Data Capture is a core workflow on the Primary Research Platform

Remove the manual layer after the call

Most teams already do Data Capture, just manually. They use a discussion guide, re-listen to recordings, map answers back to questions, pull quotes, and rebuild a grid usable for clients or internal stakeholders. It is necessary work, but it is slow and scales poorly.

Techsperts Data Capture turns that same workflow into something repeatable. Calls become structured outputs quickly: a Q&A grid built from your discussion guide, with evidence attached so it’s easy to validate, share, and reuse.

It shifts where your team spends its time. Less effort goes into compiling, formatting, and re-checking. More time goes into synthesis, recommendations, and making decisions with confidence.

What customers are seeing

When Data Capture is in the workflow, teams move faster without trading off confidence. The proof isn’t 'AI is faster.' It’s what teams do with the time they get back, shippingmore research, handling more workstreams, and keeping quality high under deadline pressure.

We’re seeing results like:

~50% efficiency gains on post‑call analysis and delivery work

90+ minutes saved per interview that can be reinvested into synthesis and work that only you can deliver

Insights ready for client deliverables in record time, because outputs are structured and evidence‑linked from the start

If you want the details behind those numbers, you can explore the customer stories linked at the end of this article.

Why evidence-linked output changes everything

Speed is easy to claim, but trusting the output is the hard part, and in PMR, trust is what determines how fast work actually moves.

Most delays don’t come from the first draft of the analysis. They come from stakeholders asking for proof, teams re-opening recordings, scanning transcripts, re-pulling quotes, and then reformatting everything to fit different internal templates. One person wants the exact wording. Another wants the broader context. Someone else wants a clean quote for a slide. And suddenly the 'quick grid' becomes a multi-day loop of verification and repackaging.

Evidence-linked output changes that dynamic, because the proof is cited directly to the source. Each answer has a direct line back to the supporting material, so reviewers can validate in-line without sending the researcher back into the source files. The result isn’t just a faster grid. It’s a faster review cycle, with fewer 'prove it' threads, fewer rewrites to rebuild confidence, and fewer last-minute scrambles before client or stakeholder deadlines.

This is especially valuable when research is shared beyond the immediate project team. The broader the audience, the higher the scrutiny, and more importantly, it is essential that every insight is defensible without extra manual work.

Part of a Primary Research Platform

Data Capture isn’t a standalone feature. It’s a core workflow on the Primary Research Platform, turning raw conversations into usable, shareable outputs.

The broader aim is simple - reduce the fragmentation that slows PMR down. Calls happen in one place, notes live in another, grids live in a third, and the final output gets rebuilt each time it needs to move to a new stakeholder group. A platform approach means fewer handoffs, less duplication, and more continuity from research initiation through to decision-ready output.

The broader aim is simple: reduce the fragmentation that slows PMR down. Calls happen in one place, notes live in another, and grids live somewhere else. Then teams spend time translating, formatting, and re-validating the same insight as it moves between stakeholder groups. A platform approach means fewer handoffs, less duplication, and more continuity from research initiation through to decision-ready output.

It’s also how insight starts to compound. When outputs are structured and stored consistently, teams can reference, compare, and build on previous work instead of starting from scratch every time. That’s not a content library for the sake of it. It’s a practical way to increase throughput quarter after quarter.

What 'self-serve' means in practice

Self-serve is about control and timing. It means your team can run Data Capture as and when the work is needed, not when someone has time to do the admin around it.

In practice, it’s designed to fit the way PMR teams already work:

• If you already have calls recorded, no matter which provider you used, you can start from those.
• If you’re working from a discussion guide or question set, you can use that to auto-structure the output.
• If you need something stakeholder-ready, you can export the grid and move it straight into your internal workflow.

That’s what makes it usable at scale. When the workflow is easy to run, it becomes repeatable across projects and teams. You don’t just save time once. You remove the same manual steps from every project going forward.

And that’s where the real impact shows up as more workstreams can be handled in parallel, more research is delivered with the same team, and more time is reinvested in synthesis and recommendations instead of formatting and re-checking.
s to give your team time back for the work that actually creates value.

If you want to see it in action, start with one recent recording and your discussion guide or question set today: Start your Data Capture