Human-in-the-loop Workflows
AI does the heavy lifting and your people make every final call. We build the review steps, approval queues and sign-off logs that keep a named person in charge of what goes out.
How we build a review workflow
Our team starts by sitting with your reviewers. AI coding agents build the queues and checks, and a senior engineer reviews every component. The review path itself comes from the people who sign.
- We map who signs whatWith your medical, legal and regulatory reviewers, we write down every step a piece takes and who has to approve it.
- We start with the slowest stepThis is usually the wait between review rounds. We measure how long it takes today, so you can see the change.
- Checks run before a person readsEach draft is checked against your rules and references first, and anything the model isn't sure of is flagged for the reviewer.
- Reviewers try it on live workYour reviewers use it on real pieces from the first weeks, and what they change goes into the next week's build.
Review takes longer than the draft
Teams can draft content with AI now. The slow part is still sign-off. In pharma medical affairs and medical communications, every piece goes through medical, legal and regulatory review, and a reviewer checks each claim against its reference. At a CME provider, an accredited provider or a clinician has to put their name on every course.
Meanwhile the draft waits between documents, review portals and email while people chase each other, and outside agencies bill for every round of revisions.
In licensed work a person has to sign. We make the signing fast and easy to audit, and we never take it over.
Your reviewers still sign every piece.
- Every approval logged with a name
- Drafts checked before review
What we build
We build the review into the tools your team already uses, so nobody has to learn a new portal to approve something.
- A check before review. Each draft is checked against your own rules and references before a person sees it, with every claim linked to its source. The reviewer starts from a clean draft.
- Review queues with named reviewers. A draft waits with its sources beside it, assigned to one person, with a due date everyone can see.
- Approval logs. Who approved what, when, and which version they saw, kept in your own accounts for audits.
- Exceptions sent to people. When a model isn't sure about a field on a form or a claim in a draft, it stops and sends the item to a person to check.
The drafting and checking come from our AI agents and automation work. This page is about the part where your people decide.
Questions we hear
Our review process won't let an outside team near our content.
That review stays. We build it into your workflow with review queues, approval logs and named reviewers. Your reviewers still sign off on every piece, and they spend less time chasing it.
Does the AI approve anything?
No. It drafts, checks and sorts. A named person on your team approves every piece that goes out, and the log shows who it was.
What about clinical risk?
We don't build tools that make clinical or diagnostic claims, and we don't carry that risk. The person signs, and we make the sign-off fast and easy to audit.
- AI Agents & AutomationAgents and automations that handle repetitive work inside the tools you already use.
- 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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