Interactive demonstrations

See where the model sits and what a person controls.

Every demo here runs on public or fully synthetic data, and each shows the same thing a Data and AI engagement has to show: where a model is used, what it takes in, what it returns, and which decisions stay with a person.

Open RCD PacketOps demo

Design-partner validation

Healthcare evidence-operations product

RCD PacketOps

RCD PacketOps is a file-first evidence and reviewer workbench for regional home-health coding, OASIS, QA, documentation-review, and revenue-cycle partners managing Review Choice Demonstration packets.

Open interactive demo

Industry intelligence

Four operating environments, four working decision demos.

Each one runs on public or fully synthetic data and shows the same chain: evidence, method, explainable finding, a scenario you can change, and the limits of the result.

Open-data demonstrationService & Field Operations

Service Demand & Crew Intelligence

Which requests should limited crews handle first?

A geospatial decision lab that turns unresolved service requests into risk-ranked hotspots and tests how crew capacity changes priority coverage.

Density-based geospatial clusteringExplainable multi-criteria risk scoringCrew-capacity scenario allocation

Open interactive demo

Synthetic-data demonstrationHealthcare Administration

FHIR Evidence Readiness Intelligence

Which requirement is unsupported, and does the record actually prove it?

An evidence workbench that traces each requirement to the FHIR element meant to support it, validates against the published R4 specification, and separates what a validator may decide from what a reviewer must.

Requirement-to-element traceability graphCardinality validation against the published specProgram rules on dates and linkage

Open interactive demo

Open-data model demonstrationManufacturing & Distribution

Predictive Maintenance & RUL Intelligence

How much useful life remains, and how uncertain is that estimate?

An engine-held-out remaining-useful-life model that combines multiscale sensor features, calibrated uncertainty, driver proxies, and a transparent maintenance-cost scenario.

Multiscale rolling sensor featuresExtra Trees ensembleEngine-level train/calibration/test split

Open interactive demo

Start with the operating problem

Show us where the work slows down.

You do not need a polished technical brief. Describe the decision, handoff, report, document workflow, or AI initiative that is harder than it should be.

Start the conversation