I find where AI will create value, and build the workflow that proves it.

Freeport Metrics had an AI voice-based solution on the table as an internal bet. The discovery ran before a line of production code was written.

Diagnose

Initiated the discovery before any build: reached out to B2B clients, invited them to discovery calls, and ran 7 interviews diagnosing their real needs, problems, and bottlenecks around AI voice implementations.

Design

Turned conversations into evidence: assumption mapping and an opportunity solution tree converted the interviews into testable requirements and a clear picture of client readiness.

Measure

The evidence showed clients were not ready to adopt. Recommended a no-go, and the project stopped before build. The company avoided building a product without customers, and the discovery playbook stayed.

Freeport Metrics, an American-Polish software consultancy, was growing faster than its quality function. Six concurrent client projects, no shared processes, and rising defects.

Diagnose

4 QA specialists, no standardized processes, no centralized knowledge, no visibility across 6 concurrent projects. Defects climbing with every hire.

Design

Built the infrastructure before scaling the headcount: centralized tribal knowledge, designed a competence matrix, stood up a portfolio-wide dashboard. Replaced feedback with feedforward sessions.

Measure

Defects down 50%. Team scaled to 16+. Team-initiated improvements up 30%. Over 20 process changes shipped through retrospectives.

A language school owner running two education businesses with no structured way to figure out where AI would help. Data scattered across files, phone contacts, and memory.

Diagnose

No centralized client database. Compiling a full list took an hour across multiple sources. Trial-lesson conversion unmeasured. Marketing spend untracked.

Design

Designed a 28-question diagnostic across seven business dimensions. The intake surfaced that the highest-ROI intervention was centralizing client data. Set up the owner's AI working environment in Claude Cowork.

Measure

A single client database as the source of truth, and the owner's AI working environment set up in Claude Cowork on top of it. The roadmap deliberately sequenced AI after the data foundation: three interventions ranked by measurable return, not by what was trendiest.

Beta cohort of a done-with-you AI implementation practice. Clients came wanting an AI content system built for their businesses.

Diagnose

Every client said the same thing: help me create content faster. A structured diagnostic intake before building revealed that the majority had unfixed strategic foundations. AI tools on top would produce faster content that still didn't convert.

Design

Pivoted mid-cohort. Diagnosed each client individually. Built what they actually needed (offer clarity, research systems, voice-preserving quality gates) before wiring the AI tool on top.

Measure

3 of 4 clients arrived asking for the wrong solution. They needed strategic diagnosis before any AI tool would help. Caught by intake. Restructured the offer and built the diagnostic framework now used in every engagement.

Bold Poland (formerly Zety), a global career-services platform, ran four multilingual products on manual regression. Quality incidents kept reaching production.

Diagnose

Manual regression could not keep pace across four multilingual services, and incidents kept slipping through. The open question was which flows to automate first, out of all the ones that mattered.

Design

Built a risk-based automation strategy in Selenium with Python: ranked flows by business risk and automated the high-risk paths first. Readiness checklists and standardized defect management backed up the coverage as it grew.

Measure

Quality incidents dropped 40%, 100% of high-risk scenarios were automated, and regression coverage rose across all four services. Automate by risk, not by coverage vanity: the same logic now applied to choosing which AI use cases are worth automating.