Clinical and operational data
Connect AI features to EHR, scheduling, billing and reporting data through scoped, auditable access.
For healthcare companies
AI inside clinical and revenue-cycle workflows.
Agents and AI features that work inside clinical, scheduling and revenue-cycle workflows, with the permissions, audit trails and integrations a regulated product needs.
What we solve
Where AI meets clinical and financial workflows.
Actions with consequences
Approval steps, role-based permissions and full traceability around anything an agent can change.
Ready for security review
The evaluations, access model and records your security and compliance reviewers ask for, delivered with the software.
Proof
Work we have already done.
We built the core of an EHR and revenue-cycle platform, carried it through acquisition by an a16z-backed company, and delivered the enterprise data layer behind more than 100 reports used by hospitals and healthcare organizations.
Healthcare / selected work
Technical leadership through acquisition
- The problem
- A regulated EHR and revenue-cycle platform connecting clinical workflows, revenue operations and patient access.
- What we built
- The core EHR/RCM system, with HL7 orders and labs, telehealth and a patient portal, then a Microsoft Fabric data layer after the acquisition.
- What it made possible
- A platform the acquiring a16z-backed company chose as its technology foundation, with 100+ reports in use by hospitals and healthcare organizations.
- CTO
- platform leadership through acquisition
CTO leadership, TeqEngine software development and technical diligence through acquisition.
Read case studyContinuity through a technical transition
- Understand the estateArchitecture, dependencies and technical risks
- Make the decisionDiligence findings · leadership communication
- Carry the platform forwardSystem and data delivery after acquisition
- Maintain ownershipOperational context · decisions · handoff
How we engage
A forward-deployed team, on terms you can plan around.
Build the feature with a forward-deployed team, keep it running on a monthly retainer, or start with a review of what you have.
Forward-deployed build
Initial three-month engagement
A forward-deployed team embeds with yours and builds the agent platform, AI product or the systems behind it. Scope, milestones and acceptance criteria are agreed before work starts.
You get: Working software, the harness your engineers keep using with coding agents, evaluation and release gates, runbooks, and a handoff your team runs with.
Explore this optionForward-deployed operations
Monthly retainer
After launch, we stay on to run the evaluations, keep the harness current as models change, respond to incidents and ship improvements.
You get: Evaluation and cost reporting, harness upkeep, incident response, ongoing improvements and a leadership readout.
How the retainer worksArchitecture & Harness Review
Two to three weeks
An independent read of your AI system or your team's agentic engineering workflow, ending in a prioritized plan.
You get: System or workflow map, risk register, evaluation and release plan, harness gap list, and a decision brief with the next scope.
Explore this optionGo deeper
Read how we approach it.
Start a conversation
What are you building—or deciding?
Tell us where you are. We reply within one business day, and the first conversation ends with a recommended next step.