AI product development built for production
We build AI agents, copilots and RAG systems with controlled behavior, measurable quality and real business integration.
How we build
AI must create operational value
We begin with the decision or workflow that should improve, not with a model demo. Data access, tools, permissions, human oversight, quality metrics and failure modes shape the architecture.
Agents, RAG, evals and guardrails
Production AI needs orchestration, tool use, retrieval, context management, evals, traces, guardrails and fallback behavior. Multi-agent systems also need explicit policy boundaries and operator control.
AI integrated into real software
We connect AI to mobile products, SaaS, backend services, business data and security while keeping the model/provider layer adaptable.
Working model
Agents, copilots, RAG/knowledge systems, intelligent workflows and multi-agent systems.
With evals, representative test sets, guardrails, traces and human review where needed.
Production AI: agents, RAG, evals and control
An AI product should create measurable operational value rather than simply demonstrate a model. We begin with the workflow, decisions, data access, allowed tools, quality criteria and failure modes. For agents we define permissions, human approval, auditability, retries, fallback behavior and autonomy boundaries before model integration.
RAG systems are engineered around ingestion, chunking, metadata, retrieval, access control, evaluation datasets and observability. Model choice is only one layer. Production quality depends on whether the system retrieves the right context, respects permissions and produces testable outputs under realistic workloads.
Agentic workflows add structured outputs, policy boundaries, traces, cost controls and provider abstraction. This lets the product evolve across models while controlling the risk of AI actions against CRM, ERP, documents, internal APIs and business-critical systems.