Our engineering methodology starts with constraints, not capabilities. AI is introduced only where provenance, determinism, and operator control are already in place. This is the full explanation of what that means—and why it matters.
Cast Net Technology builds Governed intelligence, not guesswork, audit-grade software—engineered for control, observability, and defensible outcomes. We build governed, audited, evidence-grounded systems where automation is earned through constraints, provenance, determinism, and operator control.
The dominant pattern in modern AI product development is AI-first: deploy AI broadly, accept errors as a cost of speed, and refine post-hoc. For many domains, this tradeoff is acceptable. The cost of an error is low, correction is fast, and the aggregate benefit of speed outweighs individual mistakes.
That calculus fails completely in regulated, high-stakes, or high-trust domains. In Medicare Advantage risk adjustment, a confident wrong ICD-10 binding isn't a UI bug—it's a clinically and financially consequential error in a chart that may be audited. In options trading, a confident wrong recommendation isn't an annoyance—it's the difference between an informed decision and a misled one.
Governed intelligence, not guesswork engineering inverts the default. Automation is earned, not assumed. Every automated step requires prior justification through constraints, test coverage, and provenance—before it touches real data or real decisions.
The difference isn't just philosophical. It determines whether an auditor can trace a decision back to a source, whether a regression test can catch a silent change in behavior, and whether an operator can intervene in time.
These are not aspirations—they are architecture requirements evaluated at the design stage of every product we build.
Every output must trace back to a source. Whether it's a character offset in a chart PDF, a market data snapshot, or an operator action in a workflow—every assertion has a citable origin. Nothing is asserted without evidence.
Applied to: chart findings (page/offset), ranked market candidates (scoring inputs), accounting events (order logs), inventory records (API source + operator edit).
Given the same inputs, the system produces the same outputs. This enables reproducible audit, regression testing, and meaningful comparison of outputs across time or across versions. Non-deterministic components are isolated, bounded, and explicitly labeled.
Applied to: PHI-safe synthetic chart evaluation packs; reproducible market data session replays; SQLite effective-config snapshots for crypto research.
Automation is introduced only after the problem is bounded. Edge cases, failure modes, and adversarial inputs are enumerated before code is written. The constraint set defines the boundary of safe operation; behavior outside that boundary triggers a flag or a halt.
Applied to: detection confidence thresholds; grid regime gates; liquidity/spread risk gates; OCR quality minimums.
Policy layers, review gates, and kill switches are first-class features. The operator configures the system, approves outputs, and retains the right to override, pause, or halt any automated behavior. The system advises; the operator decides.
Applied to: chart review workflow; Google Sheets approval gates for listings; EdgeOS decision support (no execution); crypto swarm kill switches.
New automation logic runs in parallel with existing behavior, read-only, against live or production-equivalent data. The shadow output is observed and compared to the baseline before promotion. No new logic enters production without a shadow validation period.
Applied to: EdgeOS ranking model updates; crypto swarm strategy parameter changes; detection model updates in the healthcare pipeline.
Behavioral changes must be intentional. PHI-safe synthetic evaluation packs, deterministic test sets, and baseline comparisons ensure that a code change cannot silently alter system behavior. Regressions are caught before deployment, not discovered in production.
Applied to: healthcare chart intelligence releases; EdgeOS scoring changes; swarm logic updates.
Our strongest moat is knowing what not to build. These are explicit commitments, not aspirations.
Any system where the logic between input and output is opaque, uncheckable, or unexplainable to a domain expert is not a system we will build or endorse. Explainability is a minimum requirement, not a premium feature.
In any domain where a wrong decision has consequential real-world outcomes, we do not build fully autonomous systems. The operator remains in the decision loop. Always.
We will not claim regulatory compliance on behalf of customers, guarantee clinical coding accuracy, guarantee financial results, or imply that our systems eliminate the need for qualified human review. They do not.
Particularly in healthcare, market research, and any domain involving sensitive operational data, our default architecture keeps data inside the customer's infrastructure. Third-party integrations are explicit, documented, and opt-in.
If the Governed intelligence, not guesswork methodology resonates with your organization's requirements, we'd like to talk. We work best with teams who take audit trails seriously.