Market research is getting its copilots—and now, its architects.

quantilope has rolled out a major update to its AI Research Partner, quinn, completing what it calls a fully integrated, end-to-end AI research workflow. The headline feature: quinn can now create and review comprehensive, methodologically sound research studies from scratch.

For a sector long dominated by manual setup, logic checks, and spreadsheet wrangling, that’s a bold claim.

From DIY to “Do-It-With-AI”

quantilope is positioning this release as more than incremental AI enhancement. The company says it marks a formal transition from traditional DIY research to what it calls “Do-It-With-AI” (DIA)—or, in branded shorthand, “Do-it-with-quinn.”

The shift reflects a broader trend across enterprise software: AI is no longer just summarizing outputs. It’s designing workflows.

With the update, quinn now supports the entire research lifecycle:

• Drafting studies from high-level objectives

• Structuring questionnaires using advanced methodologies

• Automatically validating logic and setup

• Conducting AI-powered analysis

• Generating automated reports

In other words, quinn moves from assistant to orchestrator.

AI as the Research “Nervous System”

At the core of the upgrade is what quantilope describes as advanced end-to-end AI integration. Quinn now maintains persistent context across the research journey—from initial study design through analysis and reporting.

That continuity is crucial.

Many AI tools in research today operate in silos: one for survey drafting, another for analysis, another for visualization. Context gets lost between steps. Errors creep in. Researchers spend time re-explaining objectives.

Quinn’s updated architecture aims to eliminate that fragmentation by acting as the platform’s “nervous system,” carrying intent and logic across stages.

The update also includes:

• Strengthened AI model performance

• Saved chat histories for contextual continuity

• Expanded dashboarding capabilities

• Direct integration within quantilope’s Editor

That Editor integration is particularly significant. Researchers can now convert high-level business objectives into structured questionnaires within minutes—using advanced methods—while quinn automatically reviews configurations to catch logic mistakes before launch.

For teams under tight timelines, that automation could cut hours—or days—of back-and-forth.

Real-Time Refinement Inside the Workflow

Beyond study creation, the update introduces real-time refinement tools.

New “quinn Action Buttons” allow one-click improvements to question phrasing, helping researchers fine-tune clarity and reduce bias. Meanwhile, persistent chat functionality lets users interrogate survey logic or request technical clarifications without leaving the build environment.

That conversational layer reflects a larger UX shift happening in enterprise platforms. Instead of navigating complex menus, users increasingly interact through dialogue—asking systems to explain, adjust, or optimize on demand.

In practical terms, it lowers the barrier to advanced methodologies. Researchers don’t need to manually configure every detail—they can collaborate with the AI to get there faster.

Productivity Gains—With a Human in Control

quantilope is careful to emphasize that quinn is “Human-Led, AI-Powered.” The positioning mirrors broader AI adoption narratives across enterprise software: augmentation over automation.

The company frames quinn as a master architect—handling structural rigor and execution—while researchers provide strategic context, brand nuance, and stakeholder considerations.

That balance matters in research, where methodological integrity and contextual understanding are critical.

According to quantilope’s leadership, the productivity shift is substantial. Instead of spending time on manual configuration and error-checking, researchers can focus on higher-level insight generation and strategic interpretation.

In a market where insights teams are often asked to do more with fewer resources, that productivity narrative is compelling.

Competitive Context: AI Arms Race in Insights

The consumer insights space has seen an AI surge over the past two years. Survey platforms, analytics vendors, and full-stack research solutions are racing to embed generative AI across their offerings.

But many tools still function as bolt-ons—AI summarizing findings after the fact, or suggesting edits without owning the process.

quantilope’s bet is that full lifecycle integration is the differentiator.

If quinn can reliably draft, validate, analyze, and report within one cohesive workflow, it could reduce the need for external scripting, manual QA, and third-party analysis tools.

The real test will be methodological depth. Enterprise research buyers won’t trade rigor for speed. If quinn consistently produces statistically sound studies while maintaining flexibility for customization, it could raise expectations for the category.

What This Means for Research Teams

For insights professionals, the implications are clear:

• Faster time from brief to field

• Fewer manual logic errors

• More iterative experimentation

• Greater focus on strategic storytelling

It also signals a philosophical shift. Research platforms are evolving from execution tools to collaborative intelligence systems.

If AI can shoulder structural complexity, researchers can concentrate on the harder part: asking better questions.

The Bottom Line

With this update, quantilope is aiming to redefine how enterprise research gets done. Quinn’s evolution from support tool to end-to-end workflow engine reflects a broader transformation across B2B tech—where AI is embedded deeply, not sprinkled on top.

The promise is ambitious: compress the research lifecycle without compromising methodological integrity.

If delivered consistently, “Do-It-With-AI” may not just be a slogan—it could become the default operating model for modern insights teams.

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