We focus on operational friction your team can recognize and measure. The starting point is the work as it happens today—not a predetermined platform.
Information arrives by email, forms, spreadsheets, or PDFs and is repeatedly entered into systems by hand.
Customer-facing work slows down while information is collected, checked, approved, and chased across teams.
The same facts live in several applications, and reporting depends on manual exports, reconciliation, and explanation.
A promising AI use case needs to be tested against actual value, risk, data, and the people who will own it.
Most engagements begin with a fixed-scope Look at one workflow. You receive useful findings before deciding whether implementation makes sense.
Examine one high-friction workflow using interviews, representative records, and the systems already in use.
You get → a current-state mapCompare practical options by cost, expected result, risk, and confidence. Decide what is worth doing now.
You get → a costed planImplement against representative data and clear acceptance criteria. See the system working, not a slide about it.
You get → a working systemDocument the system, train its owners, and agree how it will be supported. Ownership is part of delivery.
You get → runbook + ownerThe Look stands on its own. Look + Choose is fixed-scope and fixed-fee, agreed before work begins. Implementation is optional and quoted separately from the findings.
Recent work spans AI and data-platform delivery, product and adoption strategy, integration research, operational design, and business systems. Engagements are senior-led from the first question through delivery.
Delivery accountability across Microsoft Foundry, Databricks Genie Spaces, and Customer 360 discovery for regulated organizations. Multidisciplinary teams and executive stakeholders, delivered through another consultancy.
Developed a three-tier approach covering individual employee use, product decisions, and machine-to-machine accessibility. The work connected training, product value, risk, and technical implementation.
Worked with a technical team on proof-of-concept research into integration-template builders that used system documentation to create basic connectors.
Designed CRM workflows, disaster-recovery procedures, governance plans, and practical Odoo implementations supporting sales, purchasing, and banking operations.
Client identities are omitted where work was confidential or delivered through another consultancy.
Understand the workflow, evidence, exceptions, and people before choosing technology.
Begin with one useful, reversible workflow rather than an organization-wide program.
A workflow should work with the records and exceptions the team actually handles.
Build on current systems and replace software only when the case is clear.
Evaluate access, data handling, error paths, and oversight while the solution is still easy to change.
Documentation, training, monitoring, and a named owner are part of implementation.
03 / Next step
Thirty minutes to understand the workflow and decide whether a focused Look is useful.
Book a 30-minute call →