Services

What we do.

Most clients come to us with a single problem: a forecast the business does not trust, or a pilot that never reached production. Engagements usually begin with one of the services below and add the others as the work requires.

Data Foundations

Data readiness, master data, and the integration work the first use cases depend on.

Typical work

Readiness scan across source systems
Master data and entity resolution
Pipelines and integration
Lineage back to the source system
Data architecture

Output

Reconciled data for the first use cases, the integrations that feed them, and a ranked list of what to fix next.

How this usually shows up

Customer, product, and supplier records are defined differently in the CRM, the ERP, and the billing system. Most clients find that out when the first use case needs a number that reconciles across all three.

The data & AI foundation

AI Strategy & Portfolio

Ranking AI opportunities by value, feasibility, and data readiness, then sequencing the build.

Typical work

Opportunity discovery across functions
Value cases finance will sign
Feasibility and data checks
Portfolio sequencing
Intake and stage gates

Output

A ranked portfolio, a value case for each opportunity that finance has reviewed, and a delivery sequence.

How this usually shows up

Most companies have more AI ideas than they can fund or staff, and the list mixes opportunities that carry real financial value with ones the available data cannot support. The early use cases still have to be delivered while the foundations for the larger ones are being built.

AI & Machine Learning Delivery

Building AI applications and putting them into production inside the workflows people already use.

Typical work

Forecasting and prediction
GenAI, copilots, document work
Decision products and workflow redesign
Baselines and measurement
Deployment into production systems

Output

AI applications running in production inside your existing systems, with results your finance team has reviewed.

How this usually shows up

Models that perform well in testing are often left unused, because the output arrives outside the system where the work happens or the process around it never changed. It comes up when a pilot is supposed to become part of the operation.

Platform & MLOps

The platform, pipelines, and deployment process the current use cases need.

Typical work

Cloud and data platform sized to demand
CI/CD for models
Monitoring, drift, retraining
Reusable pipelines and templates
Cost monitoring and controls

Output

Shared pipelines, deployment, and monitoring that your engineering team runs.

How this usually shows up

The first model a company builds is usually put together by hand. Without shared pipelines, deployment, and monitoring, the tenth costs about the same, because nothing from the first nine can be reused.

Governance & AI Risk

Policy, approval paths, and model risk controls sized to what each system decides.

Typical work

Responsible AI policy people can follow
Model risk management
EU AI Act, SR 11-7, and your sector regulator
Privacy and access
Value tracking finance reviews

Output

Policy, named owners, model risk controls, and a record of what the program has cost and returned.

How this usually shows up

Once AI moves past a single pilot, legal, risk, and audit become part of every release. Reviewing a demand forecast to the same standard as a credit decision stalls most of the portfolio, and approval that takes months pushes teams toward consumer tools.

Knowledge Preservation

Capturing how experienced people make decisions, in a form the next person can use.

Typical work

Expert interviews and observation
Decision rules and exception capture
Case and precedent libraries
Searchable knowledge systems
Expert assistants and guided decisions

Output

A written record of how your experienced people decide, and a system the next person can work from.

How this usually shows up

Decision quality in planning, scheduling, maintenance, and underwriting often rests on a few people who have done the job for decades. What they know is not in the ERP or the procedure manual: which supplier commitments hold under pressure, and when the standard process should be overridden.

Operating Model & Handover

Setting up the team, the standards, and the way of working that runs AI after we leave.

Typical work

AI office design
Roles, pods, hiring
Ways of working and intake
Pathfinder handover
Change management and adoption

Output

An internal team that can deliver the next use case without us, and the documentation to support it.

How this usually shows up

A small central group holds the platform, the standards, and the specialist roles that are hard to hire. Delivery teams sit in the business units, and each includes someone who knows the business well enough to tell when a number is wrong.

How We Work

Engagement models.

Most clients start with the readiness and strategy sprint and decide on the rest from there.

Model	Best used for	Typical duration
Readiness & Strategy Sprint	Ranking the portfolio and assessing readiness	4–12 weeks
Program Build	Foundations, production delivery, and handover	12–24 months
Advisory Retainer	A program already underway	Ongoing
Focused Intervention	One problem that has stalled	Scoped to the problem

The portfolio and roadmap are yours whether or not you hire us for the build.

Industries

Industries we work in.

Primary experience

Manufacturing · Logistics · Utilities · Industrial · Banking · Investment Banking · Fintech · Hi-Tech · Software · Retail

What changes by sector is which source systems hold the data that matters and what the regulator requires.

Start with one problem.

Tell us what you are trying to fix, and we will say which of these services it involves and where we would start.

Book a discovery call