01 / The starting point
The problem to solve.
A research project needed usable interview material from a transcription service. Recurring automation and API failures stood between the original interviews and the structured outputs required by the research team.
02 / My contribution
Where I came in.
I recovered historical transcripts, built and adjusted Make workflows, investigated integration failures and delivered structured research data. A separate part of the engagement involved building and testing catalogue AI agents.
03 / Implementation
What I worked on.
- 01
Extraction and transformation workflows connecting the transcription service to the research output.
- 02
Troubleshooting across API and automation modules, including coordination with provider support and another consultant.
- 03
Agent prompt and behaviour refinement, scenario testing and QA feedback on team-built agents.
Separate a plausible AI answer from a usable deliverable. Workflows needed consistent structure and agents needed testing across different inputs.
04 / Delivery
Usable research data delivered.
Recovered interview material and structured outputs delivered for the research team, alongside integration fixes and tested AI-agent behaviour.
Delivered as a consulting engagement, in collaboration with the client team and integration providers.
Could this apply to your team?
A similar problem,
your own context.
Teams with a broken integration, inaccessible historical data or an AI workflow that still needs too much manual correction.
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