Automation capability pillars, deployment patterns, and reference implementations. Each section describes the engineering approach, the deployment model, and the governance architecture applied.
PDF ingestion with native text extraction and OCR fallback. Per-page quality assessment. Structured output with character-offset provenance back to the source document. Handles scan artifacts, mixed formats, and low-quality inputs without silent degradation.
Applied in: healthcare chart review, document-intensive ops workflows.
Same inputs, same outputs—always. Pipelines are designed for reproducibility: any historical run can be replayed to verify behavior. Non-deterministic components (LLM calls, OCR) are isolated, bounded, and explicitly labeled. Regression packs validate behavior across releases.
Applied in: all reference implementations.
AI is introduced as a bounded, last-mile component—only after constraints, evidence requirements, and review gates exist. Every AI-assisted output is labeled, scoped, and connected to a source. The system does not assert what it cannot evidence. Ambiguous cases surface as flags.
Applied in: ICD-10 detection, market candidate scoring.
Constraint-defined operating envelopes with explicit gate logic. When inputs fall outside bounds—stale data, low-quality OCR, insufficient liquidity—the system halts or flags rather than proceeding. Live observability dashboards surface system state in real time.
Applied in: market research risk filters, crypto circuit breakers, OCR quality gates.
Policy layers, approval queues, and kill switches are first-class architecture. The operator configures thresholds, approves outputs, and retains override authority at every stage. Shadow mode runs new logic in parallel, read-only, before it influences any live operation.
Applied in: all reference implementations; central to the governance framework.
Structured integration with external APIs, internal data sources, and third-party services via intermediary services that validate, normalize, and audit data at ingestion. Taxonomy mismatches and unrecognized entries are flagged at the boundary, not silently aggregated.
Applied in: Harvest API automation, market data feeds, optional healthcare integrations.
We deploy inside the customer's environment. The pattern is chosen based on data sensitivity, network architecture, and integration requirements.
Full stack deployed within the customer's network perimeter via Docker Compose. No data leaves the environment during normal operation. External API calls (market data, optional integrations) are explicit, configurable, and logged. Default pattern for healthcare and finance implementations.
Core processing and data storage remain on-prem. Specific, bounded components—market data feeds, optional enrichment APIs—may contact external endpoints. Each external integration is documented, scoped, and operator-configurable. Data residency boundaries are explicit.
Full deployment within a network-isolated or air-gapped environment. All external dependencies are resolved at build time or via controlled artifact feeds. Suitable for environments with strict network egress controls or regulatory requirements prohibiting external service contact.
Deployment within the customer's own cloud account (AWS, Azure, GCP) in a private VPC. Infrastructure remains under customer control; Cast Net Technology does not have access to customer cloud environments. Data residency and access controls are determined by the customer's cloud configuration.
Each section below describes one of our reference implementations: the problem it addresses, how the pipeline works, and the governance architecture applied.
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 implementation 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 operator'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 runs on-prem. No chart content, extracted text, or structured output is 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 implementation →
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 system.
Market Edge 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 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 calibration layer provides feedback on whether the ranking model's emphasis is producing useful candidate selection over time.
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 implementation →
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.
A .NET intermediary service 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 provides 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.
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.
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. Discuss this pattern →
Contact us to discuss your domain and deployment requirements. We'll tell you honestly whether our reference builds are the right starting point and what an engagement would involve.