AI agent architecture & implementation
We help product and operations teams turn a promising agent prototype into a controlled system that can use tools, handle real business workflows, and be measured in production.
Reference architecture
The model is one component—not the control plane. Each layer has an explicit responsibility and failure boundary.
Validate the request, identity, permissions, and operating constraints before work begins.
Plan bounded steps, maintain state, enforce budgets, and route exceptions to people.
Use fit-for-purpose models and allowlisted tools behind typed, testable interfaces.
Integrate APIs, knowledge sources, databases, queues, and existing operational workflows.
Outcome, telemetry & evaluation inform the next decision
Controls across every layer
What we build
Agents that classify, research, prepare actions, and update business systems—with approvals where mistakes carry real cost.
Retrieval and reasoning systems that cite source material, expose uncertainty, and make expert review faster.
LLM features integrated into an existing SaaS or fintech product with tenant isolation, usage controls, and product telemetry.
Production engineering
We define which actions are automatic, which need confirmation, and which should never be delegated to a model.
Representative evaluation cases, task-level success criteria, and regression checks are designed alongside the workflow.
Idempotent tools, execution logs, retries, timeouts, and human queues keep partial failures from becoming silent damage.
Model routing, context budgets, caching, and per-workflow telemetry connect AI spend to useful business work.
Engagement model
Identify the highest-value workflow, failure costs, data boundaries, integration constraints, and measurable success criteria.
Build one end-to-end path with real tools and representative cases to test reliability before expanding scope.
Add evaluations, observability, security controls, cost limits, deployment, documentation, and operational handover.
Tell us what the team does today, where time or quality is lost, what systems are involved, and what a costly failure looks like. We can determine whether an agent, a simpler automation, or no AI at all is the right answer.
Useful context: workflow, systems, data sensitivity, timeline, and budget range.