How a Leading Healthcare Services Organization Unlocked $7–10M in Digital Engagement Revenue — Without Moving a Single Record Off-Premises
Frontier Foundry — AI-Native Digital Conversion Intelligence for Regulated Healthcare.
$7–10M
Projected revenue from digital engagement channels
10 Days
From data access to first channel recommendations
1.7M+
Member records securely analyzed on-premises
How Legacy Systems Limited Member Engagement
Our client, a leading U.S. healthcare services organization, excelled in healthcare communications management. Having partnered with a top-tier national health insurer, they managed member communications, benefit activation, and portal onboarding for a population of multiple millions of insured consumers.
But outdated systems limited their impact.
The client's outreach model had been built when print was the primary channel. Portal registration sat at roughly 2.5% of the eligible member base, and additional print volume was no longer producing proportional conversion. Every unregistered member represented a double cost: a recurring printing expense and a digital relationship that was never opened.
Three structural constraints were limiting progress:
Untargeted outreach at population scale
Campaigns ran against broad demographic slices rather than members statistically likely to respond — producing low conversion, unnecessary print volume, and avoidable call-center load.
Sparse source data with no behavioral signal
The starting dataset carried little more than address, ZIP code, and date of birth per member — not enough to predict who would engage or how.
Cloud AI was not an option
The client evaluated commercial AI services and disqualified them on data-security grounds. Sending PHI-adjacent member records to an external inference provider was incompatible with its regulatory posture and contractual obligations.
Challenge vs. Frontier Foundry Solution
The Frontier Foundry Approach
Frontier Foundry built a fully on-premises, member-level engagement intelligence system engineered for the privacy, auditability, and data-rights constraints that govern regulated healthcare.
The system is not a general-purpose model adapted for healthcare. It is a purpose-built pipeline — trained on the client's own cleansed and enriched member data, running entirely within the client's infrastructure, and producing analysis that is both explainable and reproducible.
Secure Data Foundation with Kundi
All ingestion, cleansing, enrichment, model training, and inference flow through Kundi, Frontier Foundry's proprietary secure data layer. Member data is encrypted at rest and in transit, stored exclusively on-premises, and used exclusively to train models within the client's own environment. No member record touches an external inference server — ever. This is not a policy assurance; it is an architectural one.
A Custom, Client-Trained LLM Powered by Limni
Built on Limni, Frontier Foundry's proprietary LLM infrastructure, the recommendation engine was trained on the client's own data and deployed as a self-hosted model. The Limni-powered model reasons over actual cluster definitions and returns specific channel recommendations with example outreach language — in plain English the client's operational teams can use directly.
Enriched, Significance-Tested Member Data
The starting 1.7 million records were cleansed, standardized, and enriched with census-derived attributes covering education, household size, age, and income. Logistic regression then separated signal from noise — identifying the variables with defensible predictive power. Only after the data foundation was validated did the system move to clustering, producing five distinct member segments with clear engagement profiles mapped to outreach channels.
Platform Capabilities
Member Segmentation Engine
K-means clustering on cleansed, census-enriched records produces five distinct engagement segments — each with demographic and behavioral signatures that drive targeted outreach decisions.
Limni-Powered Recommendation LLM
A self-hosted LLM built on Limni, trained on the client's own data, generates channel selection and example outreach copy for each cluster in plain English.
Significance-Tested Feature Selection
Logistic regression identifies the variables that genuinely predict portal registration — separating real signal from noise and providing defensible targeting logic.
Synthetic Data Generator
Produces statistically identical, PHI-free datasets from source data, allowing analysts to test workflows and models without data-rights friction.
Channel Prioritization Framework
Each cluster is mapped to its highest-yield outreach channel — voice, SMS, email, or print — enabling spend shifts from low-conversion paper campaigns to higher-return digital modes.
On-Premises Training & Inference
Every stage of the pipeline runs inside the client's own infrastructure through Kundi — no external API calls or data egress at any point.
From Paper to Performance in 10 Days
Within ten days of first data access, the system was producing actionable channel-prioritization analysis. Full operational deployment followed at approximately six weeks. The client moved from an undifferentiated, paper-heavy outreach model to a segmented, data-driven one:
$7–10M
in projected digital engagement revenue — converting unregistered members through targeted digital channels at rates far exceeding the 2.5% baseline
5
actionable member segments with defined channel strategies — each with clear demographic and behavioral signatures mapped to voice, email, SMS, or print
100
targeted tests with no consistency errors across holdout and synthetic datasets — giving the business the defensibility required to operationalize
The system is live today. Beyond case-by-case channel prioritization, it continues to surface macro-level trends in digital engagement — allowing the client to reallocate spend from low-conversion paper outreach toward higher-yield digital channels the model now identifies per segment.
Why Healthcare Organizations Choose Frontier Foundry
Several commercial AI vendors could have built a clustering model. None could build it inside the client's infrastructure, under the client's data-rights constraints, and against the compliance posture required by the underlying insurer.
Frontier Foundry is the only AI firm built from the ground up to serve regulated industries — by a team that has operated inside them. Our founder served as Chief Innovation Officer at the FDIC, reporting directly to the Chairman. Our team has testified before regulators and designed compliance infrastructure for top-tier financial institutions.
Every engagement is bespoke, confidential, and backed by Frontier Foundry's enterprise security and SOC 2 compliance framework.
Ready to Move Member Engagement From Paper to Performance?
Likelihood-to-Engage deployments are bespoke engagements. We begin with a confidential technical scoping call to map your current data estate, regulatory constraints, and member outreach stack — and to identify exactly where AI-driven segmentation will move the needle against your conversion and cost-to-serve targets.
Engagements are subject to availability. Frontier Foundry works with a limited number of new clients per quarter to ensure implementation quality.