TL;DR:
- This article consolidates verified client results across four named case studies and one anonymized B2B SaaS client, independent Clutch ratings, attribution methodology, and a vetting guide for evaluating organic search agencies.
- Gladia grew sales-accepted leads 7x in 4 months, with 93% of AI-referred leads coming from LLM search.
- Granola grew monthly activated users from AI search by 4,600%, while organic sessions rose 134%.
- incident.io lifted its AI visibility score from 38% to 64% and organic meetings booked by 22%.
- Sova Assessment grew organic demo requests 167%, making organic its #1 channel for leads and marketing qualified leads (MQLs).
Shortlisting an organic search agency is the easy part. Defending that choice to a CFO or board is harder, because you now need to prove performance on two surfaces: traditional Google results and AI-generated answers in ChatGPT, Claude, Perplexity, and Gemini. This page consolidates Discovered Labs reviews, published client results, and independent ratings in one place, so you can build the business case from verified sources.
Discovered Labs reviews tell a consistent story across our published case studies and Clutch profile: pipeline-first organic search results across both Google and AI answers. Documented outcomes include Gladia's 7x growth in sales-accepted leads in four months, Granola's 4,600% growth in monthly activated users from AI search, incident.io's AI visibility score moving from 38% to 64%, Sova Assessment's 167% lift in organic demo requests, and one anonymized client's 6x growth in AI-referred trials in 7 weeks. We are an organic search agency for B2B SaaS, built AEO-native, running SEO and AI search as one operation with month-to-month terms and public pricing.
That two-surface split matters for how you read these results. Ahrefs analyzed 863,000 keyword SERPs and 4 million AI Overview URLs and found only about 38% of cited pages also rank in the top 10 for the same query, a snapshot of low overlap rather than a precise trend. Our own analysis of 144,284 AI citations found Reddit referenced in roughly 27% of ChatGPT's internal search slots during query processing. The practical point: most of what AI cites is not what ranks, and the signals that drive each surface differ.
We trace every published number to a named client, a timeframe, and a measurement path, and we report pipeline metrics (MQLs, demos, trials, meetings booked) rather than citation counts alone. Our in-house platform tracks rankings, citation rate, and share of voice across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, and we work with clients to connect that data to their CRM systems through custom fields and UTM parameters.
We also publish our methodology openly. Our analysis of 144,284 AI citations found Reddit referenced in roughly 27% of ChatGPT's internal search slots during query processing, which is why third-party consistency sits inside every engagement.
Client | Verified result | Supporting metric |
|---|
Gladia | 7x sales-accepted leads in 4 months | 93% of AI-referred leads from LLM search |
Granola | +4,600% monthly activated users from AI search | +134% organic sessions, +558% AI citation rate |
incident.io | AI visibility score 38% to 64% | +22% organic meetings booked |
Sova Assessment | +167% organic demo requests | Organic became #1 channel for leads and MQLs |
Anonymized B2B SaaS (NDA) | 6x AI-referred trials in 7 weeks | 3,500+ AI-referred trials total |
4 proven B2B SaaS case studies#
We have published four engagements as our proof foundation: Gladia (speech AI), Granola (productivity software), incident.io (incident management), and Sova Assessment (HR tech). Each write-up on our published case studies page documents metrics, method, and timeline, pairing the headline number with the approach that produced it. A named client, an exact metric, a documented method, and a stated timeframe are the minimum bar. We set out the full standard in what a credible AEO case study contains. Different verticals, same operating system: one content operation serving both Google rankings and LLM retrieval, measured in pipeline.
Documented feedback from B2B clients#
Qualitative feedback clusters around three themes: technical depth, speed to initial signal, and reporting that connects to revenue. Tom Wentworth, CMO at incident.io, frames the strategic bet directly:
"It's clear that working on AI visibility is as important now as SEO was in the 2010s... I believe early adopters will win the Answer Engine Optimization battle, so it was important for me to find a partner who would help us see results quickly." - Tom Wentworth, CMO at incident.io
Sova Assessment, an HR tech company, valued the collaborative approach and technical rigor:
"The main result we saw really quickly was an increase in organic traffic and an increase in our share of voice in AI search. That wasn't something we were tracking before, but with your help we could see where we were placing compared with our competitors. We've also had more prospects and leads say they found out about Sova through AI." - Sabina Reghellin, Content Marketing Manager, Sova Assessment
GetVocal saw similar depth in execution and reporting:
"The partnership was very strong, it was very educational, and the exchange always left clear takeaways and actionable items afterwards." - Ana Banta, VP of Marketing, GetVocal
Clutch ratings for agency credibility#
We maintain verified client reviews on Clutch, collected through independent client interviews. Clients consistently mention communication, technical knowledge, proactive strategy, and measurable SEO and AEO results, and our verified Clutch profile is public for anyone to inspect. Clutch is one of four places agency reviews live, and each carries a different mix of verification rigor and commercial bias. We rank them in where AEO agency reviews live and what each source actually tells you.
Gladia case study: 7x sales-accepted leads in four months#
Gladia needed qualified sales pipeline beyond what traditional SEO was producing. We ran one connected organic search program: technical and on-page SEO for Google, plus content restructured for LLM passage retrieval on priority buyer queries. The result, documented in the Gladia case study, was 7x growth in sales-accepted leads within four months.
Liam breaks down the mechanics of a comparable engagement in this 2026 case study walkthrough, including how we structured content to earn citations on buyer-intent queries.
93% of leads sourced via LLM search#
The detail that matters for your board slide: 93% of Gladia's AI-referred leads came from LLM search. The attribution path has two parts. AI-referred sessions carry UTM parameters where platforms pass referrer data, and a self-reported "how did you hear about us" field on the demo form captures the rest, with both flowing into CRM source fields. Most AI visibility tools stop short of pipeline: our review of Peec AI found it reports citations without CRM integration or AI-referred traffic data. The takeaway: AI-sourced pipeline is measurable enough to defend, provided you instrument it before the program starts.
The CITABLE framework implementation#
We rebuilt Gladia's content around CITABLE, our 7-component content-for-retrieval framework: Clear entity and structure, Intent architecture, Third-party validation, Answer grounding, Block-structured for RAG (retrieval-augmented generation), Latest and consistent, and Entity graph and schema.
The CITABLE framework post documents each component. The retrieval logic is academic, not preference: dense retrievers outperformed traditional keyword-matching algorithms by 9 to 19 points on passage retrieval in Karpukhin et al.'s research, so sections that independently answer one question get retrieved. You can score any page against these criteria with our AEO content evaluator.
Granola case study: 4,600% monthly user growth#
Granola's engagement shows what AI search does for a product-led motion. Monthly activated users arriving from AI search grew 4,600%, and the AI citation rate rose 558% over the program, figures published in the Granola case study. The significance for a CMO is the channel itself: AI answers functioned as an acquisition source feeding activation, not a vanity awareness metric.
Conversion paths in AI search#
The path AI-sourced users follow is short: a buyer asks a category question in an AI assistant, the answer cites a vendor, and the user arrives pre-qualified. Third-party citation infrastructure supports that path. Our 144,284-citation study found Reddit referenced in roughly 27% of ChatGPT's search slots during query processing. We map these query-to-activation paths in our AI search dominance guide. When the answer does the selling, the click that follows converts differently.
Achieving a 134% organic session lift#
The Granola program also answers the cannibalization concern: organic sessions rose 134% while the AEO work ran, because the same content operation served both surfaces. The surfaces share foundations and differ in signals, a distinction we detail in our SEO versus AEO analysis and in this video explainer. Google still weighs links and rankings, while LLMs weigh passage structure, entity clarity, and third-party consistency. Run both as one operation and each asset does double duty.
incident.io case study: 38% to 64% AI visibility score#
incident.io operates in incident management, where PagerDuty dominated AI answers on category queries. Our program closed that gap: incident.io's AI visibility score moved from 38% to 64% across priority buyer queries, documented in the incident.io case study. The starting point will sound familiar to most CMOs:
"Before Discovered Labs, we were using homegrown LLM prompts, without a clear strategy for what to optimize for or exactly how best to structure content." - Tom Wentworth, CMO at incident.io
Achieving a 22% lift in organic bookings#
Visibility only matters if it converts. Over the same period, incident.io saw a 22% lift in organic meetings booked, connecting citation presence on buyer-intent queries to a calendar-level outcome the revenue team tracks. We structure reporting as that pairing, visibility score plus meetings booked side by side, so progress reads clearly in a quarterly board review.
For incident.io, we confirmed MQLs through source fields and self-reported attribution, not dashboard numbers alone. Our AI tracking platforms analysis documents a structural flaw in how some platforms report visibility, which is why we validate tool data against CRM outcomes. Ask any agency on your shortlist how their reported visibility reconciles with your Salesforce instance.
Sova Assessment case study: +167% organic demo requests#
Sova Assessment, an HR tech company, wanted organic search to carry real pipeline weight. We combined technical SEO, CITABLE-structured content, and off-page consistency work, and the program delivered a 167% increase in organic demo requests, detailed in the Sova Assessment case study. More importantly for internal reporting, organic search became Sova's #1 channel for leads and MQLs, the point at which a marketing line item starts reading as a revenue function.
Driving 76% growth in organic MQLs#
Organic MQLs grew 76% over the same period. The growth came from information consistency: the same accurate claims about Sova appearing on its site, on Reddit, in independent publications, and in comparison content. Google's research on grounding for large language models shows models reward claims corroborated across independent sources. Our Reddit marketing work builds that corroboration deliberately, and Liam's Reddit strategy breakdown shows the tactical side. Links still matter for Google. Consistency is what the AI surface adds.
Sova's attribution ran through standard infrastructure: UTM conventions for AI referrers, GA4 channel grouping, HubSpot source fields, and a "how did you hear about us" field added to the demo form during onboarding.
What the client results have in common#
Across four verticals (speech AI, productivity, incident management, HR assessment), we see the same pattern: fix the technical base, structure content for passage retrieval, build third-party consistency, and measure in pipeline. The verticals differ. The retrieval mechanics do not, which is why the playbook transfers.
Documented client results and ROI#
Beyond the named clients, we helped one anonymized B2B SaaS company under NDA grow from 575 to 3,500+ AI-referred trials in 7 weeks, a 6x lift documented on our SEO agency page. On cost: retainers start at €7,995 per month, and the honest CFO conversation is pipeline value per euro. When organic becomes the #1 MQL channel, as it did for Sova, the retainer compares against the CAC of every channel it displaces.
Why clients cite technical depth#
We see three themes recur in client feedback: technical depth, founder involvement, and revenue focus over rankings. The depth comes from team structure. Our CTO Ben Moore is an ex-Stanford AI researcher who built fraud systems at Stripe and Coinbase, and our CEO Liam Dunne scaled a B2B SaaS past $20M ARR from the marketing seat, with full-time ML engineers working alongside SEO strategists, as detailed on our about page. Clients notice when the people doing the work understand retrieval at the model level.
Why peer referrals outweigh rating averages#
Beyond the verified ratings, the strongest signal is what clients say to peers. Tom Wentworth again:
"I have recommended you to multiple peer CMOs. There are large organizations like Hubspot and Ramp who have dedicated teams to work on large projects like AEO. For everyone else (except my competitors) there's Discovered Labs!" - Tom Wentworth, CMO at incident.io
A peer referral from a sitting CMO reflects what a marketing leader stakes their own credibility on, and that carries more weight than any rating average.
How we validate Discovered Labs reviews#
Our publishing standard is rigorous: every metric names a client and a timeframe, and NDA-protected work appears by numbers only. We review and update case studies so figures stay current, and we cut anything we cannot verify rather than soften it. That discipline is what makes this page safe to forward to a CFO.
How to verify Discovered Labs client impact#
You can run your own verification in four steps before signing anything:
- Read published case studies: Check that every metric names a client and a timeframe.
- Review client feedback: Verify consistency with the case study claims.
- Request the attribution setup: Ask for UTMs, CRM fields, and self-reported capture in writing.
- Schedule a reference call: Speak with a client in your vertical or deal-size band.
Apply the same protocol to every agency on your shortlist, not just to us. Our guide to verifying an AEO agency's claimed AI visibility results sets out the raw proof to demand before you sign.
Tracking AI-sourced pipeline#
A typical technical setup includes UTM parameters on AI referrers (chat.openai.com, perplexity.ai, claude.ai, gemini.google.com), a GA4 custom channel group for AI search, source fields in HubSpot or Salesforce, and a self-reported attribution field on high-intent forms. We cover the shifting measurement ground in this search industry analysis. AI visibility measurement is probabilistic, and third-party tools estimate rather than observe.
Attribution reality check: GA4, HubSpot, your CRM, and self-reported data will give different answers to the same question. That is an industry condition, not a vendor failure. We reconcile all four monthly and report a range with caveats stated, so your board slide survives scrutiny.
Key vetting questions for potential partners#
Use this four-point vetting guide with any organic search or AEO agency, us included:
- Technical depth: Can they explain passage retrieval and why it differs from keyword ranking?
- Attribution: Do they integrate AI-referred pipeline into your CRM, or just report visibility?
- Service vs. software: Are you buying execution, or a dashboard you operate yourself?
- Evidence: Do published results name clients, timeframes, and pipeline metrics? For context, some AEO vendors offer proprietary attribution tools as separate products. Our approach integrates AI-referred pipeline tracking into your existing CRM through custom fields and UTM parameters.
Discovered Labs reviews and testimonials#
Shorter engagements produce quotable signal too. Passion.io saw measurable AI search impact within the first month:
"99% of agencies suck. But you guys do not. 1 month of working with you, and our referrals from ChatGPT are up 29%." - Dan Harvey, Co-founder, Passion.io
The quantified outcome: +29% ChatGPT referrals in one month. We publish these as data points, not dressed-up narratives.
Accessing our verified result datasets#
During the sales process, qualified prospects can review anonymized performance datasets: query-level citation tracking, AI-referred session exports, and CRM source data with identifying details removed. This exists because the NDA client behind the 6x trial lift cannot be named, yet the underlying pattern is still worth inspecting before you commit budget.
Speed to signal: our proven 90-day roadmap#
The operating cadence:
- Weeks 1 to 2: Initial visibility audit, query mapping, and technical foundation work.
- Weeks 2 to 4: Initial AI citations typically begin appearing.
- Weeks 6 to 8: Meaningful share-of-voice gains.
- Months 3 to 6: Compounding effects build across both surfaces, with pipeline impact visible in the CRM. Liam walks through the current operating model in our 2026 SEO roadmap video. We break the cadence down checkpoint by checkpoint in what results to expect from an AEO agency in the first 90 days.
How to verify client references#
Serious prospects can request direct reference calls with existing clients in a comparable vertical or ARR band. We arrange these after an initial fit call, and we encourage hard questions about communication cadence, reporting quality, and what did not work.
Pricing and commercial terms#
Pricing is public and month-to-month, with no annual lock-in:
Tier | Monthly | 6-month rate | Content units/mo |
|---|
Establish | €7,995 | €6,995 | up to 20 |
Compete | €12,995 | €10,995 | up to 30 |
Outlier | from €15,995 | custom | 40 |
The Search Visibility Diagnostic runs €4,370 one-off, credited in full to your first retainer if you sign within 14 days. Full deliverable breakdowns live on our pricing page.
Organic search now produces pipeline on two surfaces, and the agencies worth hiring prove it in both rankings and citations. If you want to verify fit before a sales call, request a baseline AI visibility audit through our answer engine optimization team. We map your citation rate across ChatGPT, Claude, Perplexity, and Gemini on your priority buyer queries and tell you honestly whether we are the right partner.
FAQs#
What is Discovered Labs' current rating on Clutch?#
Discovered Labs holds a 4.9 out of 5 overall rating on Clutch across 4 verified reviews, collected through independent client interviews. Reviews cite communication, technical knowledge, and measurable SEO and AEO outcomes.
How much do Discovered Labs' services cost?#
Establish is €7,995/month (€6,995 at a 6-month commitment), Compete is €12,995/month (€10,995 at 6 months), and Outlier starts from €15,995/month. The one-off Search Visibility Diagnostic is €4,370, credited to a first retainer signed within 14 days.
What is the minimum contract commitment?#
All retainers are month-to-month with 30 days notice and no annual lock-in. Committing to 6 months lowers the monthly rate.
How long does it take to see results from an AEO program?#
Initial citation movement typically appears within 2 to 4 weeks, with meaningful share-of-voice gains in 6 to 8 weeks. Compounding effects build over 3 to 6 months, with pipeline impact visible in the CRM.
Is Discovered Labs only an AEO agency?#
No. Discovered Labs is an organic search agency for B2B SaaS running SEO and AI search as one operation, covering technical SEO, content, link building, Reddit marketing, and AEO.
Key terms glossary#
Answer Engine Optimization (AEO): The practice of structuring content so AI search engines like ChatGPT, Claude, Perplexity, and Gemini retrieve and cite it. It runs alongside SEO inside one organic search operation.
Citation rate: The percentage of priority buyer queries where AI answers cite your brand as a source. It is the leading indicator for AI-sourced pipeline.
Passage retrieval: The process where an LLM pulls semantically relevant text blocks from a document to synthesize an answer, rather than ranking whole pages. Section-level structure matters more than page-level authority for citations.
Information consistency: The alignment of accurate claims about a brand across independent sources, including Reddit, publications, and comparison content. LLMs treat corroboration as a trust signal.
Share of voice: Your brand's presence in AI answers for category queries relative to named competitors. It is the metric behind "why does ChatGPT cite them and not us."
