AI can read anything. AI cannot be trusted to decide — not when the decision is a denied claim, a spoiled shipment, or an audit exposure. We build the layer between the two. Our engines use AI to interpret messy real-world data, then evaluate it against encoded expert criteria with deterministic logic. Same input, same output, every time. Every decision traces back to the rule and evidence that produced it.
Payer policies. SLA contracts. Regulatory thresholds. The criteria are written down — in 40-page PDFs, spreadsheets, and contract appendices that no one reads under pressure.
Clinical documents. Shipment events. Sensor readings. The information exists — scattered across portals, emails, TMS screens, and EHRs. No single system has the full picture.
Experienced operators make good calls. New hires don't. The institutional knowledge lives in someone's head. Every departure, every shift change, every vacation is a risk.
EHRs, TMS platforms, and dashboards are systems of record — they show you what happened. They don't tell you what to do next. The decision layer does not exist.
Payer criteria lived in PDF guidelines. SLA terms lived in contract appendices. The rules existed — but only for people to interpret, not for software to enforce. Encoding them requires domain expertise, not just engineering.
Clinical documents, shipment events, sensor readings, emails — scattered across systems with no common format. Structured extraction at the point of decision was not practical at scale.
Language models are brilliant at reading messy input. They are also inconsistent by design — sampling randomness means the same question can produce different answers. That is fine for a chat interface. It is disqualifying for a prior authorization, an insurance determination, or an autonomous action in a high-stakes system. The decision itself must be deterministic, and every decision must be traceable to the exact rules and inputs that produced it.
EHRs, TMS platforms, and CRMs store data and surface alerts. They were never designed to evaluate inputs against rules and produce a specific recommended action. The decision layer was always missing.
What changed: language models can now turn messy real-world input into clean structured data. That solves half the problem. Interpretation alone is not enough — a probabilistic read of a clinical note or a shipment event cannot carry a regulated decision on its own.
The other half is what Avectic builds: a deterministic evaluation engine that takes the structured interpretation and applies encoded expert rules to produce an auditable, repeatable decision. AI on one side. Deterministic logic on the other. The bridge between them is the product.
Not a copilot. Not a chatbot. A decision engine.
Spine surgery prior authorization intelligence. A coordinator describes the planned procedure. The engine returns exact CPT codes, modifiers, NCCI compliance alerts, and payer-specific documentation requirements — in seconds, not hours.
Supply chain exception handling intelligence. A dispatcher describes the exception. The engine returns a recommended action, reasoning chain, cost analysis, and a draft customer notification — before the next call comes in.
Every domain we add reuses the same evaluation engine, the same feedback loop, the same output generation. The domain rules change. The infrastructure doesn't.
PA LogiQ took months to build. Exception LogiQ took weeks. The third domain will take days. Each one makes the platform more valuable and harder to replicate.
Internally, we refer to this architecture as WAFL (Workflow Authorization Framework Layer). Patent pending.