The AI-era operating layer for insight agencies.
The fastest path to making your agency AI-native. One platform, flat rate, unlimited AI-powered research. Your team runs faster, your margins improve, your clients get better work.
Join waitlistBuilt for agencies of 5 to 500 people.
Latin America's leading insight agency, going AI-native.
Case study
Provokers
Latin America's leading insight consultancy. 150 people across 8 countries.
By 2024, Provokers was running multi-source projects across 8 countries using multiple tools. Research lived in spreadsheets, survey platforms, social listening tools, and shared drives that didn't talk to each other. Synthesis was manual. A single project took 15 days before anyone saw a deliverable.
Some team members had started using ChatGPT and Claude on the side. It helped for quick tasks but not for real research. Inconsistent outputs, no governance, no way to connect it to an actual workflow. They didn't need another AI tool. They needed infrastructure.
Provokers deployed Meaningful across the entire organization. Not a pilot. Not a test group. Every analyst, every strategist, every office. Today every project runs through Meaningful: surveys deployed, social sentiment pulled, market intelligence gathered, synthesis automatic. The tool stack collapsed into one platform.
15 days
1.5 days
to deliver a full multi-source report
Multiple tools
1 platform
across every project
Manual synthesis
Automatic
every data point, cross-source
“While analyzing this information typically takes us 15 days, with Meaningful we obtained a complete report in just 1.5 days. We cannot operate without Meaningful anymore.”
Rafael Cespedes, CEO, Provokers Chile
Read the full storyHow it works
From client brief to deliverable.
In days, not weeks.
One workspace per client
A dedicated space for every engagement. Your team, your client, and all your data in one place.
Collect from every angle
Run new research or bring in what you already have. Every source feeds the same workspace.
Synthesise and deliver
Meaningful reads every response, every transcript, every data point. Then turns it into a deliverable.
How is this different from giving my team ChatGPT, Claude, or Perplexity?
These tools work well for a quick question or a single document. For research across multiple sources, large datasets, and synthesis across methods, they work by retrieving the most relevant chunks of your data and skipping the rest. That's fast and cheap, but it's not research. Meaningful runs exhaustive analysis: every survey response, every interview transcript, every data point examined and synthesised together. That's the difference between a chat tool and research infrastructure.
The cost is just as stark. A 70-person agency buying AI seats pays $1,750 to $2,800/month just for basic access. Push to premium tiers and that climbs to $8,750 to $22,750/month. And on standard plans, usage limits kick in fast: ChatGPT caps messages per hour, Claude limits output per day, Perplexity throttles searches, Gemini restricts queries per user. The more your team uses AI, the sooner they hit a wall. Meaningful replaces all of that at a flat rate with no caps, and with capabilities those tools were never built to provide.
The business case
Not a cost. A profit center.
Flat rate
One monthly fee. No per-query billing. No usage anxiety. Quote every project with confidence because your costs are fixed.
Mark it up
You pay us a flat rate. You charge clients per project. The more you use Meaningful, the more margin you make.
Consolidate your stack
Stop juggling Dovetail for analysis, ChatGPT for synthesis, and spreadsheets for everything else. One platform handles the research workflow end to end.
Why Meaningful
Built for the job.
To put things in perspective: our client thinqinsights is a five-person North American consultancy running complex, multi-method engagements for some of the biggest brands in the US. A single project might combine a quantitative survey, a set of interviews, a social listening pull, and a competitive review. Each generating a different kind of data, each living somewhere different. Someone still has to pull it all together into a story that means something.
That is exactly what Meaningful is built for. Here is what it looks like in practice.
Emerging technology study for a B2B client.
Quantitative survey combined with market analysis and social scraping, all integrated through Meaningful to build vertical-specific profiles. What would have taken weeks took hours.
Creative testing for a US health system.
Conversational-style interviews with parents, testing reaction to video concepts before full production. Results delivered via a live dashboard with key findings, sentiment, themes, representative quotes, and full transcripts. The client didn't get a PDF three weeks later. They got a link.
Concept testing for a major snack brand.
Quantitative survey results came back too close to call between two campaign directions. Raw data uploaded, consumer trends and social insights pulled in, synthesised review delivered. Primary and secondary research, unified in one analysis.
Pop-up store intercepts for a retail client.
QR-code-based conversational surveys at a live event. Individual brand dashboards plus a merged cross-brand report layered with secondary data. Real-time data from a physical event, synthesised and delivered while the event was still fresh.
The platform does the heavy lifting. The researcher does the thinking. Meaningful processes everything so you can focus on what no model can replicate — the interpretation, the strategic angle, the recommendation that actually changes something for a client.
“What used to take weeks now takes hours. Meaningful integrates all our data sources seamlessly, from surveys to social insights to market analysis, delivering faster insights and smarter decisions across every industry we work in.”
Eva Gott, Partner and Head of Research, thinqinsights
Read the full story
Our story
Why we're building this
We started Meaningful in January 2024 because we saw something that didn't add up. AI was transforming how research gets done, but the tools being built were either enterprise software repackaged with a chatbot, or generic AI products that knew nothing about the craft of insight work. Nobody was building infrastructure for the agencies actually doing the work. The mid-size firms with 10 to 500 people, serving real clients, producing research that shapes real decisions. They were left to figure it out on their own, duct-taping ChatGPT into workflows never designed for it.
It hasn't been easy. We've had seasons where the only thing keeping us going was the work itself and the customers who kept telling us to keep building. Two years later, we're still building, and more certain than ever that mid-size insight agencies are the most underserved and best-positioned players in the AI era. They have the domain expertise, the client relationships, and the craft. What they've been missing is the operating layer that brings it all together. That's what Meaningful is.
Adam Paton Stanley-Smith
Co-Founder and CEO, Meaningful
From the founders
What we're learning from our first customers
Two agencies. One with 150 people across 8 countries. Another with 5. Both solving the same problem. Here's what that taught us.
FAQ