Playbooks are reusable templates, workflows, tools, and best practices drawn from real healthcare transformations. We work alongside your team to identify high-value opportunities, redesign the work around people and agents, deploy solutions quickly, and build the capability to improve and scale them over time.
Four elements · One plan
One room. You + partner + GraymatterLab, working it together.
Every Playbook ties AI to a business outcome instead of a technology experiment — and it always includes these four elements, with the Agent Catalog it draws from living in the Agent Platform.
Sets the direction
Defines where to focus, why it matters, and how to sequence the work — workflow map, value case, target-state design, metrics, operating rhythm, and governance model. Aligns the organization before the first agent is built.
Supplies the building blocks
The reusable agent templates the Playbook draws from — proven healthcare building blocks we tailor to each client. The modular base of the final solution, never a blank sheet.
Builds the outcome
Focused two-to-six-week sprints with clear scope, named outputs, and exit gates. Each delivers a measurable outcome — prototype, workflow redesign, agent design, pilot launch.
Makes it stick
Builds the team's capability to run, govern, and improve the new way of working — role-based learning paths, workshops, knowledge transfer, and operating-cadence coaching.
Direction, delivery, and durability — in one packaged system. Combined, the four elements tie AI to a business outcome and leave the team able to run it.
The same repeatable lifecycle every engagement follows — time-boxed, with named deliverables and exit gates, so speed never means skipping the work.
Map the workflow, identify where value is created and lost, define the outcomes that matter, and establish the governance model.
Reimagine the process; define the agents, where human gates are needed, and the metrics to prove it works.
Build agents from the Catalog, integrate them into the workflow, and run a time-boxed pilot against the metrics that matter — with human gates in place.
Transfer capability through Embedded Learning, set the operating cadence, and scale the workflow across teams once the outcome is proven.
Each Playbook is targeted at a specific domain, workflow, or buyer pain — and the metric it's designed to move — and ships with the agents, Spaces, and training to get there.
Agents assemble the packet, check requirements, draft submissions, and monitor status — humans review and approve before anything reaches the payer.
Designed to move · Cycle time, approval rate, rework
Agents coordinate eligibility, benefits, prior-auth needs, and out-of-pocket estimates so teams see what's ready and what needs human judgment.
Designed to move · Clearance time, first-pass rate
Agents keep the case moving from referral to scheduled therapy — benefits, documentation, site-of-care rules — while humans manage exceptions.
Designed to move · Time-to-therapy, referral-to-schedule
Agents check documentation completeness, run same-or-similar logic, identify gaps, draft requests, and preserve audit evidence.
Designed to move · Denial rate, completion time
Agents flag missing evidence, policy mismatches, and workflow exceptions before submission — when correction is fast and cheap, not after.
Designed to move · Preventable denials, days in A/R
Agents coordinate payer rules, benefits, prior auth, assistance, and hub follow-up so eligible patients start high-cost therapy faster.
Designed to move · Time-to-therapy, abandonment
Forward-deployed engineers can build impressive agents. Strategy decks can make a compelling case. Generic copilots can improve individual productivity. None of them reliably turn AI into durable operating improvement.
A Playbook is designed from the start for the team that has to run the workflow after the pilot ends. Embedded Learning builds the capability to govern agents and improve the process. The Roadmap governs what gets built. The Catalog keeps every deployment grounded in what works.
Done means the team can run it. The work isn't done when an agent works in a demo — it's done when the team can operate the workflow, govern the agents, and improve the process on their own.
We'll help you identify where AI can make a measurable difference, map the workflow, and put a working agent in the hands of your team.
From AI opportunity to operating value — every time, with a repeatable, domain-built method.
