Validated opportunities, not summaries
Perplexity and ChatGPT return a research summary you have to interpret. Iris ends with a focused, opinionated opportunity recommendation: which niche, why now, what the next move is.
Iris·Deep ResearcherAI Research Agent that turns insights into products
Iris reads the market, the audience, and the SERPs, then hands a structured brief to your AI Team so research turns into a product.
Research that ends in a shipped product, not a PDF.
Trusted by builders at
Why research never reaches the codebase
Reports that die in Notion
Perplexity gives you an answer. SparkToro gives you a chart. Both end as a doc somebody pastes into Notion and nobody reads in week 3. Iris hands her findings to Emma so research becomes a PRD.
Single-source answers you cannot trust
One LLM summary or one trend chart is not a market read. Iris synthesizes search, communities, audience data, and competitor pages into one cited view you can interrogate, not just accept.
Trends that look real but are not
Exploding Topics shows a chart trending up. It does not tell you whether the demand is durable or a TikTok blip. Iris validates trends across signals before you commit a sprint to building for them.
Three weeks from question to decision
Discovery, interviews, competitor scans, slide deck, exec review. By the time the deck lands the founder already picked a direction. Iris compresses the loop into one Editor report your team can challenge the same day.
A day with Iris
From your first prompt to a shipped result — here is how Iris actually works.
- 01
Listen to your idea or question
Iris turns "should I build X?" into a researchable hypothesis — what to validate, what to ignore.
- 02
Deep-search across multiple sources
Search engines, forums, marketplaces, app stores, social — broader than a single Google query.
- 03
Map the demand and competitive signals
Volume, growth, intent, willingness to pay, incumbents — laid out as a comparable signal map.
- 04
Rank niches by go-or-no-go
Score opportunities so you see "this niche, this angle, this evidence" instead of a research dump.
- 05
Hand the validated opportunity to Emma
The winning niche becomes the input to a focused PRD — research never sits in a doc nobody opens.
Hand-off to Emma
Everything Iris needs for trustworthy research
Multi-source data synthesis
Combines search results, community signals, audience data, and competitor pages into one structured view.
Cited findings
Every key claim is backed by a source link so you can verify the evidence yourself.
Underserved need detection
Identifies recurring user complaints and feature gaps competitors are not addressing.
Competitor weakness mapping
Maps where existing players are weak so you know where to position.
Audience segmentation
Breaks the market into segments with distinct needs instead of one monolithic user.
Structured Editor reports
Findings land in an Editor block with sections, takeaways, and recommendations.
Direct handoff to PM
Conclusions feed straight into Emma so research turns into PRD inputs, not a dead document.
What changes when Iris is on your team
Hand-rolled workflows are slow, manual, and tool-heavy. Hover any card to see why each gain matters.
Why builders pick Iris over the rest
Compare vs
Coming from ChatGPT Deep Research? Here is where Iris pulls ahead.
Research that becomes a product
Standalone research tools hand back a Google Doc. Iris hands the validated niche to Emma, who writes a spec; Alex then builds the product. Your insight turns into shipped software in the same session.
Built for builders, not analysts
Most deep research tools optimize for citations and breadth. Iris optimizes for go-or-no-go decisions: "Is this market real? What evidence backs it? What is the smallest product that wins here?"
Atoms vs Perplexity Pro: compare features, pricing, and capabilities
| Feature | Atoms Recommended | Perplexity Pro |
|---|---|---|
| Output | Validated opportunity | Research summary |
| Hands off to a product spec | Direct to Emma | You copy into a doc |
| Niche ranking with signal scoring | Built in | Narrative only |
| Same-session product start | Yes | Research only |
| Source breadth | Multi-source incl. forums, marketplaces | Web + light forums |
How Iris works with the rest of your AI team
Iris does not work alone. Here is how the handoffs land when you build with the full team.
IrisEmma
Iris hands the validated opportunity to Emma. Emma turns "this market is real" into a concrete product spec.
See how Emma worksIrisSarah
Iris's keyword research feeds Sarah's content cluster planning. Your SEO strategy is grounded in real demand, not vibes.
See how Sarah worksIrisAdrian
Iris identifies the high-intent customer; Adrian targets them with paid ads on the right channel.
See how Adrian worksWhat Iris researches for builders
Concrete research questions Iris answers with evidence and a path to product.
- Scan a market
Market opportunity scans
Find underserved niches in a market before committing to building anything.
- Dive on a competitor
Competitor deep dives
Map competitor positioning, weaknesses, and pricing patterns in one report.
- Profile an audience
Audience persona research
Understand who the users are, where they hang out, and what they actually complain about.
- Validate a trend
Trend validation
Test whether a trend is real and durable before building on it.
- Benchmark pricing
Pricing benchmark
See how competitors price, package, and bundle so your pricing decision has a baseline.
- Run discovery
Pre-PRD discovery
Run the discovery work before writing a PRD so Emma builds on real insight, not assumption.
Try these prompts with Iris
Scan a market for underserved niches
@Iris find underserved niches in the personal finance app market for Gen Z in the US. Pull from search, Reddit, and the top 10 incumbents. Identify 3 gaps with evidence and hand the top one to Emma to scope.
Deep dive a single competitor
@Iris deep dive Notion. Map their pricing, positioning, recent product moves, community sentiment, and where their power users are leaking. Cite every claim and flag the two weaknesses we could exploit.
Validate a trend before we build
@Iris is "AI agent for accountants" a real durable trend or a 6-month hype cycle? Check search velocity, community discussions, funding signals, and incumbent moves. Tell me if it is worth Emma writing a PRD for.
Profile an audience before launch
@Iris profile freelance designers earning $80k-$200k/year in the US. Where do they hang out, what tools do they hate, what do they complain about? Hand the persona to Emma so the PRD reflects real users, not assumptions.
Meet the rest of Iris's AI team
No agent works alone. Tap any teammate to see how they handle their part of your product.
Frequently Asked Questions
Put Iris to work
Stop researching in one tool and building in another. Let Iris run discovery and hand her findings to your AI Team in Atoms.
