Strategy tied to a real operating workflow
Clear priorities, evidence, controls, and next steps—not a generic list of AI ideas.
Workflow and readiness audit
Current process, bottlenecks, data, systems, exceptions, owners, and measurable baseline.
Opportunity prioritization
Value, feasibility, risk, adoption, and time-to-evidence scored across candidate use cases.
Prototype and evaluation
A bounded proof using representative data and an explicit quality and safety test set.
Governance and operating model
Ownership, review, permissions, vendor choices, monitoring, and change management.
AI consulting that ends in an executable decision
A practical path from scattered AI ideas to one validated use case, a responsible architecture, and a credible delivery plan.
Audit the work as it actually happens
We interview the people doing the work, map systems and handoffs, review available data, and measure time, error, delay, and rework. Unofficial spreadsheets and manual exceptions are included because they often determine whether an AI solution can succeed.
- Workflow and stakeholder mapping
- Data availability and quality
- Security, privacy, and compliance constraints
- Baseline cost, speed, and quality metrics
Prototype the riskiest assumption
A prototype should answer a decision: can the system reach the required quality, integrate with the workflow, and create enough value to justify production? We use representative cases and define pass, fail, and human-escalation criteria before testing.
Leave with a production roadmap
The roadmap covers scope, architecture, vendors, data work, permissions, evaluation, operating ownership, timeline, cost ranges, and adoption. If the evidence says not to build, that is also a useful outcome.