We build AI agents, tool integrations and data pipelines that take the repetitive middle out of a process, with a person in the right place and a log of every step.
Each engagement is scoped to a fixed outcome at a fixed price, so you know what you are getting before work begins.
Language-model steps embedded in real processes: triage, drafting, extraction, classification and review. Every step is logged and every output is checkable.
We connect models to your systems through Model Context Protocol servers: scoped, permissioned tools for your database, files, calendar and ticketing, instead of copy-and-paste.
Collect, clean, join and monitor data from public sources, files and internal systems. Scheduled, versioned and tested, so the numbers you act on are true.
We map how work really moves, remove hand-offs that do not need a person, and turn regulatory requirements into repeatable routines with the records to prove them.
Three views of the same system: the tools a model is allowed to use, the workflow it runs inside, and the run history you can audit. These are illustrative mocks with example data.
search_records(query, limit)read · no approvalget_record(id)read · no approvalpropose_update(id, fields)write · needs approval| Time | Workflow | Input | Result | Status |
|---|---|---|---|---|
| 09:14 | inbox-triage | support request | draft ready for review | awaiting approval |
| 09:02 | weekly-data-refresh | 3 source files | 212 rows updated · 4 flagged | complete |
| 08:47 | inbox-triage | invoice question | sent after approval | complete |
| 08:30 | change-monitor | public filings feed | no changes | complete |
| 08:12 | doc-extraction | scanned form | low confidence → person | routed |
Most automation projects fail from vague scope and invisible failure modes, not weak models. We design against both.
We work backward from what a person needs decided, sent or filed, then automate only what leads there.
New automations run beside the manual process first. We compare results before anything is switched over.
Anything irreversible, such as sending, paying or publishing, waits for explicit approval.
Models get narrow, named tools with read-only defaults, never open-ended access to your systems.
Inputs, decisions and outputs are recorded so any result can be traced and any error reproduced.
Plain scheduled jobs, open standards and no lock-in. Nothing you cannot hand to someone else.
A two-line description is enough. We reply with whether it is a good fit and what a first step would cost.
brian.keegan@keeganlabs.net