The model is not the system
A useful LLM workflow needs context design, retrieval, tools, permissions, evaluation, logging, cost control, and fallback behavior.
We design applied AI workflows with retrieval, tool calling, agents, model routing, evals, traces, guardrails, and human review where the business risk demands it.
AI assistants, agents, RAG, document workflows
3 to 10 weeks from prototype to controlled workflow
AI connected to tools, data, and accountability
A useful LLM workflow needs context design, retrieval, tools, permissions, evaluation, logging, cost control, and fallback behavior.
We define which tools an agent can call, what data it can see, how it reports uncertainty, and when it must ask a person.
Golden datasets, regression tests, trace review, latency budgets, and human scoring keep the system honest after launch.
Chunking, metadata, permissions, reranking, citations, stale-content handling, and retrieval tests.
Function calling, MCP-compatible connectors when useful, workflow state, retries, approvals, and audit trails.
Automated evals, prompt/version tracking, model routing, latency/cost dashboards, red-team cases, and rollback paths.
LLM workflow design and risk map
RAG, tools, agents, integrations
Evaluation suite and trace review
Monitoring, cost controls, human escalation
AI acts on business context instead of generic prompts.
Failures can be inspected and fixed.
Teams know when automation should stop.
Only when retrieval, prompt design, tools, and routing are not enough. Most business systems improve first with better context and evaluation.
Yes. We connect models to tools through permissioned APIs, audit trails, approvals, and clear escalation paths.
A good brief includes the current workflow, the systems involved, the people affected, and what must improve after launch.