INNOVACCER GRAVITYRevolutionizing Payer Data Integration with an AI-Driven Agent
Revolutionizing Payer Data Integration with an AI-Driven Agent
Published on June 20, 2025  |  4 min read
Ashish Singh - President - Platform Management,
Ashish Mishra - Associate Director - Platform Engineering,
Aseem Raghav - Senior Data Scientist - Analytics
Mridul Saran - Senior Product Manager - Platform Management
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Revolutionizing Payer Data Integration with an AI-Driven Agent

Payer Data's Hidden Complexity

More than 1,000 payers make up the US healthcare system, and each one provides a variety of plans and data formats to meet the needs of the market. For healthcare organizations, integrating this data has become a difficult and time-consuming nightmare. Each payer provides their own formats, each with varying schemas, missing documentation, and intricate relationships across entities like claims, members, and providers.

This structural complexity creates massive inefficiencies and delays. Data teams spend weeks reverse-engineering schemas, manually validating assumptions, and writing custom pipelines for every single integration. These delays cascade through everything: quality reporting, risk stratification, operational insights, and business outcomes all get pushed back while teams wrestle with basic data formatting.

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Solving the Integration Challenge with Purpose-Built AI

Gravity by Innovaccer tackles the payer integration challenge head-on. Drawing from deep healthcare expertise and hundreds of real-world payer implementations, we built a purpose-driven AI engine that transforms fragmented, undocumented payer data into clean, connected, analytics-ready datasets.

The approach eliminates dependency on manual effort or static rules. The system intelligently ingests, models, maps, transforms, and validates data, cutting integration timelines from months to days.

The AI-powered Data Integration Agent works through four integrated stages:

Intelligent Ingestion and Profiling: Every integration begins with the ingestion engine profiling incoming data. It analyzes schema structure, field-level distributions, sparsity, and anomalies, offering early visibility into data quality and completeness before deeper modeling begins. This proactive step eliminates blind spots that typically surface weeks into integration.

ER Mapping and Data Modeling: After profiling, the system structures data fields using statistical analysis, pattern recognition, healthcare knowledge graphs, custom-trained ML models, hierarchical clustering, and reasoning models to infer how files relate. It maps relationships between members, claims, encounters, and more, even without documentation or keys. The AI-driven modeling transforms raw files into connected datasets with accurate joins and entity relationships.

To explore how this foundational modeling works in detail, check out our blog: Solving the Hidden Crisis in Healthcare Data Integration

AI-Powered Mapping: The mapping layer is where integration often fails or drags on for weeks. The AI-powered mapping agent brings speed, intelligence, and adaptability into one seamless engine. At its core is an advanced auto-mapping system that doesn't just match field names but understands data, interprets unfamiliar schemas, infers mappings across payer variations, and dynamically adapts to new formats in real time. Key capabilities include leveraging prior integration intelligence for high-confidence mappings, applying small language models trained on payer data semantics with insights from 2,000+ integrations, and chaining prompts through RAG stores built for payer logic specificity.

AI-Driven Data Transformation: The system normalizes variations in dates, booleans and other categorical or coded fields (e.g. diagnosis codes), aligns timestamp formats and measurement units, and restructures nested or multi-column fields into analytics-friendly formats.

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The Agentic workflow in the Payer data ecosystem
The Agentic workflow in the Payer data ecosystem

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Data Observability 

The Data Integration Agent uses Data Pulse to ensure trust in data across layers of data integration journey. Data Pulse performs hundreds of automated tests in addition to mapping and transformation, checking for completeness, accuracy, and consistency in all fields. Anomalies are caught early by these checks, and only data that passes quality standards gets integrated.

For more on how Data Pulse drives data quality, see our blog: Data Pulse - The Neural Network of Healthcare Data Observability

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The Outcome

Days rather than months of payer data activation. Organizations are provided with clean, normalized data sets ready for quality metrics, reporting, insights, and downstream operational use cases without extra engineering time.

The Data Integration Agent is not only quicker, it's inherently smarter. By putting agentic workflows at each step and keeping humans in the loop, the platform ensures accuracy at scale. Every integration makes the system more clever, continuously learning to eliminate mistakes, enhance confidence scores, and speed up subsequent integrations.

Payer data integration is no longer something that takes months of specialized engineering. AI does it automatically.

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A decade ago, a few of us asked a simple question:
What if healthcare data actually worked for the people delivering care?

Not just for billing.

Not just for compliance.

But to reduce burnout. Improve outcomes.

And help doctors and nurses get back to what they do best, caring for people.

That question set off a journey. One filled with late nights, tough lessons, and moments of clarity that built the foundation of Innovaccer.

And now, the stakes have never been higher.

By 2030, the world could be short 18 million healthcare workers including 139,000 physicians in the U.S. alone. Demand is rising. Patients are older, sicker, and in need of more complex care. At the same time, hospitals are operating with margins below 1%. Over a quarter of rural hospitals are at risk of shutting their doors.

So yes, AI is on every boardroom agenda. In a recent survey, 97% of healthcare executives said they need to adopt AI urgently. But only 14% feel ready.

Because the hard truth is this: AI in healthcare is mostly stuck in pilot mode.
One AI tool for documentation. Another for prior auth. Maybe something in the call center. All running in silos. Different vendors. Different data. Different departments. And none of them talk to each other.

Meanwhile, the data is messy, fragmented, and incomplete. It makes it hard for any AI to do something useful. So even the best tech ends up as just another sidecar, disconnected from actual clinical or operational workflows.
That’s why we built Gravity.
Gravity is the healthcare intelligence platform built to close the gap between AI’s promise and healthcare’s reality. It’s not another point solution. It’s the infrastructure layer, the intelligence platform, that brings it all together.
Gravity, the healthcare intelligence platform
Gravity is the cloud-agnostic intelligence platform that’s purpose-built for healthcare with the goal to unify fragmented data, activate enterprise AI, and deliver real-world outcomes.

Think of it as your system’s AI operating layer.

  • Unified Data Fabric: Gravity stitches together clinical, financial, operational, HR, and supply chain data into one secure, FHIR-ready foundation. Whether it’s 70 different codes for diabetes or an HbA1c lab result, Gravity maps it all to a single signal that AI can actually use.
  • Prebuilt Healthcare Context: We’ve baked in the terminology, ontologies, and disease definitions so your teams don’t waste months reinventing what’s already known.
  • Self-Service Studios: Analysts, developers, and data scientists can build agents, train models, and launch workflows in days, not months. No AI PhDs required.
  • Enterprise-Grade Security: Gravity runs inside your own environment, with HIPAA, HITRUST, and SOC-2 baked in. No PHI leaves your control. Every output is traceable, explainable, and compliant by design.
Gravity isn’t just for one department. It powers use cases across the entire enterprise from reducing denials and optimizing bed capacity to planning staff schedules, forecasting supply chain needs, and improving patient experience
From pilots to platform
With Gravity, healthcare systems don’t have to choose between buying a dozen disconnected tools or building a custom solution from scratch. You get:
  • Clean, connected data from day one
  • Tools to build what you need, fast
  • Governance and compliance already in place
  • Real AI agents that plug into the work, not just sit beside it
Experience a unified approach to healthcare’s AI transformation
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