We design, build, and operate AI agents and copilots that automate real work—combining Microsoft's Copilot Studio and Microsoft 365 Copilot with open-source frameworks like LangGraph, LangChain, and ReAct agent graphs, all grounded by RAG and connected via open standards like MCP and A2A. We also support local and private LLM inference with Gemma 4 and Ollama.
Custom copilots and agents built natively on Microsoft's AI Cloud Partner stack, with Entra ID-secured, tenant-governed deployments. We design conversational flows, plugins, and connectors that extend Microsoft 365 Copilot into your line-of-business processes.
For organizations that need open-source flexibility and model portability, we build stateful, multi-step agent graphs with LangGraph and tool-using agents with LangChain—deployable on Azure, AWS, or on-prem/FedRAMP infrastructure.
We ground AI responses in your authoritative documents, policies, and data—using access-controlled vector search so every answer is accurate, current, and audit-ready.
A typical agent workflow combines a Copilot Studio or Microsoft 365 Copilot front end with a LangGraph orchestrator that retrieves knowledge and executes enterprise actions—all with full audit logging.
We combine Microsoft and open-source components into a single, access-controlled RAG pipeline that keeps your AI's answers grounded in your own content.
Beyond cloud-hosted models, we implement emerging open standards for agent interoperability and support fully local LLM inference for air-gapped, sensitive, or cost-sensitive environments.
We implement ReAct (Reason + Act) loops as structured agent graphs—giving agents explicit reasoning steps before every tool call, producing more reliable, explainable, and auditable outcomes compared to single-shot prompting.
Anthropic's open Model Context Protocol standardizes how agents discover and call tools, access data sources, and expose prompts. We build MCP servers and clients so your agents connect consistently to any system—now and as the ecosystem grows.
Google's A2A protocol enables secure, structured communication between autonomous agents across organizational and platform boundaries. We design multi-agent architectures where specialized agents collaborate and delegate tasks via A2A, enabling complex workflows no single agent can handle alone.
For air-gapped environments, sensitive data workloads, or cost-controlled deployments, we run Google's Gemma 4 and other open-weight models locally via Ollama—delivering full LLM capability with no data leaving your infrastructure.
We build high-performance Python API backends with FastAPI to expose agent capabilities, RAG pipelines, and tool endpoints—providing async, OpenAPI-documented services that front-end applications and other agents can call reliably.
We deliver modern, server-rendered agent interfaces with Next.js—streaming AI responses, real-time status updates, and chat UIs that work across desktop and mobile, integrated directly with your MCP and FastAPI backends.
Entra ID authentication, role-based access control, and ISO/IEC 27001:2013-aligned security practices govern every agent and data source.
Every agent action and retrieval is logged, supporting compliance reviews and ISO/IEC 20000-1:2011-aligned service management.
Open-source frameworks mean you're never locked into a single model provider—swap or combine models as your needs and budgets evolve.