Launched this week
ResearchOS
AI-native end-to-end market research
32 followers
AI-native end-to-end market research
32 followers
Most AI research tools only handle one piece of the puzzle. ResearchOS is the first true AI-native end-to-end market research platform. From raw client brief → scoped proposal, smart questionnaire, sample targeting, data collection, AI advanced analysis, segmentation, and final client-ready reports — all in one unified workflow. Faster, cheaper, and built for real market research.
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Launch Team / Built With
I've been doing market research for 8 years. I don't get excited about tools. I'm excited about this one.
Proposal. Survey. Fieldwork QC. Analysis. Report. All in one place, all actually good. No duct tape. No switching between 6 platforms. No exporting CSVs into SPSS at midnight.
The AI doesn't just automate — it thinks. It caught a leading question in my survey draft and suggested a fix. That's not a template, that's a research brain.
Founding 50 pricing is a gift. Grab it before they come to their senses." @mahendra_chaudhary3 @ResearchOS
@anjali_goplani Really appreciate this — especially coming from someone who’s spent years in market research.
A big reason we built Research OS was because researchers are constantly juggling different tools, files, and workflows. We wanted to make the entire process feel smoother without compromising research quality.
Super happy to hear the survey intelligence and QC features stood out to you. Thanks again for the support and for taking the time to share such thoughtful feedback!
@anjali_goplani Wow, thank you! Coming from an 8-year veteran, this feedback hits different.
Exactly why we built it — one platform, no more CSV → SPSS at midnight madness. Really glad the AI caught that leading question for you.
Founding-50 is our way of saying thank you to early believers like you. Grab it quick!
DM me anytime if you want to walk through a real project together.
ok so I was genuinely skeptical. we've tried so many 'AI research tools' that turned out to be fancy survey builders with a chatbot slapped on top.
ResearchOS is different. I uploaded our brief, it built out a proposal with methodology that I'd have written myself — honestly better structured than some I've done manually. Then the survey builder actually understood what I was trying to measure, not just what I typed.
The part that got me was the QC. It flagged 40+ bad responses automatically speeders, straight liners, gibberish open ends — before I even touched the data. That used to take me half a day in Excel.
Final report came out clean, visual, client-ready. Sent it without reformatting a single slide.
@vikas_kori1
Thank you, this genuinely made my day.
The "fancy survey builder with a chatbot" frustration is one we share — every researcher we talked to before we started building had the same complaint. The whole bet was simple: keep the brief alive through the report, so every later stage is anchored to your real objectives, not a generic template.
The QC catching 40+ before you touched the data is the thing we hear most often. Honestly, it's not magic — speeders, gibberish, dupes are 90% of what eats research time, and it's all mechanical to detect.
If anything broke or felt clunky, I'd love to hear it.
I run research projects solo so every hour I spend on proposals or cleaning data is an hour I'm not billing. ResearchOS basically gave me a team.
The proposal builder alone saves me 3-4 hours per project. Secondary research module pulls everything together in one place instead of me having 47 browser tabs open. And the analysis actually explains what the numbers mean, not just charts, but the 'so what' in plain language.
One thing I didn't expect, the report output is genuinely presentable. Like I'm not spending Sunday night reformatting PowerPoint slides anymore.
I've recommended it to two other consultants already. Both signed up the same day.
@palak_patel16 Really appreciate this feedback. 🙏
We built Research OS to help researchers spend less time managing workflows and more time delivering insights, so it’s great to hear the platform is creating real value for your projects.
Thank you as well for recommending us to others — that truly means a lot.
@palak_patel16 This is awesome — thank you!
So glad ResearchOS feels like having a full team behind you. Saving 3–4 hours on proposals and ditching the Sunday night PowerPoint reformatting are exactly the wins we wanted to deliver.
Really appreciate you recommending it to your fellow consultants — means a lot.
Let me know how your next project goes, or if there’s anything we can make even better for solo researchers like you.
Appreciate you!
The end to end workflow is the most interesting part here. Curious....how ResearchOS handles research quality checks, for example biased questions, weak sample design, or conflicting findings during analysis?
@dmitrii_volosatov Great question — quality runs across the whole pipeline at ResearchOS rather than living in one tab.
Biased questions: Every questionnaire is generated from the project's Brief and presented in a plain-language review before fielding — you canrewrite any question inline. A structural check flags issues that often correlate with poor wording (broken skip logic, ambiguous question types, missing options, "needs-a-closer-look" cues). Honest caveat: we don't promise to catch every leading-question phrase automatically — that's exactly why the editable review step is mandatory rather than optional.
Weak sample design: Cint panel feasibility surfaces real supply-side numbers (country, quotas, age/gender, incidence) before you spend a dollar on fielding. The Brief captures the target audience and methodology up front so design isn't an afterthought. And the synthetic-data engine lets you dry-run the whole pipeline on believable persona-based responses before going live — a cheap way to stress-test the design.
Conflicting findings during analysis: Analysis is staged (column detection → descriptive → inferential → modeling → text → charts →recommendations), so divergences between layers are visible per phase rather than buried in one summary. The dataset is QC'd first — automated checks for straightlining, speeders, out-of-range values, contradictions, OE gibberish, duplicates — so the model isn't reasoning over noise. And the narrative layer is constrained by design: every number in the prose maps to a structured field in the analysis output, not an LLM guess.
@dhaval_bundela
Excited to be part of this journey as a developer! 🚀
A lot of effort has gone into building this platform to simplify and automate market research workflows using AI. From survey creation to analytics and reporting, the goal has always been to save researchers time and effort.
Would love your support and feedback on the launch 🙌
mailX by mailwarm
Congrats on your launch!!
@manal_essalek1 Thank you...