a black box you can't inspect or take with you.
AI Agents & Automation: your questions.
How are AI agents different from RPA?
RPA follows a fixed script and breaks when the screen or the input changes. An AI agent can read messy, unstructured input like emails and PDFs, decide the next step within rules you set, and use your tools through their APIs. We often keep simple RPA where it already works and add agents for the parts that need judgment.
Will the agent make mistakes?
Yes, sometimes, just like people do. That's why we measure it against your own examples before it goes live, keep a person approving the steps that matter, and monitor accuracy after launch. The goal is fewer errors than today, caught earlier.
Which AI models do you use?
Whatever fits the job, your budget and your data rules. That can be OpenAI, Anthropic Claude, Google Gemini or an open-source model you host yourself. We design the system so you can swap models later without rebuilding it.
Does our data get used to train someone else's model?
No. We use API and enterprise terms that exclude your data from training, or self-hosted models when your rules require it. Where the data lives and who can see it is written down before we build anything.
How long does a first pilot take?
For one well-defined process, a working pilot usually takes a few weeks, not months. The exact number depends on how many systems it touches and how clean the data is, and we'll tell you honestly after the discovery call.
Want this for your team? Let's scope it together.
Tell us how the work runs today. We'll come back within one business day with an honest first take and a suggested first step.
- Weekly demos
- No lock-in
- You own the code
- 1 business day