Provider ladders with fallback
A quota wall at a provider should degrade the answer, not stop the business. Primary, fallback, and a record of which one served each call.
LLM tools and agent orchestration
Not a chatbot on your homepage. The systems that use a model to do work, and the machinery that keeps them honest when it answers badly.
What we build
Structured output, validated
The model returns JSON against a schema and the call retries on a mismatch. Parsing free text and hoping is not an integration.
Evaluation gates
A generated message is checked before a customer sees it — language, structure, whether it repeats something already sent. Failures escalate to a person instead of going out.
Memory that knows when
A temporal knowledge graph rather than a flat vector store: facts have a validity window, so a system can answer what was true in March without confusing it with what is true now.
Parallel agents
More than five working at once, each in an isolated git worktree, against a shared task list with single-writer locks. Isolation is what makes the parallelism safe rather than merely fast.
The part that is actually hard
A prompt is a request, not a guarantee. Everything that must be true of the output has to be checked in code afterwards. We learned that on our own system: an instruction to write in the customer's language lost to a context written in another one, and the proposal went out in the wrong language until a check — not a better prompt — caught it.
What you get
- Pipelines with fallback and quota handling
- Schema validation and a repair path
- Evaluation gates with escalation to a human
- Cost and latency visible per call
Stack Python · Claude · DeepSeek · Gemini · Graphiti · FalkorDB · Celery
Start here
Describe it in a paragraph.
You will get a real answer from an engineer — what we would build, roughly what it costs, and honestly whether it is worth building at all.