Solutions
Two systems. One reliability bar.
We build retrieval systems for trusted answers and workflow agents for operational work. Both are designed to stay inspectable after launch.
Grounding
Citations, retrieval quality, and source control.
Workflow
Tools, approvals, handoffs, and bounded actions.
Operations
Tracing, evals, and regression review before rollout.
Reliable RAG Systems
Turn company knowledge into AI assistants that answer from the right sources, cite their work, and fail safely.
Common use cases
AI Workflow Agents
Automate repetitive operational workflows with agents that can read context, follow rules, and hand work back to teams cleanly.
Common use cases
System Design
The production stack sits under every deployment.
The visible interface is only the surface. Reliability comes from the system layers underneath.
Data and ingestion
Document parsing, chunking, metadata, access control, and refresh pipelines.
Orchestration
Prompting, retrieval, tool use, retries, and workflow state that stay inspectable.
Guardrails
Confidence thresholds, approvals, safe refusals, and explicit escalation paths.
Evaluation
Benchmarks, traces, review queues, and regression checks tied to the workflow.
Build The Right System
Retrieval, agent design, and evaluation should ship together.
We scope the workflow, define the reliability bar, and ship the operating layer with the product surface.
Retrieval that holds up
Hybrid search, reranking, and source-aware responses.
Agents that stay bounded
Tool contracts, workflow state, and visible escalation.
Evals before hype
Release checks tied to real failures and regressions.