We build what the strategy describes.
Data strategy, AI systems, and the platforms underneath them — scoped and delivered by one senior team, from the first decision through to the thing running in production.
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 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.
Healthcare operating context
A behavioural-health administration floor — the operating context, not the software.
16:9 · 2400×1350 min
Prompt and stock terms in DataProducts_Image_Sourcing_Brief.xlsx
Alt: “A healthcare administration floor, the operating context the workforce analytics platform serves”
Behavioral health · Business intelligence
HR decisions were being made on weeks-old spreadsheets
Thresholds' people team was pulling from disconnected systems and reporting on a manual cycle. We built an analytics layer over their UKG ERP, so leaders see live workforce data instead of a reconstruction of last month.
Read the story →
Physical operating environment
A real production line. Scale and order — the physical consequence of a forecast.
16:9 · 2400×1350 min
Prompt and stock terms in DataProducts_Image_Sourcing_Brief.xlsx
Alt: “A modern production floor, the operation the forecasting model schedules”
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 →
A working session in progress
Real participants, shot from behind. No identifiable faces.
16:9 · 2400×1350 min
Prompt and stock terms in DataProducts_Image_Sourcing_Brief.xlsx
Alt: “A data literacy working session in progress”
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.
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.
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.
Editorial opener for the flagship piece
The gap between a pilot that demos and a system that survives production. Magazine opener.
3:4 portrait · 1600×2133 min
Prompt and stock terms in DataProducts_Image_Sourcing_Brief.xlsx
Alt: “An unfinished span disappearing into fog — the gap between a pilot and production”
AI policy
AI nationalism: why governments are building sovereign AI infrastructure
Compute is becoming statecraft. What that means for enterprises whose data crosses borders. ReadData sovereignty
Who owns the moon? The lunar data race has already begun
Jurisdiction, ownership and who holds the record — more relevant to terrestrial data governance than it looks. ReadExecutive Bytes lands monthly. Short, opinionated, written for the person who has to make the decision rather than admire it.
One problem rarely belongs to one practice.
Data IQ™ is how we sequence 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.
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.
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.
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, GenAI integrated.
05
Enablement
Training, literacy, governance operations, advisory.
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 to prioritize?
A maturity audit with stakeholder work, architecture review and investment scenarios, ending in a board-ready recommendation. A paid advisory engagement — distinct from the free five-minute tool.
AI & Data Assessment Sprint
See the sprint →
Know the use case but need to prove it?
From use-case definition to a working agent or copilot on your data, in your cloud, with accuracy and cost measured. Production-grade, not a slide deck.
GenAI Pilot
See what you get →
Data can't support what you want to build?
A lakehouse proof-of-concept on your data, with measurable performance benchmarks — so the foundation question is settled before anything is built on top of it.
Data Platform Pilot
See the approach →
Timelines and scope are set in the first conversation. All three run as bounded programs rather than open-ended engagements.
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.
One immersive operating environment
Deliberately one image, not six sector thumbnails.
4:5 portrait · 1600×2000 min
Prompt and stock terms in DataProducts_Image_Sourcing_Brief.xlsx
Alt: “The layered interior of a modern institutional building”
We work across the technology you already run.
Model-neutral by policy — we hold no exclusive incentives. See the full partner ecosystem
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 strategy call is a conversation 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, a GenAI Pilot or a Data Platform Pilot 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 strategy call is a conversation about your problem, not a pitch deck. If the honest answer is that you do not need us yet, that is the answer you will get.
Not sure where to start? Five minutes to a readiness score. No form.