We build what the strategy describes.
We help leaders decide which AI and data investments are worth making, what they will cost, who owns the risk, and what has to change to put them into production. Then the same senior team builds it.
Senior-led throughout · Fixed scope · Your cloud, your data
A demo needs three things. Production needs eleven.
Pilots almost never fail on the model. They fail on the unglamorous scaffolding around it — real data, an evaluation harness, a governance path, a cost model — which nobody prices or funds until it blocks a launch.
IBM TechXchange · Standing-room session · Published in full
Most teams stall in a place they did not expect.
Five questions, thirty seconds, nothing sent anywhere. You will know whether your real blocker is the data foundation, the direction, the build, or whether anyone actually uses what you ship.
No form · No email gate · Instant result
1 / 3
Three systems, live, with the numbers attached.
Not pilots, not prototypes. Each one replaced a process people were doing by hand, and each one is still running. Every number here is measured, not modeled.
Behavioral health · Business intelligence
HR decisions were being made on weeks-old spreadsheets
A multi-site behavioral health provider’s people team was pulling from disconnected systems and reporting on a manual cycle. We built an analytics layer over their core people system, so leaders see live workforce data instead of a reconstruction of last month.
Read the story →
Global industrial manufacturer · Machine learning
Production was being scheduled on intuition and averages
A global manufacturer was absorbing the cost of overruns and missed demand because planning ran on historical rules of thumb. We built a forecasting model that learns continuously and feeds straight into the operations workflow.
Read the story →
Regional health system · AI & data literacy
The dashboards existed. Nobody opened them.
A health system had the tools and none of the confidence. The problem was never the software. A structured literacy program across departments turned passive report recipients into people who now generate their own insight.
Read the story →
The full case studies carry the brief, the architecture and the method behind each figure. Including what did not work first time.
Before another AI or data investment, five questions should have answers.
A CFO, a board and a risk committee ask all five, usually after the budget is committed. We answer them first, then build what the answers call for.
Where would you actually stall?
Answer honestly — nothing is sent anywhere and there is no form. You will get a read in about thirty seconds, and it will tell you which of the eight practices is your real starting point.
This is a rough read, not a diagnosis. The free AI Readiness Assessment scores six dimensions properly and returns a prioritized list — about five minutes, and no consultant needed to start.
One problem rarely belongs to one practice.
Data IQ™ is how we organize the work: decide what is worth building, build it, secure it, and make it stick. Our eight practices map onto those four stages, and the same senior team carries you across all of them. That continuity is the whole argument for hiring one firm instead of four.
These are groupings, not steps in a queue. Most engagements start in one stage and pull in two more, and Secure runs through all four rather than waiting its turn.
01 — Set direction
Decide what is worth building before anyone builds it.02 — Build
The platform, the models, and the interfaces people actually touch.03 — Secure
The exposure that arrives with the capability.04 — Make it stick
A system nobody can use is a system nobody keeps.Every practice page carries its own capabilities, proof and entry point. Most engagements start in one and pull in two more.
Start where the uncertainty is smallest.
These are paid, fixed-scope engagements, each ending in a decision you can defend, including the decision not to proceed. If you would rather start free, the five-minute AI Readiness Assessment is the self-service version.
Not sure what is worth funding?
Six readiness dimensions scored against your own systems, a gap register, an order-of-magnitude view of what it will cost, and prioritized next steps. Evidence you can take to a board.
AI & Data Assessment Sprint
See the Sprint →
Know the use case but need to prove it?
One use case taken from workflow to a running system on your data, in your cloud, with accuracy and unit economics measured and a scale-or-stop decision at the end.
AI & Agent Pilot
See the Pilot →
Data can’t support what you want to build?
A diagnostic of what to keep, fix and build, then a working foundation on the platform you already own, so the data question is settled before anything is built on top of it.
Data Platform Accelerator
See the Accelerator →
Bought by data and technology leaders, by IT directors starting from a standing start, and by business owners with one number to move.
Six starter services, each with a fixed scope and a fee agreed in writing before any work starts. Governance, security and executive workshops are among them.
Senior from the first question to the working system.
Phase boundaries are where engagements lose their context — a strategy team leaves, a delivery team arrives, and the reasoning behind every decision has to be reconstructed. We do not run it that way.
Senior people stay on the work
The senior practitioners who frame the problem remain accountable through delivery. The person who understood it in week one is still answerable for it in week twelve.
Strategy and engineering live together
Strategy and delivery remain one engagement. The team that writes your roadmap builds the system it describes, so architecture, engineering and deployment share a single thread of reasoning.
Data treated as an operating asset
Your data gets ownership, lifecycle management and measurable value, the way software does. That turns a one-off project into an asset that keeps paying rather than one that decays after handover.
The four Data IQ™ stages above describe what the work is. These five describe when it happens inside a single engagement. Security and governance are not among them because they are not a step: they run across all five.
01
Assessment
Maturity audit, architecture review, stakeholder interviews.
02
Strategy
Roadmap, governance design, business case validation.
03
Architecture & build
Platform, pipelines, ML infrastructure, integrations.
04
Deployment
Models live, agents configured, generative AI integrated.
05
Enablement
Training, literacy, governance operations, advisory.
Domain knowledge, not just technical capability.
Sectors where we already understand the data model, regulatory environment and common failure modes, so the engagement starts with the problem rather than an industry primer.
We work across the technology you already run.
Partner
Microsoft
Partner
Databricks
Platform we build on
Snowflake
Platform we build on
AWS
Platform we build on
Model-neutral by policy: the recommendation follows your data and your footprint, not a quota.
How we work with partners Selling one of these platforms? When to bring us in
What we are arguing about this month.
Published positions, not recycled vendor content.
Why AI projects fail after the pilot — and how to scale
A standing-room session at IBM TechXchange. The finding: pilots rarely fail on the technology. They fail on the unglamorous prerequisites — data readiness, governance, adoption planning and unit economics — which nobody budgets for until they block a launch.
AI economics
Building the AI business case
What it is worth, what it costs to build and to run, and the number that decides whether it gets funded. ReadGovernance and accountability
NIST AI RMF in practice
Who owns the outcome, what evidence to keep, and how a review path works day to day. ReadExecutive Bytes. Short, opinionated, written for the person who has to make the decision rather than admire it.
One table, a short guest list, one hard question.
Invitation-only dinners for senior executives, held city by city. No slides, no panel, nobody pitching. Request an invitation, nominate a peer, or tell us which city should be next.
Three we get asked first.
Where should we start if we are not sure what we need?
Start with the free AI Readiness Assessment. It takes about five minutes, scores your organization across the dimensions that decide whether a data or AI build succeeds, and returns a prioritized list of what to fix first. If you would rather talk it through, a first conversation with a senior advisor is about the work, not a pitch.
How do you work with our existing team and technology?
In your cloud, on your accounts, with your data staying in your environment — and what we build is yours to run. We are model-neutral by policy and hold no exclusive vendor incentives, so the architecture recommendation follows your data sensitivity, volume and existing footprint rather than a partner quota. Where your team needs more depth to keep pace, we extend it with senior specialists rather than replacing anyone.
What does a first engagement look like?
Fixed scope, fixed fee, and a defined decision at the end. Most clients begin with an Assessment Sprint, an AI & Agent Pilot or a Data Platform Accelerator depending on where their uncertainty sits, and most start within two to three weeks of a signed scope. Larger platform engagements are scoped individually, but we would normally run a bounded program first so the scope rests on evidence rather than assumption.
Procurement, security review, data handling and contracting are covered in the full FAQ.
Tell us what is not working. We will tell you if we can fix it.
A first conversation is about your decision, not our deck. If the honest answer is that it is not worth funding yet, or that you do not need us, that is the answer you will get.
Not sure where to start? Five minutes to a readiness score. No form.
