AI & Agentic Automation
Agentic AI that survives contact with enterprise reality.
We design, build, and govern applied AI systems — multi-agent workflows, retrieval-augmented knowledge tools, and document intelligence — grounded in the security and integration discipline enterprises can't compromise on.
Overview
The gap isn't models. It's engineering.
Enterprise AI initiatives rarely fail on model quality — they fail on integration, governance, and workflow design. Our practice combines hands-on experience with modern agentic frameworks (LangGraph, CrewAI, OpenAI Agents SDK, Microsoft's agent tooling, and the Model Context Protocol) with two decades of enterprise architecture. We build AI systems that connect to your real data, respect your security boundaries, keep humans in the loop where it matters, and are measured against business metrics from day one.
Core capabilities
- Agentic AI systems — Multi-agent workflows for document-heavy processes — ingest, extract, retrieve, draft, score — with human review gates.
- Retrieval-augmented generation (RAG) — Knowledge platforms over your policies, contracts, and documentation with citation-grade traceability.
- Document intelligence — Automated extraction, classification, and summarization for RFPs, contracts, compliance evidence, and operations documents.
- AI integration engineering — MCP-based tool integration, API orchestration, and secure connection of AI systems to enterprise data.
- AI governance & responsible AI — Risk assessment, evaluation harnesses, guardrails, and audit trails aligned to emerging AI governance expectations.
- AI opportunity assessment — Structured discovery that ranks use cases by value, feasibility, and risk — before a dollar is spent on builds.
Offerings
How clients engage this practice.
AI opportunity assessment
A 3-week engagement producing a ranked use-case portfolio, feasibility analysis, and a build/buy/wait recommendation per candidate — grounded in your data reality.
Agentic pilot in 90 days
One high-value workflow taken from concept to a governed, measured pilot — typically a document-intensive process like proposal response, compliance evidence, or knowledge search.
RAG knowledge platform
A production retrieval system over your internal content: ingestion pipelines, chunking and evaluation tuned to your corpus, access control, and measurable answer quality.
AI governance framework
Policies, evaluation standards, and technical guardrails that let your organization scale AI adoption without scaling risk.
Copilot & workflow automation
Practical automation with the AI tools you already license — integrated into Teams, DevOps, and business workflows where adoption actually happens.
AI engineering enablement
Hands-on upskilling for your developers: agent frameworks, evaluation practice, and secure integration patterns, taught by practitioners.
Tools & platforms
Technology we work in daily.
FAQ
Common questions.
We've run AI pilots that never reached production. What's different here?
We start from the workflow, not the model: who reviews outputs, how quality is measured, where the system connects to real data, and what 'good' costs. Pilots are built on production-shaped architecture so promotion is a decision, not a rebuild.
How do you keep our data safe when using AI models?
Architecture first: enterprise API tiers or private deployments with contractual no-training guarantees, data minimization in prompts, access control on retrieval, and full audit logging. We document the data-flow so your security team can approve it.
What's a realistic first agentic use case?
Document-heavy internal workflows win first: proposal and RFP response, compliance evidence gathering, knowledge search, report drafting. High volume, clear quality criteria, and a human already in the loop.
Do we need a data science team to maintain what you build?
No. We build on managed platforms and mainstream engineering skills your team already has, and enablement is part of every delivery. You should be able to run it without us.
Pick one workflow. Prove it in 90 days.
Start with an AI opportunity assessment or go straight to a governed agentic pilot.