AI-Enabled Go-to-Market Assessment.
A PE-backed clinical data intelligence platform with three decades in the provider market and about 4,000 clients wanted to sell its risk adjustment product to value-based care buyers. A Claude-assisted top-down and bottom-up market assessment narrowed the book to 52 named targets, and a two-person team ran what had historically taken six to eight.
Company Profile
A PE-backed clinical data intelligence platform approaching the end of the hold period, with heightened focus on growth. Thirty years selling into the provider market and about 4,000 active client relationships. A workflow-embedded risk adjustment product ready for the payer market. The commercial team had product-market fit on the provider side but had not run a payer go-to-market motion at scale.
The Challenge
The product was ready. The problem was that the payer market is wide, fragmented across lines of business, and the company had less experience selling into it than into providers, where the buying personas are different. The commercial team had no ranked target list for provider-sponsored health plans, payviders and payers, no tested value proposition, and limited payer experience. Leadership and the board wanted targeted outreach to start in the first quarter of 2025.
The client carried three differentiators into the engagement: data source modularity across EHRs and claims, 4,000+ provider relationships, and in-workflow integration of its risk adjustment product. The engagement translated that asset base into a payer value proposition and a motion the team had not run before.
The Engagement
Phase I (December 2024 to July 2025): assessment and build
A top-down read of the payer market and a bottom-up read of the existing client book ran in parallel. The top-down view filtered the market to the lines of business where workflow-embedded risk adjustment creates measurable value: Medicare Advantage first, select Medicaid managed care markets next. The bottom-up view matched the client book against public Medicare Advantage, ACO, Medicaid and ACA plan data and kept the clients that run risk-based lines of business with more than 1,000 members. Claude Sonnet did that segmentation inside Claude Cowork and Claude Code, with the Aequalis Payer Insights Platform as the data engine.
Around the target list: county-level risk adjustment use cases, 15 voice-of-customer interviews reached through the 3Pillars network (VP to board level), profiles of 10+ competitors, persona messaging, account-level revenue sizing, buyer maps for each priority organization, and two board updates. Claude also drafted the executive-facing materials, which let a two-person team run an engagement that had historically taken six to eight people.
The build stood up the two motions the client did not have: a systematized outreach engine for payer buyers (email sequences, call scripts, talk tracks, objection responses and one-pagers by line of business and buyer role; humans own every external touch, AI drafts behind them) and a Pilot Support Program with a defined sales-to-customer-success handoff, intake forms, training and success criteria worked out before the first pilot landed.
Phase II (July 2025 to January 2026) and follow-on to March 2026
A second phase ran on Claude Cowork beside the customer success team and was built in real time: client-ready materials, customized sales materials, automated KPI and metric reporting, customer-facing HTML dashboards for the client’s own customers, and training for end users and the sales team on what was built. It also produced a change management playbook and value metrics for the product at pilot sites. Follow-on work on Claude ran through March 2026.
Results
Pilot-site value, Q2 2025: at one pilot site, 22 of about 600 providers accepted 62 suggestions across 48 problem lists, an estimated 0.47 RAF per list with accepted suggestions. After training, 6 providers reviewed more than 25% of suggestions, up from 3.
Functional Areas Touched
The engagement created downstream impact across the commercial side of the business. The map below shows a typical clinical-data-platform operating structure. Highlighted nodes are where this engagement created downstream impact.