Inside the engine · runtime
Raw input in. Trusted output out.
Once it's built, this is what runs at runtime — the same data pipeline powering ScribeAI in production. Every stage is concrete, observable, and auditable.
Documents, uploads, scans, and data streams come in. Vision + OCR turn even handwritten input into structured text the system can work with.
The relevant domain knowledge is retrieved (RAG) and fed to LLM pipelines (LangGraph + Gemini) — so the model reasons with your context, not generic guesses.
Outputs are scored, validated against rules, and routed. Confidence bounds and constraints are enforced before anything moves forward.
A person approves before anything is published. The review gate is part of the pipeline — the safeguard that makes the output trustworthy. No auto-publish.
Approved results are delivered — published, written back, or actioned via event-driven workers — and outcomes feed back to improve the next cycle.
What defines it
Four principles we never bend.
Production-first
We build for real load from day one. If it can't run reliably in production, it isn't done — the architecture doesn't change when the volume does.
Human-in-the-loop
AI proposes; a person decides where it matters. Oversight is a first-class part of the design, not an afterthought bolted on later.
Auditable by design
Every decision carries a traceable record — inputs, reasoning, and the human sign-off. You can always see why the system did what it did.
Improves in operation
Outcomes feed back into the system. Accuracy compounds the longer it runs — a structural property, not a scheduled retraining project.