AI prepares the routine work. Your team handles the exceptions.

For operations teams in Australia, the US and the Philippines

We build document workflows, operational assistants and data products around a job your team does every week. Each system is tested on your own examples, and anything that needs approval waits for a person.

What we build

Document and approval workflows

Read incoming documents, pull out the details your team needs and flag anything missing or conflicting for review.

Example Claims intake. Each submission is checked against the required documents. Complete files are queued for approval, and incomplete ones go to a reviewer with the missing items listed.

Operational assistants

Answer questions from your own policies and records, draft briefs and follow-ups, and show the source behind each answer. Anything that needs approval waits for your team.

Example Lead follow-up. For each new inquiry, the assistant gathers what your team already knows, drafts a first reply and queues it for a person to review and send.

Data and intelligence products

Turn data you are allowed to use into reports, dashboards or APIs built around a specific decision.

Example Operations reporting. Exported records become a weekly view of backlog, exceptions and turnaround by team, with a note on what the data does not cover.

These examples show the kind of system we build. They are not client case studies.

Explore what we build.

What happens to an exception

In this example, the system organizes details from documents the team has permission to use. A required detail is missing, so it sends the record to a reviewer.

  1. Documents received

    Documents the team has permission to use

  2. Details organized

    A record is prepared for review

  3. Missing detail flagged

    A reviewer must complete or reject the record.

  4. Reviewer approves

    An approved record can move to the next step.

Conceptual workflow — an illustration of a proposed review process. It is not a client deployment.

On a project, we agree who may use the documents, who reviews flagged records and how mistakes get corrected. Access, approvals and testing

Start with one workflow

We plan the first phase to end with a working system tested on your own examples, so you can judge it on real cases before deciding on wider use.

  1. Understand the taskYour team shows us how the work happens today and where it gets stuck.
  2. Agree what to testWe pick real examples, including the difficult ones, and agree who checks the results.
  3. Build and reviewWe build against those examples. Your reviewers see the sources, the errors and anything missing.
  4. Decide the next stepYou decide what goes into daily use, what needs more work and what stays manual.
See how we work

Projects and partnerships

Working across time zones

Led by Bernd-Michael Rennebeck

Bernd is a software engineer and data scientist who has built technology products across three continents over two decades. Outside SageDynamics, he serves as CTO across several VS12 ventures, including Agent Image, a real estate web platform that has built more than 27,000 agent websites in North America. He was previously Chief Data Scientist at mClinica, now SwipeRx.

Tell us about one task your team repeats

Describe how it works today and where it gets stuck. A short, non-confidential description is enough to start.

Discuss a project

Exploring a data product or venture? Start with partnerships.