AI Agents & Automation
We build AI agents and automations that work where your team already does: in your chat tool, in your dashboards and in the workflows you run every day. They answer from your own data, take on the repetitive steps, and run under the controls a regulated business needs.
How we build an agent
Senior engineers build the agent with your team, one job at a time. Before it reaches your people, the agent is tested against the questions and documents they handle every day.
- We pick one job and time itOne repetitive job, like the product question two specialists answer all day or the forms someone reads before anyone can act, with how long it takes today written down.
- We collect your real casesWe gather a few hundred real questions or documents, with the answers your experts expect, and score every version of the agent against them before it ships.
- It works where your team worksThe agent runs in the chat tool, dashboard or workflow tool you already use, with access to only the data it needs. Your team signs in the way they already do.
- A person reviews what the agent isn't sure ofWhen the agent's confidence falls below a line you set, the item goes to a named person, and the logs show what the agent did and why.
Where the hours go
Most of this work starts with a task that's repetitive and slow, done by people whose time is better spent elsewhere.
- The same questions, every day. At a medtech or diagnostics company, field reps call the same two product specialists for answers that are already in the manuals. At a society, years of expertise sit in session recordings that members can't search.
- Paperwork someone has to retype. At a specialty practice, a referral arrives as a PDF, and someone types its details into an insurance portal and again into the practice system. Across every site in a healthcare PE portfolio, that adds up to hours a day.
- Handoffs that stall. A request moves between teams by email, waits in someone's inbox, and gets copied into the CRM by hand. At a pharma company, what medical science liaisons hear from doctors rarely reaches strategy at all.
Every answer shows where it came from.
- In your chat tool and dashboards
- Runs in your own cloud account
What we build
Everything works from your own content and data, runs in your own accounts, and is measured against a result you care about, like hours saved or questions answered without waiting on an expert.
- Agents in the tools you already use. Ask a question in your chat tool or on a dashboard and get an answer from your own data in the same place, with its source linked.
- Assistants on your own material. Questions about your products, courses and policies, answered from your documents with a link to the page, or the minute of the recording, each answer came from. Anything that needs an expert still goes to one.
- Document flows. Faxes, PDFs, forms and portal data read once and written everywhere they're needed. When a field is unclear, a person checks it.
- Operations handoffs. Requests sorted, routed and tracked between teams and systems, so nothing waits in an inbox or gets typed in twice.
When a person has to approve what the agent produces, we build that step in with human-in-the-loop workflows.
An agent in Dermsquared's Slack
Dermsquared's team wanted quick answers about their own business without waiting for a report. We built an AI agent that lives in their Slack and works from their data in Snowflake and their Tableau dashboards, so a question asked in a channel gets an answer in the same thread.
Why most AI pilots stall
Most pilots work in the demo and stall after that. Answers don't show which document they came from, so staff can't check them. When the AI provider updates its model, nobody retests the answers. And once the people who built the pilot move on, nobody inside the company owns it.
We solve each of those issues as part of the build. Before we write a prompt, we collect the real questions your staff get, with the answers your experts expect, and score every release against them. A named person on your side owns the agent.
Scattered data stalls pilots too. When your documents and numbers live in too many systems, we start with our data science work.
Questions we hear
Do our staff need a new tool?
No. The agent answers in your chat tool, in your dashboards or in the workflow tool your team already uses, under the logins they already have.
What if an answer is wrong?
Every answer shows its source, so the reader can check it. Where a wrong answer would matter, a named person reviews it before anyone acts on it. We don't build tools that make clinical or diagnostic claims.
Where does our data go?
It stays in your accounts. The agents and the models run in your own cloud account, under your agreements.
What happens when a better model comes out?
We run your questions against it, and you decide whether to switch with the scores in front of you. The prompts, tests and logs are yours, so changing providers doesn't mean starting over.
We tried an AI pilot and it went nowhere.
Most pilots stall because nobody owns the result. We agree with you on how we'll measure it before we build, ship inside your tools, and show you the before-and-after numbers within four weeks.
- Human-in-the-loop WorkflowsReview steps and approvals so a named person signs off on what AI produces.
- Data SciencePipelines, models and reports that turn scattered data into decisions.
- App Design & DevelopmentApps, portals and internal tools your teams use every day, owned by you.
- Discovery & StrategyWe hear from your people at scale, then build a roadmap we help you ship.
- AI Team UpskillingA clear AI plan plus hands-on training for your whole team.
We'd love to chat.
Bring what's slow, rented or stuck. You'll leave with what we'd build first and how long it takes.
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