What we build
The engineering around the model.
The model is one component. These are the four we build around it to make an LLM dependable — every one of them running in ScribeAI today.
LLM pipelines
Structured, multi-step chains — not a single prompt. Each step has a job, a schema, and a check, orchestrated with LangGraph the way ScribeAI's evaluation runs.
- Multi-step graphs
- State between steps
- Deterministic control flow
Orchestration & routing
Send each task to the right model or step, with fallbacks when one is slow or down. Use a cheaper model where it's enough, a stronger one where it counts.
- Task-aware model routing
- Cost / quality balancing
- Fallbacks & retries
Prompt & output engineering
Reliable, structured outputs you can act on — not free text you have to parse and hope. Schemas enforced, so the next step always gets clean input.
- Enforced output schemas
- Grounded, cited responses
- Versioned prompts
Evaluation & quality
Measure whether the system is actually right, and catch regressions before your users do — the difference between a demo and something you can trust in production.
- Quality metrics & test sets
- Regression detection
- Continuous improvement
Why you can trust it
Built to survive real production.
The hard part of AI isn't the first working prompt — it's staying reliable as models change and load grows. Four properties we engineer in from day one.
Model-agnostic
Not locked to one vendor. As models improve or prices shift, we swap the engine underneath without rewriting your product.
Structured
Outputs are validated against a schema, so downstream systems get clean, predictable data — never free text you have to guess at.
Evaluated
Quality is measured against real test sets, continuously. Regressions surface in our checks, not in front of your users.
Resilient
Retries, fallbacks, and circuit breakers mean a single model outage degrades gracefully instead of taking the whole system down.