Simulated research, built on real Gen Z conversations

Keplar’s biggest differentiator is the AI moderator itself. It’s the most natural, conversational voice AI interviewer in the industry, probing, following up, and adapting in real time with a tone and pacing indistinguishable from a skilled human moderator. That natural back-and-forth is what gets participants to actually open up, surfacing the honest stories and nuance that drive real business decisions, not the shallow, scripted answers you get from a chatbot or survey.

Beyond the moderator, Keplar is the only platform combining primary research and simulated testing in one place, backed by a full-service forward-deployed researcher option when you want it handled end-to-end. And it’s built by people who understand the problem from both sides: founded by a former Google speech and voice AI researcher working alongside veteran consumer insights leaders, so every engagement starts with your business question, not a product demo.

Keplar supports four sourcing methods depending on what fits your project: our own vetted panel (with built-in fraud and bot prevention), bringing your own panel or customer list, a public link you can distribute anywhere, or sending invitations directly through the Keplar platform via email.

Every study you run in Keplar feeds a growing, indexed repository, so your research doesn’t expire the moment a report ships. You can go back and requery or remine past studies for new questions at any time, and that same repository is what powers simulated testing.

Simulated testing is really an extension of the analysis layer, more like advanced analytics than a new research method. It lets you run concept and message tests based on real consumer voices already in the platform, not “digital twins” or “synthetic panels.” It’s evidence-backed and grounded in real data, designed as a secure, fast, iterative sandbox for early-stage innovation and ideation. Instead of commissioning new fieldwork every time you want to pressure-test a hypothesis, you can extend the shelf-life of research you’ve already run and get directionally useful signal in minutes.

Everything is auditable and traces back to a real consumer verbatim. No finding can appear in a report without a traceable source, so outputs are grounded in actual evidence rather than model priors, and when confidence is limited the system says “I don’t know” rather than speculating.

On the analysis side, every conversation is semantically coded by the AI so it’s easy to search and cut by theme, market, or demo. But counting and quantitative aggregation is handled by traditional math and statistical software, not the LLM itself, since LLMs have a well-documented weak spot with precise counting. That combination gives you the best of both: AI-powered semantic search and theming, with rigorous, accurate math underneath.

Guardrailing isn’t a bolt-on feature. It’s foundational to how Keplar is built. The voice moderator operates under four layers: model-level alignment (constrained by system-level instructions scoped to study objectives, not a general-purpose assistant), principled prompting (evidence-backed rules and objection-handling for edge cases), topic redirection (gracefully steering away from sensitive or off-topic areas, with a graceful exit protocol for distress or abusive language in development), and post-call quality evaluation (every conversation scored for verbosity, length, and on-topic fit, with rejected responses never billed).

On the analysis side, the agentic layer has its own governance: misuse prevention (an orchestration layer blocks out-of-scope queries like statistical significance testing before they reach the evidence base), evidence integrity (AI outputs never re-enter the evidence corpus, preventing synthetic drift), full client control and audit trail over the prompting frameworks, and prompt governance that flags attempts to work around guardrails rather than silently complying.