Cashew Research, a Calgary-based startup, is gunning for the $90-billion market-research industry with an AI-powered platform that promises to compress timelines, cut costs and keep humans in the feedback loop. The company constructs research plans and surveys using generative models, deploys them to real respondents and then leverages AI to turn the results into reports ready for decisions.
A Hybrid Strategy Torn Between Speed and Quality
Instead of supplementing analysts, Cashew automates the routine mechanics of insights work: drafting questionnaires, programming surveys, cleansing data and summarizing open-ended responses. A researcher still controls methodology and sample design, and every project features original human data rather than reused synthetic respondents. The outcome: a workflow designed to convert weeks of work to days — not by ripping out the guardrails that marketers lean on, but rather by becoming more akin to an express lane.
Graves had spent over a decade moving through traditional research and co-founded the company (with COO Rose Wong) in 2023. They started with a focus on consumer packaged goods — food and beverage in particular — where concept tests, flavor preference and tagline resonance studies are common — and time-sensitive. Those are typical use cases where a delay can mess up a launch window or bite into media spend.
Through templating best-practice designs and AI-driven fast coding of open-ends, Cashew aims to bring self-serve analysis that looks like a senior researcher wrote it — not a chat transcript. For brand teams, it’s immediate drops modded by demo or geo, fast lift on messaging and obvious next steps as opposed to straight tables and jargon-filled decks.
Why The Insights Industry Is Ready For Automation
The global insights sector, which is continuously rated and benchmarked by the likes of ESOMAR and Statista, has for a long time been divided between heavyweight incumbents — the likes of Kantar, Ipsos, NielsenIQ, Qualtrics — and a long tail of agencies. The fundamental grievance from buyers is the same: projects are taking too long, costing too much and all too often stalling at analysis. AI is reshaping that calculus. Forrester has observed that generative models accelerate questionnaire design, open-end coding and report writing as long as expert guidance is utilized to avoid hallucinations and maintain methodological rigor.
Cashew’s pitch fits squarely within that gap. Automation drives down the per-study cost, and that has in turn created an opening for small and mid-sized brands which historically didn’t have budgets for full-service research. At the same time, keeping humans in the loop is an answer to a rising skepticism of fully automated “insights” that never touch a real respondent. Concern about online sample quality has been ever-present; the demand for excellent validation and fraud prevention safeguards is not vanishing.
Building a Data Flywheel Alongside Guardrails
Beyond speed, Cashew is banking on a proprietary data advantage. It aggregates and anonymizes the results of client projects into an internal database, which in turn form benchmarks that can guide future studies and make model prompts better. In time, that flywheel might be used to predict likely outcomes — for example, expected awareness lift from a campaign — before one response has been gathered.
The approach raises familiar questions about privacy and governance. Cashew says it anonymizes client data and limits the use to aggregated insights, a protocol that is well in line with basic compliance strategies under GDPR and CCPA. For enterprise buyers, the proof will be in transparency: clear documentation on how data are processed, what models are being used and what precautions have been taken against bias and leakage.
Funding, Growth, and the Competitive Landscape
Cashew has secured C$1.5 million in pre-seed funding and is preparing to open a seed round of up to $5 million to build out its tech stack.
In the short term, the company would like to expand its U.S. footprint and flesh out a B2B offering for teams already purchasing research in bulk. That puts it in more direct competition with agile platforms and DIY tools by established players as well as newer entrants that are focused on AI-first survey generation.
Incumbents are not standing still. Ipsos’s self-serve tools and offerings from Qualtrics and SurveyMonkey already automate some of the pipeline. What will set Cashew apart is a combination of end-to-end automation and human-vetted methodology, as well as approaching each new project with fresh samples — emphasizing respondent freshness over sheer velocity.
What to Watch Next for Cashew Research and AI
The most unknown quantity for Cashew is the integrity of the samples it generates, how well it performs on messy, real-world data and evidence that hybrid models beat DIY ones every time. More spotlight on panel fraud detection, respondent verification and bias audits, and more call for enterprise-grade controls like data residency and model choice.
If Cashew can turn its early traction into repeatable workflows, it could drag a new cohort of marketers over to the research category while flaying that high-margin project work away at their margins. In an era when days count, and budgets are tight, a credible offer of faster, cheaper but still trustworthy insights is worth considering.
