Multi-Agent System Design & Build
Outcome: Workflows that took days now run in seconds, unattended.
We design agent systems that coordinate across tools, data sources, and reasoning steps. Adaptive depth, MCP integrations, and proper controls from day one.
This means agent teams that know when to act and when to escalate, tool orchestration that holds under load, persistent memory and context, and observability so you can trace exactly what happened and why.
Good fit for: Automating multi-step internal workflows, replacing manual decision pipelines, building AI-native products.
- Working agent system deployed to your infrastructure
- Architecture documentation and decision log
- Observability and monitoring setup
- Handover sessions with your engineering team
Knowledge & Retrieval Systems
Outcome: Your team gets accurate, source-cited answers in seconds instead of searching for hours.
We design retrieval pipelines that ground agents in your actual data so they act on facts, cite sources, and stay inside their lane.
This goes beyond plugging in a vector store. It means getting retrieval quality right for your specific documents and queries, measuring it honestly, and connecting it through MCP so agents can access context from across your stack.
Good fit for: Internal Q&A tools, customer-facing knowledge bases, compliance and regulatory assistants.
- Retrieval pipeline tuned to your data and queries
- Evaluation suite with precision and recall benchmarks
- MCP integration with your existing tools and data sources
- Source citation and traceability built in
Production Deployment & Engineering
Outcome: Your AI goes from prototype to production without the 6-month learning curve.
Getting agents working in a demo is the easy part. Getting them running reliably, with evaluation frameworks, inference cost controls, compound error management, and graceful failure handling, is where most builds stall. BCG research shows 74% of companies struggle to move AI past the proof-of-concept stage.
We've shipped agents into production at scale. We know the gaps between a promising prototype and a system you can trust, and we close them.
Good fit for: Teams with a working prototype that needs to be productionized, or greenfield builds that want to skip the expensive learning curve.
- Production-ready deployment with CI/CD pipeline
- Evaluation framework for ongoing quality measurement
- Cost monitoring and inference optimization
- Failure handling and graceful degradation patterns
AI Audit & Advisory
Outcome: A clear picture of what's working, what's at risk, and exactly what to fix first.
Already have agents or AI systems in place? We conduct structured reviews of architecture, agent behavior, reliability, safety, and risk, and deliver a clear report with prioritized recommendations.
- Agent architecture and orchestration review
- Tool use, memory, and retrieval quality assessment
- Failure mode, edge case, and safety analysis
- Recommendations for observability, governance, and cost control
Good fit for: Teams that shipped fast and want to know what they got right and what needs fixing before it becomes a problem.
- Written report with findings and prioritized recommendations
- Risk assessment with severity ratings
- Architecture diagrams of current vs recommended state
- Follow-up session to walk through findings