Jindal Healthcare is a US revenue cycle management operator, headquartered in Houston, Texas, that now goes to market through ANKA — its AI revenue-cycle platform. ANKA takes on the whole post-submission cycle: denial management, underpayment recovery and AR follow-up, reading EOBs, writing and filing appeals, disputing underpayments and chasing payers, with people handling the exceptions.
It serves physician groups of 10 to 50-plus providers, community and rural hospitals, PE-backed healthcare organisations and in-house revenue cycle departments. The company states it manages 300+ providers across its portfolio, has processed over $1B in claims, brings more than a decade of RCM operating experience, and reports 100% client retention. It is HIPAA compliant and SOC 2 certified, and sells on performance-based pricing backed by SLAs.
2. Problems
What Jindal Healthcare was up against
- 01Every client is its own dataset — distinct claims data, payer mix, denial patterns and reporting expectations, multiplied across the roster.
- 02A senior-analyst bottleneck. Answers needed someone who could write, run and reconcile SQL across systems that were never designed to talk.
- 03Reconciliation overhead. Numbers had to be stitched together by hand every time a question crossed two systems.
- 04Four hours per question. At that cost analysis was rationed, saved for problems the team already knew were problems.
3. Outcome
What changed with Supaboard
- 90%
- Reduction in analytics cost across RCM operations
- 4 hrs → 2 min
- Per-client analysis, fast enough to run on demand
- Every client
- Claims, payer and denial data unified into one source of truth
4. The story
The Jindal Healthcare story
Jindal Healthcare operates in revenue cycle management — one of the most data-heavy corners of healthcare. Every RCM client brings its own claims data, payer mix, denial patterns, and reporting expectations. Multiply that across a client roster and the analytics workload becomes its own operations problem.
With Supaboard, Jindal unified RCM data across clients into one source of truth. The trainable agent learned the domain — claim lifecycles, denial categories, payer behaviours, and the difference between aging that signals a process problem and aging that's just how a payer pays.
When answering questions costs as much as the work
A single client analysis used to take four hours. The work was spread across multiple analysts, multiple SQL queries, and multiple reconciliations between systems that were never designed to talk to each other. At four hours a question, analysis gets rationed: you save the deep look for problems you already know are problems.
One source of truth, trained on RCM
Supaboard unified Jindal's RCM data across clients and learned the domain, so analysis stopped depending on who was free to write the query:
- Unified across clients: All claims, payer, and denial data in one trusted source instead of scattered systems.
- Trained on the domain: Supaboard learned claim lifecycles, denial categories, and payer behaviours — including when aging is a process problem versus just how a payer pays.
- Anyone can ask: Analyses that required a senior analyst became conversations any operator could have with the data directly.
From four-hour SQL builds to two-minute answers
What used to mean opening a ticket, waiting for an analyst, and reconciling outputs now happens in a single question. Analyses that took four hours now take two minutes — and analytics cost dropped 90%. The query layer, the reconciliation, and the senior-analyst time all collapse into a conversation any operator can have with the data directly.
Investigating at the speed of curiosity
What changed wasn't just speed — it was which questions became askable. When a deep look cost four hours, the team saved it for known problems. When it costs two minutes, anyone can chase a hunch. Denial root-cause work, client reviews, and benchmarking now start from curiosity, not from whatever the analytics queue had time for.
Where Jindal Healthcare landed
Jindal Healthcare now runs RCM operations on Supaboard. The change shows up across the team's day-to-day:
- 90% lower analytics cost: Answering questions no longer rivals the cost of the work the answers inform.
- Four hours to two minutes: Per-client analysis is fast enough to run on demand.
- More questions, answered: The team investigates freely instead of rationing what it can look into.
