From the agent and the systems it talks to, through the ML training and evaluation work, down to the data infrastructure underneath. We work across the full path between idea and production AI.
Agentic systems do more than answer a prompt. They use tools, call APIs, query data, and carry out multi-step work with a degree of autonomy. Getting them to behave reliably is the hard part: real workflows need clear boundaries, predictable handling of edge cases, and a sensible point for a person to step in. We design agent and multi-agent systems around how the work actually runs, and around the failure modes that show up once they leave a demo.
An AI agent is only as useful as the systems it can reach. The Model Context Protocol gives agents a structured, governed way to connect to internal tools, data, and APIs. The work is part integration and part security: defining what an agent is allowed to do, scoping access per tenant and per user, and logging every action so the connection holds up to review. We build MCP servers against the systems a business already runs.
General-purpose models don't know your business. Retrieval-augmented generation grounds them in your own content, such as documents, policies, and knowledge bases, so answers are accurate, current, and traceable to a source. Done well, retrieval is its own discipline: how content is chunked and indexed, how results are ranked, and how quality is measured all decide whether the system is trustworthy or just plausible.
The difference between an AI prototype and a production system is knowing how well it works. Evaluation makes quality measurable: structured test suites, automated checks in the pipeline, and monitoring that catches regressions after release. Guardrails sit alongside: content safety, output validation, and defenses against prompt injection, so the system stays within bounds as inputs change and models are updated.
Not every problem calls for a large language model. Classical machine learning still wins for forecasting, classification, ranking, and anomaly detection, and custom or fine-tuned models often outperform general ones on a narrow task. The lasting work is the operational layer around them: reproducible training, reliable serving, and monitoring that flags drift before a model quietly degrades in production.
AI and analytics are only as good as the data underneath them. Most of the effort in any data initiative goes into moving, shaping, and validating data so it's reliable enough to build on. We design the pipelines, lakes, and warehouses that feed analytics and machine learning, both batch and streaming, and increasingly the vector and embedding pipelines that AI systems depend on.
Few systems live in one layer. The work here usually connects to the disciplines around it.
Whether you are taking an AI system from prototype to production or building the data foundation it runs on, we can help you get it right.
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