AI that earns its place in your business.
Four kinds of AI engagement we run. Pick the one that fits where you are.
Most engagements start in one of these tiers and grow into the next. We structure them this way because the four jobs are genuinely different — different teams, different decision-makers, different definitions of "done" — and because labeling them honestly is more useful than packaging the whole thing into a single "AI transformation" pitch.
AI strategy and readiness
The AI vendor market roughly doubled in the last eighteen months, and a meaningful portion of what's in it now won't still be in it eighteen months from now. The job in front of you is figuring out which decisions to make this quarter, which ones to defer until the category settles, and which categories aren't ready for any decision yet at all. We help leadership teams work through that with a clear-eyed read on your environment, your data, your people, and the operational realities of getting anything funded inside the organization. The "what to hold off on" half of the conversation is usually the more useful one.
- An honest readiness assessment across strategy, data, talent, and governance
- A sequenced adoption roadmap structured around your budget cycles
- Business cases written to survive contact with a CFO who has read a few
- Success criteria that name specific numbers, not generic outcomes
AI adoption and enablement
The pattern we see in most organizations right now: licenses purchased in 2024, usage metrics that look acceptable in aggregate but fall apart team-by-team, and a quiet sense in leadership that the investment isn't compounding the way the business case promised. The issue is almost never the tool. It's that the workflow wasn't redesigned to make the AI assistance feel natural in the flow of real work, training was treated as a one-time rollout event, and the people who would have measured the impact didn't have a meaningful baseline. We rebuild the parts that didn't get built the first time, and we measure adoption against numbers your CFO will recognize.
- Copilot and AI tool configuration, including the permissions and governance pieces most rollouts skip
- Workflow redesign around the way your teams actually do their work
- Hands-on training, follow-up, and change management for the people who'll use the tools daily
- Adoption tracking and value measurement against numbers your CFO will recognize
AI-powered engineering
The benchmarks the AI coding tools publish are clean, and the codebases they were measured on are clean too. Yours probably isn't. The right way to make an AI developer-tool investment in 2026 is to run a structured pilot in your environment, against your codebase, with your engineering standards, and compare the candidates head-to-head. That's most of what we do at this tier. We embed long enough to baseline productivity properly, run the pilots cleanly enough that the results survive scrutiny, and produce a recommendation that you can take to a CFO with a real number attached. When the pilot finishes, we stay through the integration work.
- Head-to-head pilots across the tools that actually compete in your stack (Copilot, Cursor, Claude Code, Cody, CodeRabbit)
- Productivity baselining done well enough to defend in a budget review
- AI-assisted software development for engineering work in flight during the pilot
- CI/CD and pipeline integration that survives the engineer who set it up moving teams
AI agent and platform development
The interesting enterprise AI capability worth building right now requires connecting an LLM to your operational systems — CRM, document repositories, ticketing, internal data warehouses, the workflow tools the business actually runs on. Off-the-shelf assistants don't reach those systems. Building the connections, the data layer, the orchestration, the permissions model, and the production infrastructure to keep all of it running is real software engineering, and it's the work we've spent the last two years getting good at. The Model Context Protocol matters here and we use it; we also know where it's still rough and what to build around it.
- AI agent platform architecture, including the security and permissions parts most demos skip
- MCP server development and configuration against your actual systems
- Connector and integration work across CRM, ERP, internal databases, and SaaS tools
- Multi-agent system design when the use case justifies the complexity, single-agent when it doesn't
- LLM orchestration and tool-use frameworks
- Retrieval-augmented generation pipelines tuned to your data, not a public benchmark
- Cloud AI infrastructure, API integration, and the governance layer security will eventually ask about
Three things that are true about every engagement.
Strategy and delivery are run by the same team.
The partner you meet at sale is the partner who owns the engagement six months in, and the senior engineers who'd evaluate a recommendation are the ones writing it. This isn't unusual in technology. It's surprisingly unusual in consulting.
We're not on any vendor's referral spreadsheet.
We have no commercial reason to prefer Azure OpenAI over Anthropic, Copilot over Cursor, or Snowflake over Databricks. The recommendations we give you are the ones we'd give a friend in your seat. Sometimes that's the cheapest option; often it isn't.
Most of the value shows up after we leave.
Adoption holds or it doesn't. Systems get the operational attention they need or they quietly atrophy. We stay engaged through that window, checking in, optimizing, and updating the operating model as the technology underneath shifts, which in this category happens roughly every quarter.
A real AI readiness assessment, not a sales tool dressed up as one.
Most "AI readiness" assessments you'll find online are lead capture in a different costume. Ours isn't. Twenty questions across four dimensions — strategy, data, talent, governance — with a 1-to-5 scale and an interpretation guide written so a leadership team can score it themselves on a Tuesday afternoon and have a useful internal conversation about the results without us in the room.
We hand it out because the firms that benefit from talking to us tend to be the ones scoring below 2.5 on at least one of the four dimensions. If you score above that across the board, you may not need a consulting partner at all, which is the kind of thing we'd rather tell you in advance.
What we build with.
The list below is what's in active rotation across current engagements. Categories will keep shifting; we'll update this as the market does. The principle behind the choices is consistent: whatever fits the work and the team, not what's on a partner spreadsheet.