AI transformation should start with the workflow.

The technology changes quickly. The discipline is more durable: understand the work, remove waste, assign responsibility clearly and build around real human decisions.

Our method

Seven steps from friction to production.

Open each step to see how workflow decisions move into architecture, implementation, governance and ongoing improvement.

01Find frictionSee where the work breaks down.

We follow work across people, documents, systems, reviews and handoffs to find what is repetitive, slow, expensive, inconsistent or dependent on manual fixes.

02DefineDecide what the workflow should do.

We remove unnecessary steps, then determine what AI should automate, augment, retrieve or monitor and which decisions people must continue to own.

03Map dataGive the workflow the right information.

We identify the minimum sources, classifications, permissions and context the workflow needs without connecting unrelated repositories simply because we can.

04ArchitectChoose the safest practical architecture.

We prefer approved client technology where practical, then define integrations, providers, identity, access, retention and data movement for any hybrid or purpose-built elements.

05Build & testCreate and evaluate the workflow.

We prototype, implement integrations, evaluate realistic and difficult cases, and make human review and exception paths visible in the experience.

06GovernSet the operating boundaries.

We define permissions, logs, responsibilities, escalation, review points and acceptable-use boundaries with the client stakeholders responsible for them.

07ImproveDeploy, learn and refine.

We train users, measure cycle time, quality, adoption and exceptions, and adjust the workflow as the work, technology or risk changes.

The Human / AI / System modelA way to decide which tasks people own, where AI can help and what rules-based software can handle automatically.

Put every task on the right layer.

Drag a task into a layer, or select it and then choose a layer. The model applies to evidence-backed medical content development, but the governing principle stays the same.

0 of 9 tasks assigned

Our design principles

Practical rules for responsible change.

01

Remove before you automate.

We remove unnecessary steps before considering how to accelerate the rest.

02

Protect human accountability.

AI can assist the work while people retain responsibility for scientific, strategic and regulatory decisions.

03

Design for review.

Clear links to evidence, unusual cases and approval routes belong in the workflow from the beginning.

04

Build for the real environment.

We look first at approved systems, permissions, information boundaries, habits and handoffs.

05

Measure useful capacity.

We measure whether the workflow becomes faster, clearer, safer and easier to operate.

Regulated work

Build safeguards into the workflow from the start.

See our operating standards See our security approach

Clear human-review requirements

Evidence linked clearly to its sources

Defined escalation and exception paths

Appropriate access and data boundaries

Quality evaluation before scale

Documented ownership and accountability

Prototype to production

A prototype isn't the finish line.

Demonstrating that AI can perform a task is only the beginning. Production systems also need permissions, integrations, reliability, testing, security, monitoring, user experience, governance and a clear operating model.

Kepler26 can stay with the workflow through that transition.

  1. 01Diagnose
  2. 02Design
  3. 03Prototype
  4. 04Validate
  5. 05Engineer
  6. 06Secure
  7. 07Integrate
  8. 08Deploy
  9. 09Optimize

Ready for the environment

Architecture, identity, data boundaries, integrations and infrastructure fit approved enterprise systems.

Ready for people

Human review, exception paths, usability, training and ownership are clear before release.

Ready to operate

Testing, deployment, logging, monitoring, documentation and ongoing improvement are planned as part of the system.

Plan a production build

Better work, by design

Start with the workflow that wastes the most attention.

We will help you make the friction visible and decide what a better operating model should be.

Find the friction