- 01Discovery & Data Assessment
We start by understanding your workflows, systems, and data as they actually exist.
- 02Pilot & Validation
We build a focused pilot on real data, with human experts validating outputs against your actual outcomes.
- 03Deployment & Scaling
Once validated, we scale the solution across the relevant workflow or department, integrating with your existing systems and training your team on the new process..
- 04Continuous Human-in-the-Loop Oversight
AI models drift, payer rules change, and edge cases emerge.
We start by understanding your workflows, systems, and data as they actually exist — not as a generic template assumes them to be. This stage identifies the highest-value starting point and any data or integration gaps that need addressing first.
This typically involves reviewing sample documents and claims, understanding your current systems and how they connect to each other, and talking directly with the staff who handle the workflow today — because they usually know exactly where the process breaks down, even if that knowledge has never been written down anywhere.
We also review whatever reporting you already have — denial reports, AR aging, current turnaround times — to establish a clear, agreed-upon baseline before any pilot begins. Without that baseline, it’s impossible to demonstrate improvement with confidence later.
We build a focused pilot on real data, with human experts validating outputs against your actual outcomes. This proves accuracy and value before any broader rollout — no black-box leaps of faith, and no claims made before they’re earned.
The pilot is deliberately scoped narrow — a single workflow, a single payer, or a limited volume of claims — so we can validate accuracy quickly and cheaply before asking you to commit to a larger rollout. If the pilot doesn’t perform well enough, we’d rather find that out in week three than after a full deployment.
Throughout the pilot, we share results transparently, including where the model underperforms, rather than only highlighting the wins. If a particular document type or payer proves harder to automate than expected, you’ll know that before any broader rollout, not after.
Once validated, we scale the solution across the relevant workflow or department, integrating with your existing systems and training your team on the new process.
Scaling happens incrementally rather than all at once — expanding to additional payers, additional volume, or additional workflows only as each expansion proves out, rather than assuming success on a small pilot guarantees success at full scale.
We also build in a rollback plan for every deployment. If a specific expansion doesn’t perform as expected, we can pull back to the previous validated scope quickly rather than pushing forward and hoping performance improves on its own.
AI models drift, payer rules change, and edge cases emerge. We maintain ongoing human review and model monitoring so performance holds steady long after go-live.
This is the stage most AI vendors skip. A model that performed well at launch can quietly degrade as payer rules change or your document mix shifts, and without ongoing monitoring, nobody notices until accuracy has already dropped. We build monitoring and periodic human review into every engagement so degradation gets caught and corrected early, not months later.
Ongoing oversight also includes a regular reporting cadence — typically monthly — so your team always has current visibility into pilot performance, volume processed, and any emerging edge cases, rather than assuming the system that worked at launch is still working the same way six months later.
A member of our team will reach out within one business day to schedule a short discovery call — no obligation.