Our AI Practice
Agentic AI & GenAI systems that run in production
We design and ship autonomous agents, RAG pipelines, and LLM-powered features with the evals, guardrails, observability, and cost controls needed to trust them with real work. From AI advisory to multi-agent orchestration, engineered for reliability, not demos.
How we think about AI systems
Reliability comes from constraints, not cleverness
Agents are systems, not prompts
We treat an agent as a distributed system with state, tools, retries, and failure modes. That framing drives every design decision - from the planner loop to the logging strategy.
Human-in-the-loop by default
For any agent touching money, customer data, or external systems, we build checkpoints that let humans approve, override, or roll back. Autonomy is a dial, not a switch.
Measured, not vibes-checked
Every AI system ships with an eval suite covering task success rate, tool-use correctness, hallucination rate, latency, and cost. Regressions fail CI the same way broken unit tests do.
What we build
Full-spectrum agentic AI & GenAI services
From single-purpose copilots to multi-agent orchestrations, RAG systems, and fine-tuned models - we design the pattern that fits the problem, not the other way around.
AI Advisory & Strategy
Navigate the AI landscape with expert guidance. We help identify high-impact use cases, assess build vs. buy decisions, and develop realistic roadmaps for AI adoption.
Agentic AI & Autonomous Workflows
Single-agent copilots, planner+executor loops, and multi-agent orchestration. Built on LangGraph, CrewAI, OpenAI Agents SDK, or Claude Agent SDK with proper tool use and human-in-the-loop patterns.
Production RAG Systems
Production-grade retrieval-augmented generation with advanced chunking, hybrid search, reranking, and citation tracking. Tuned for your specific accuracy and latency requirements.
Evaluation Frameworks & Evals
Rigorous eval systems using Braintrust, Langsmith, or custom frameworks. Measure what matters: accuracy, hallucination rates, tool-use correctness, latency, and cost - in CI and in production.
LLM Fine-tuning & Optimization
When RAG isn't enough, we fine-tune models for your domain. Data preparation, training, evaluation, and deployment with proper versioning and rollback capabilities.
AI Integration & APIs
Seamlessly integrate AI capabilities into your existing applications. Robust APIs with rate limiting, caching, fallbacks, model routing, and cost controls.
Frameworks we use
We pick the agent framework that fits the problem
LangGraph
Our default for complex, stateful agents with explicit control flow. Excellent for planner/executor loops, multi-agent supervision, and workflows that need to be debuggable.
CrewAI
When role-based multi-agent orchestration maps cleanly to the domain - research crews, content pipelines, simulated team workflows.
OpenAI Agents SDK & Claude Agent SDK
When you want to lean into a specific model family and use the provider's first-party tooling for handoffs, guardrails, and tracing.
LangChain & Custom Orchestration
For lighter-weight agents or when we need to build a custom orchestrator to match existing infrastructure and observability patterns.
Why this approach works
What you get when AI systems are engineered, not prompted
AI you can actually trust with real work
Guardrails, checkpoints, and evals mean you know what the system will and will not do - before it touches production traffic.
Predictable cost and latency
Token accounting, caching strategies, and model routing keep the bill predictable. No surprise invoices from runaway agents or RAG pipelines.
Debuggable when things go wrong
Full tracing of every step, tool call, and model response. When something misbehaves, you can see exactly what it decided and why.
Evolvable as models change
Our systems are abstracted from specific providers where it matters. Swapping models or upgrading to a new version is a versioned eval run, not a rewrite.
Have an agentic AI or GenAI project in mind?
Whether you're exploring your first agent use case or need help turning a promising POC into a production system, let's talk about what it takes.