AI Solutions

AI delivers value when it is connected to your real data and processes, and when it is trusted by the people who use it. We help organizations move from experiments to dependable AI solutions, starting with the simplest approach that solves the problem and adding complexity only when it pays off.

Whether you want a knowledge assistant for your teams, an agent that automates a multi-step process, or a way to let AI tools safely work with your enterprise systems, we design, build and run it with you.

Generative AI applications

Assistants and copilots grounded in your own content, so answers are relevant, current and traceable to their sources.

Enterprise search and Q&A over documents, wikis and tickets using retrieval-augmented generation (RAG)
Document processing: summarization, classification and data extraction
Customer, employee and developer assistants embedded in the tools people already use

AI agents and agentic workflows

Systems where a language model uses tools to plan and complete multi-step tasks. We choose between predictable, step-by-step workflows and more autonomous agents based on the job to be done, and keep people in the loop where decisions matter.

Process automation across support, operations, finance and IT
Tool use with approvals, guardrails and audit trails
Evaluation and monitoring so quality, cost and latency stay under control

MCP integrations

The Model Context Protocol (MCP) is an open standard for connecting AI applications to data sources, tools and workflows, often described as a USB-C port for AI. We build MCP servers that expose your systems to AI assistants and agents in a controlled, reusable way.

MCP servers for databases, internal APIs and line-of-business applications such as ERP
Authentication, least-privilege access and logging designed in from the start
Build once and use with any MCP-compatible AI client

Data foundations for AI

Good AI needs good data. Our analytics and data teams prepare the pipelines, quality rules and master data that AI solutions depend on.

Security and responsible AI

We design for privacy, access control and human oversight, drawing on frameworks such as the NIST AI Risk Management Framework to identify and manage AI risks.

How we work

Discover: identify high-value use cases and check data readiness
Prototype: prove value quickly with a small, measurable pilot
Build: engineer for security, reliability and integration
Operate: monitor, evaluate and improve in production