AI agents, built for your systems.

We take judgment-heavy work your team does by hand and hand it to agents you can trust. That means deciding where an LLM earns its place versus a simpler automation, orchestrating agents that call your tools and data, and building the evaluation, guardrails, and observability that let them run in production rather than stall as a demo.

• Agent and multi-agent architecture for multi-step, judgment-heavy work
• LLM workflow design and orchestration across tools and steps
• Tool use, function calling, and integration into your existing systems
• Retrieval, memory, and persistent state over your data
• Guardrails, human oversight, and failure controls
• Evaluation, observability, and cost control so behavior stays measurable

Shipped: an AI shopping assistant for an online fashion platform, and a RAG support assistant taken to production.

multi-agent systems · retrieval (RAG) · tool use & integration · agent memory · evaluation & guardrails

We work with your team to connect AI applications to existing data, APIs, and infrastructure. We handle deployment and the checks needed to keep the system running: evaluation, monitoring, failure recovery, and cost control. Documentation and handover give your team the context to operate and maintain it.

• Integration with your data sources, APIs, and existing applications
• Retrieval pipelines that connect applications to relevant company information
• Deployment on your cloud or private infrastructure
• Evaluation and monitoring for quality, latency, and cost
• Failure recovery and operational support for running systems
• Documentation and handover so your team can maintain the system

Shipped: production ML workflows on Azure Machine Learning and Databricks for an enterprise telecom engagement.

data & API integration · retrieval systems · deployment · monitoring · MLOps · handover