Beyond analysis: How one researcher used flowres.io as a research copilot

The challenge

Idil Cakim, Founder of a US-based audience intelligence firm, was working with 20+ interviews with C-suite executives, each around 30 minutes long. The task was to analyse the interviews collectively, finding threads across the full dataset, testing hypotheses, identifying nuances and supporting them with the right quotes. Like often, there loomed a tight deadline. The output needed to feed into both a report and a white paper, leaving little room for the traditional cycle of transcription, manual comparison and searching through large tables of data.

Where flowres.io fit

flowres.io became a layer between the raw interview data and the final outputs. Instead of working through interviews one at a time, the researcher could probe the entire corpus with the same questions, surface aggregate trends and then drill into the underlying detail. For her, this is what made flowres.io stand apart from LLMs that she would occasionally lean on, for analysis support - “I just loved the ability to capture the aggregate trends.” - Idil Cakim, Founder at IrisFlex

That made it easier to move from broad patterns to nuance and from a finding to the evidence behind it. The researcher could also return to the data when she needed a specific example or quote, using flowres.io to explore her hypotheses, rather than handing over the analytical judgement to AI. - “I'm pretty much in the driver's seat... in the process as a writer, as an analyst.” - Idil Cakim

This was particularly useful when writing. flowres.io helped pull together relevant information and quotes as the narrative developed, while the researcher remained responsible for interpretation and context.

Outcomes & impact

The biggest impact was speed without giving up analytical depth. Working across 20+ interviews became easier than manually searching and comparing transcripts, helping the researcher get to the story faster and uncover things she may otherwise have missed. - “It allowed me to probably also catch at least two/ three things that I wouldn’t have thought of myself.” - Idil Cakim

The workflow also extended beyond analysis: the researcher used the outputs to write both a report and a white paper. “It really helped with the narrative in terms of just providing the substance that I needed to weave in.” - Idil Cakim

For her, flowres.io worked less like an automated analyst and more like a research copilot; helping her interrogate a larger body of data, find evidence and develop the story while keeping the researcher firmly in control.

Click here to know more about Idil's experience