Reddenda Predictive Intelligence In development
Reddenda already answers the hard question with arithmetic: your contracted rate versus the payer's own federal filing. The predictive layer is the next stratum, a learning system trained on how those filings move month over month. Prediction heads on payer behavior and renewal outcomes, built on the same public data spine. No PHI required.
The architecture
The predictive layer is not a pivot to a chatbot. It is the third stratum of a system whose first two strata are live and doing deterministic work today. That order matters: the learning layer is only credible because of what sits underneath it.
Every month, every commercial payer must publish every negotiated rate under federal Transparency in Coverage rules. Reddenda ingests those filings on a monthly cadence and normalizes them against the NPI Registry and the CMS Physician Fee Schedule. 314M+ records identified across the raw federal universe, with an index built toward all 50 states. Each monthly filing is one frame.
RateScore, gap math, peer benchmarks, leverage memos, renewal tracking. No machine learning involved and none needed: this stratum is arithmetic on the payer's own filing. It is live today, and you can run it on any NPI with results in about 15 seconds. No PHI, no card.
Stack enough monthly frames and the data stops being a snapshot and becomes a film. The learning layer trains on that film: small prediction heads that each answer one narrow, testable question about payer behavior and renewal outcomes. This page describes that work honestly, as work.
The learning layer
A prediction head is a small model trained on top of one shared representation of the rate landscape. Each head answers exactly one question. Narrow questions are testable, and testable is the entire point.
Which direction does this payer's filed rate for this code family move at the next monthly filing?
Trains on: stacked monthly Transparency in Coverage snapshots per payer, code family, and locality. The longer the index runs, the deeper the film it learns from.
Ships only after it survives a held-out backtest.
Which contract renewal windows are likeliest to reprice, and which go quiet?
Trains on: effective, expiration, and renewal patterns across federal filings, plus the platform's own renewal telemetry as practices work their calendars.
Ships only after it survives a held-out backtest.
How does this payer historically respond to a documented realignment request?
Trains on: outcome telemetry that accrues as leverage memos move through real negotiations. Honest note: this head needs outcome volume before it can be tested, so it ships last.
Ships only after it survives a held-out backtest.
Which locality medians for a code family are starting to move?
Trains on: benchmark drift across the normalized index. The goal is early warning, so a practice negotiates against where the market is heading, not where it was.
Ships only after it survives a held-out backtest.
Why this is possible now
Transparency in Coverage made every negotiated rate public starting in 2022. Most of the industry treats each release as a static lookup table. We treat it as a frame.
Stack the months and payer behavior becomes learnable: which payers drift, which hold, which reprice at renewal, which quietly converge toward locality medians. That stacked history is the training set, and it grows by one frame every month, on a federal schedule, whether anyone likes it or not.
And because the substrate is public filings plus the platform's own telemetry, the learning layer needs no PHI, at intake or in training.
Demo sample. This chart is an illustrative scaffold of what a payer-drift forecast will look like. It is not a live model output, and no real payer, code, or rate is shown.
The standard we ship against
Healthcare AI has an adjective problem. We are building the predictive layer under four rules, written down before the first head ships, so you can hold us to them.
You will never see a performance claim here that did not survive a held-out backtest. Until then, the status reads live, verified by backtest before it ships.
Anything illustrative on this page says so on its face. The chart above is a labeled demo sample, not a model output, and that labeling rule applies everywhere we publish.
Heads train on public federal filings and the platform's own telemetry. No PHI required, at intake or in training. The substrate is auditable because it is public.
A prediction informs a negotiation. It does not promise an outcome. The only dollar language we use is documented reimbursement opportunity, and that rule does not bend for a model.
Where it ships first
AGENTic is the autonomous tier of Reddenda, where modules run on a continuous cadence without operator clicks. That is the natural home for prediction heads: a forecast is most useful to the system that can act on it the moment it lands. AGENTic is waitlist only, with no public pricing.
The waitlist
Reddenda identifies documented reimbursement opportunity based on public contracted rates and submitted practice inputs. Actual recovery depends on payer response, contract terms, documentation, and negotiation outcome. Founder's Promise details · Methodology
Want the live engine today? Run the free Practice Snapshot on any NPI. Results in about 15 seconds. No PHI, no card.
Three fields and an optional note. We reply within one business day.
Your place is held. Expect a reply within one business day at the email you provided. The learning layer is live and the deterministic engine is live right now.
Run the free Practice Snapshot