Three core services, one principle: working software over slide decks. Every engagement ends with production-ready code, not recommendations.
Design and build of multi-agent systems — AI that uses tools, makes decisions, and executes multi-step workflows autonomously. This includes MCP server development, agent orchestration frameworks, and integration with existing toolchains.
Companies with repetitive, high-volume workflows that basic automation can't handle well. Teams that have tried rule-based automation and hit the ceiling. Organizations that want AI agents embedded in their operations — as a production system, not a toy demo.
A focused assessment of workflows and infrastructure. Identification of highest-ROI automation targets. Architecture, build, and deploy of the agent system. Typical timelines: 4-8 weeks for an initial production system, depending on complexity and integration points.
Assessment and modernization of data infrastructure to support AI workloads. This means restructuring pipelines for real-time access, building APIs that AI agents can consume, implementing data quality and governance, and connecting existing data stores to LLM-powered applications.
Any company whose AI ambitions have outpaced their data infrastructure. If you've invested in AI tools or pilots and the results are underwhelming — the problem is usually here. Over 40% of agentic AI projects fail because the underlying data architecture wasn't built for it.
A data infrastructure audit — mapping current architecture, identifying gaps, and prioritizing fixes by impact. Then hands-on engineering: new pipelines, APIs, data quality checks, and the connective tissue between data and AI systems. This is engineering work, not a report.
AI capabilities embedded into existing products and internal tools. RAG (retrieval-augmented generation) systems, intelligent search, content generation, document processing, and custom LLM-powered features that users interact with directly.
SaaS companies, startups, and product teams that want AI features integrated into the workflows their users already use. Particularly relevant for teams with a working product that want to add intelligent capabilities without rebuilding from scratch.
Collaborative scoping — what the feature does, what data it needs, how users interact with it. Build of the retrieval pipeline, LLM integration, and API layer. Integration with the existing codebase and deployment to production. The deliverable is a production feature, not a Jupyter notebook.
A 30-minute conversation. You describe what you’re trying to accomplish. We tell you honestly whether we can help and what it would take. No pitch, no pressure.
A clear scope of work — what gets built, what you’ll have when it’s done, timeline, and cost. No ambiguity, no change-order surprises.
The work happens. Regular updates and working demos as the project progresses. Integration with your team’s existing tools and workflows — Git, Slack, whatever you use.
The system goes to production. Deployment, documentation, and knowledge transfer so your team can maintain and extend everything. You own it all.
Not every company needs a full-time AI hire. But many need senior AI leadership — someone who can set technical direction, evaluate tools and approaches, architect systems, and build alongside the existing team.
A Fractional AI Lead engagement means embedding with your team on a monthly retainer — typically 2-4 days per week. Standups, architecture discussions, code reviews, and building the high-leverage AI components directly.
The goal is to become unnecessary. Build the systems, establish the patterns, upskill the team. When you're ready to bring it fully in-house, you have everything you need.
If your AI initiative is stalled, your data infrastructure isn't ready, or you need senior engineering help to bridge the gap — let's have a straightforward conversation about it.