Each section below describes one of our licensed products: the problem it is designed to address, how it works, and the type of outcomes it is built to enable. All figures are illustrative only and not guaranteed results.
Medicare Advantage organizations managing chart review backlogs for risk adjustment face a common set of problems: manual coding that is slow, inconsistent, and difficult to audit. Coders working from scanned PDFs miss ICD-10 codes buried in messy OCR output, and there is no systematic way to surface evidence for negated or historically mentioned conditions.
Cloud-based AI tools are often non-starters because PHI cannot leave the network perimeter.
The Healthcare Chart Intelligence product deploys on-prem in a Docker Compose stack within your network. The pipeline ingests chart PDFs (single and batch ZIP), applies native text extraction with OCR fallback, assesses OCR quality per page, and runs ICD-10 detection with explicit negation modeling.
Detected codes are mapped to CMS-HCC categories using the licensee's selected payment year model. MEAT evidence is extracted with page/offset provenance. Every ambiguous binding receives a "needs review" flag rather than a confident assertion.
PHI-safe synthetic charts are included for regression testing. Each release is validated against these before deployment.
Coding teams shift from reviewing entire charts manually to reviewing a structured report with provenance-backed findings and explicit flags. Time spent per chart on initial screening decreases substantially. The review workflow becomes more consistent because reviewers work from a structured, flagged candidate set rather than a blank page.
Every output is independently auditable. When a finding is questioned, the reviewer can navigate directly to the source page and character position in the original chart.
No PHI left the network. The entire pipeline ran on-prem. No chart content, extracted text, or structured output was transmitted to any external service during normal operation.
Conservative binding (ambiguous = flag, not assertion); explicit negation modeling; page/offset provenance; PHI-safe regression evaluation packs; operator-configurable thresholds. See the full solution →
Active options research operations typically rely on a combination of manual screening, commercial scanners, and informal knowledge to identify candidates. The process is noisy, inconsistent across sessions, and produces no audit trail: when a position goes wrong, it is difficult to reconstruct why it was selected in the first place.
Operators want a structured, reproducible candidate pipeline with explicit scoring criteria, IV context, and risk flags—without handing control to an algorithm or a commercial black-box product.
EdgeOS deploys on-prem with a configurable ranking model. Candidates are scored on operator-defined criteria. Each ranked candidate displays the specific factors contributing to its rank—no composite score without explanation. IV rank, IV percentile, Greeks, and theoretical values are displayed in context. Market calendar events (earnings, ex-dividend, FOMC windows) are surfaced as flags.
Risk gates filter out candidates with insufficient liquidity, stale quotes, or positions outside the defined session window. Shadow mode validates the ranking model against historical sessions before the operator uses it as a primary input.
The screening process becomes reproducible: the same inputs produce the same ranked output, which can be replayed from any historical snapshot. Decision rationale becomes documentable: when reviewing past decisions, operators can retrieve the ranked output and risk flags that were visible at the time the decision was made.
The calibration layer provides feedback on whether the ranking model's emphasis is producing useful candidate selection over time. This enables operators to adjust scoring criteria based on observed outcomes rather than intuition.
Not financial advice. No execution. This system produces research outputs for operator review. The operator makes all decisions. Past paper outcomes do not predict future results.
Evidence-grounded outputs; replayable sessions; shadow mode validation; risk gates; paper outcomes tracking only; no execution path. See the full solution →
Multi-project engineering operations tracking contractor hours manually face a common problem: the weekly process of compiling Harvest time-tracking data, mapping it to project budgets, and producing burn-rate reports consumes several hours of operational time per week and is prone to copy-paste errors at month-end.
The Business Operations Automation product includes a .NET intermediary service that reads from the Harvest API on a configurable schedule, normalizes time entries against the project and contractor taxonomy, and writes structured data to a shared dataset. An Excel dashboard consumes this data, providing project leads with real-time burn rates, remaining budget by project, and contractor hour roll-ups.
The intermediary service includes validation: entries that reference undefined projects or contractors are flagged for review, not silently aggregated into a catch-all bucket.
Weekly reporting time is eliminated as a manual process. Project leads have continuous access to current burn-rate data rather than weekly snapshots. Data validation flags catch taxonomy errors at ingestion rather than at the end of the reporting cycle, when they're harder to trace and correct.
The Excel dashboard design means teams work in a familiar environment. No new tools, no training, no vendor dependency for the reporting interface.
Input validation at ingestion; flagging of unrecognized taxonomy entries; audit log of API pull events; human-readable dashboard with traceable source data. Enquire about this solution →
Contact us to discuss licensing and deployment options. We'll tell you honestly whether our products are the right fit for your domain and requirements.