What we do, from raw data to a running program.
Most clients come to us with one specific goal. A forecast they can trust, data worth building on, a pilot ready to scale. Start with the one that matters most — the services below cover the whole path, and the rest connects when it needs to.
Data Foundations
Finding and fixing the data problems that block everything else.
Nobody decided that the CRM, the ERP, and billing should each hold a different customer ID. It happened over twenty years, one system purchase at a time. Now the model can't tell which customer is which, and no amount of clever math fixes that.
We scan your sources, find where they disagree, and fix the handful of problems standing in front of the first use cases. Not everything. Just what's in the way, starting with the ugliest one, because that one always takes three times the estimate.
- Data readiness scan across your source systems
- Master data and entity resolution
- Pipelines and integration
- Lineage, so a number can be traced back to where it came from
- Data architecture
Clean, connected data for the first projects, and a clear list of what to fix next.
AI Strategy & Portfolio
Sorting the ideas worth money from the ones that just sound good.
Somebody ran a workshop and it produced forty ideas. Ten are fine. Three are worth real money. A couple are impossible and nobody in the room wanted to say so.
We score them on value and feasibility, and feasibility includes the question everyone skips: will anyone actually open this thing. Then we sequence. Quick wins first, because you need something to show before you ask for more budget. One or two big ones in the middle, because those are what justify the program. The regulated monsters wait until the foundations can hold them.
- Opportunity discovery across the functions
- Value cases finance will sign
- Feasibility and data checks
- Portfolio sequencing
- Intake and stage gates
A short list of projects worth funding, with the numbers to back them up.
AI & Machine Learning Delivery
Building the models, and getting them into someone's actual workday.
The model is the easy part. The hard part is that the output lands in someone's Tuesday, and that person already has a way of doing the job, and their way works well enough.
So we sit with them. The output goes inside the tool they already open every morning, not a new portal with its own login. Usually the workflow around it has to change too, and that's most of the effort. Then we baseline the before, measure the after, and get finance to sign the number. That signature buys the next wave.
- Forecasting and prediction
- GenAI, copilots, document work
- Decision products and workflow redesign
- Baselines and measurement
- Production deployment, not a notebook
AI in production that people use daily, with results your finance team has verified.
Platform & MLOps
Enough platform to ship, and no more.
We've watched teams disappear for eighteen months to build something magnificent, and come back to find the business stopped waiting and bought three tools of its own.
So we build the smallest thing the first use cases need, and let real work pull the rest into being. What we're after is boring deployment. The first model is expensive. The tenth should take a week, cost a fraction, and not depend on anyone clever being free that month.
- Cloud and data platform, sized to now
- CI/CD for models
- Monitoring, drift, retraining
- Reusable pipelines and templates
- Cost control, before the bill surprises someone
A platform that makes each new model faster and cheaper to launch than the last.
Governance & AI Risk
Approval paths fast enough that people use them.
If the approved way is slower than the unapproved way, people paste customer data into a chatbot on their phone and you find out much later, or never.
We set up named owners, and model risk sized to the stakes, so a demand forecast doesn't carry the same paperwork as a credit decision. And we keep a value ledger, because the day nobody can prove the program pays for itself is the day the funding quietly dries up.
- Responsible AI policy people can follow
- Model risk management
- EU AI Act, SR 11-7, and whatever your regulator cares about
- Privacy and access
- Value tracking finance trusts
Approval processes people can live with, and a running record of what the program has earned.
Knowledge Preservation
Capturing the judgment your business can't afford to lose.
Your best scheduler retires next spring. Thirty years of knowing which supplier promise is credible, which machine sound matters, and when the standard procedure should be ignored walks out the door with her, and none of it is written down.
We sit with the people who hold that judgment and capture how they decide — the rules, the exceptions, the tells. Then we turn it into something the next person can use: structured knowledge, guided decisions, an assistant that shows its reasoning. AI makes this easier than it used to be. It doesn't make it automatic.
- Expert interviews and observation
- Decision rules and exception capture
- Case and precedent libraries
- Searchable knowledge systems
- Expert assistants and guided decisions
What your best people know, captured so it stays with the company.
Operating Model & Handover
Standing up your team, then getting out of the way.
A small center holds the platform, the standards, and the specialists who are hard to hire. Everyone else sits in the business units, close enough to the work to know when a number looks wrong. Every delivery pod includes someone who genuinely knows the business. We don't bend on that one.
Then ownership moves. We run the first use case while your people watch. We run the second one together. You run the third and we mostly keep quiet. By the end you can deliver the fortieth without calling us, which is the outcome we're actually selling.
- AI office design
- Roles, pods, hiring
- Ways of working and intake
- Pathfinder handover
- Change management and adoption
A team that can build the next one without us.
Three ways to engage.
Most people start with the sprint and decide from there.
Readiness & Strategy Sprint
We look at the data, find where the money is leaking, and tell you what to build first. The portfolio and roadmap are yours whether you hire us for the build or not. Some clients take it in-house from here. That's a fine outcome.
Program Build
Foundations, the first live use cases, and your team, all built in parallel. Your people are in the room from week one and running it by the end. This is the main thing we do.
Advisory Retainer
For a program already going. Gate reviews, architecture calls, board prep, and someone from outside who will say the pilot isn't working when everyone in the room already knows it.
Focused Intervention
One specific problem: a stalled use case, a governance model nobody follows, a scaling effort that keeps slipping. We come in, fix it with your team, and get out.
Where we've done this before.
A plant, a logistics network, a utility, a bank. The path is roughly the same each time. What changes is where the money sits, which data fights hardest, and what the culture pushes back on. We tell you the differences as we hit them, instead of pretending we've seen your exact situation before.
Not sure which of these you need?
That's normal, and a call is the fastest way to sort it out. No deck. Bring the thing that's been bugging you.