Know exactly why your clients win or lose when AI advises their buyers.

Seedli maps the decision structure AI models build around any brand, the criteria, the risks, the comparisons, the recommendations, across ChatGPT, Gemini, Claude, Perplexity, DeepSeek, and Copilot. You get the intelligence. Your clients get the strategy. Everyone stops guessing.


One buyer question. Five stages. Every blind spot mapped.

When a buyer asks an AI model a purchase question, the model doesn’t just answer. It builds a decision path: who to consider, what criteria to apply, what risks to flag, who to recommend. Seedli decomposes that path into five measurable stages.

01
Consideration"What are my options?"

AI models don't just list providers, they categorise them. Seedli maps every provider type the AI constructs, their role (primary, secondary, fallback), how they're described, and where buyers switch between them.

Example insightAI models organise your market into 7 provider types. Your client is classified as Primary with 14% shortlist share, but the top elimination trigger is "Insufficient expertise," filtering buyers out before evaluation begins. The buyer journey shows 6 decision stages, all at 50% movement ratio. At Early Exploration, buyers ask "How does an AI-native platform detect unknown threats?", and Seedli tells you exactly what content to create to move them forward at each stage.
02
Evaluation"How should I decide?"

When AI models structure a comparison, which brands survive, and on what dimensions? Seedli measures your client's performance across structured evaluation prompts, criteria win rates, elimination exposure, and trust advantage.

Example insightYour client has 100% visibility, highest in the market. And 100% criteria win rate on "Expected Outcomes," "Expertise & Competence," and "Trust Safety." But 100% elimination exposure. The brand is both the most recommended and the most filtered out. And when buyers rephrase the same need differently, "AI-Driven Threat Detection" vs "Autonomous Threat Response Platform," your client's elimination exposure shifts by 25 points. That's a positioning blind spot no other tool reveals.
03
Decision"Which one should I choose?"

Being visible doesn't mean being chosen. Seedli measures who actually wins when AI models commit to a recommendation, and quantifies the gap between being considered and being selected.

Example insightYour client has 100% evaluation share, the highest in the market. But only 65.5% decision share. The Conversion Strength Index (CSCI) is 0.66, the competitor at #2 has 0.49. That's a 34.5 percentage point drop from evaluation to selection. Under risk-averse framing, your client's win rate shifts by 25 points depending on whether the buyer asks about "AI-Driven Threat Detection" vs "Extended Detection and Response." The commercial implication: risk-driven scenarios present differentiation opportunities your competitors aren't addressing.
04
Retention"Should I switch?"

After buyers choose, AI models keep advising them. Seedli measures post-purchase trust, switching risk, and loyalty durability, and reveals what AI tells existing customers about whether to stay or leave.

Example insightEvery brand in the market, all 8, sits in "Replaceable Vendor": low trust, low switching risk. Zero brands qualify as "Trusted Partner." Your client has 7.7% retention trust and 85% switching exposure. The AI tells existing customers that loyalty in this market is built on inertia, not trust, and the top trust breaker is "escalating costs, alert noise, and inconsistent service SLAs." That's a retention crisis no NPS survey would surface.
05
Advocacy"Should I recommend this?"

When buyers or professionals recommend providers, does AI endorse your client, or does it reserve that role for competitors? Seedli measures recommendation share, peer advocacy dynamics, and the barriers that prevent word-of-mouth.

Example insightYour client has 20% recommendation share but only 10% peer advocacy. And 80% advocacy risk, AI models actively hesitate to recommend them. The Recommendation Barriers reveal why: "AI overclaims, noisy detections, complex tuning, and perceived weak local support." Meanwhile, the Advocacy Content Roadmap gives you 5 prioritised content actions to close the gap, starting with "Customer proof content: case studies with quantified outcomes by vertical."

The full picture in one view.

Individual stages tell you what’s happening. The cross-stage overview tells you what to fix first.

Customer Momentum Pipeline

BrandEvaluationGapDecisionGapRetentionGapAdvocacyGravity
Darktrace100%-35pp66%-58pp8%+12pp20%48.3
CrowdStrike57%-29pp28%-20pp8%-8pp0%23.0

Gap = percentage point change between stages. Red numbers show where momentum bleeds out.

Market Gravity Map

Trusted Leader

Lower visibility, high trust momentum

Market Champion

High visibility, high trust momentum

Invisible

Low visibility, low trust momentum

Considered but Fragile

High visibility, no trust momentum

Darktrace sits here

Low market visibilityHigh market visibility →

Most tools give you a single number: visibility score, mention count, sentiment rating. Seedli gives you a map, and the map tells a story.

In AI-powered cybersecurity, one brand appears in 100% of AI evaluations. By every visibility metric, they’re dominant. But only 65.5% of decisions go their way. Retention trust: 7.7%. Recommendation share: 20%. The Decision Gap is -35 percentage points.

That gap, between being seen and being chosen, between being chosen and being trusted, between being trusted and being recommended, is invisible to every other tool on the market. The Cross-stage overview makes it the first thing you see.

This is the view you put in front of a client in a strategy meeting. It replaces opinion with structure. And it comes with a per-stage strategy and content roadmap so you leave the meeting with actions, not just insights.


The words AI uses to help buyers decide. Now you can see them.

Seedli doesn’t just track whether brands are mentioned. It extracts the specific language AI models use at each decision stage, the criteria they introduce, the risks they flag, the comparisons they make.

Content Strategy Matrix

Every decision criterion plotted on two axes: how much buyers care (Importance) and how much the market disagrees (Polarisation). The result is a 2x2 that tells you exactly where to invest content effort.

Battle Zone

High importance, high tension

Buyers care deeply and providers disagree. Comparison guides, third-party validation, and detailed case studies earn citations here.

e.g. Expertise & Competence, Product/Solution Fit

Table Stakes

High importance, low tension

Everyone agrees these matter. Silence here triggers elimination before a shortlist is even formed.

e.g. Regulatory & Risk Safety, Expected Outcomes, Trust & Reputation

Hidden Differentiator

Low importance, high tension

Niche buyer segments care intensely. Small audience but disproportionate citation return. Ideal for long-tail content.

e.g. Flexibility & Customization, Independence & Incentives

Low Priority

Low importance, low tension

Mention briefly. The citation opportunity is small relative to other quadrants.

e.g. Cost & Fees

Why this matters for bureaus: This is a complete content prioritisation framework in one view. You stop guessing which topics to cover and start investing where the AI models are actively looking for authoritative sources.

Criteria with buyer language

Each decision criterion expands to reveal the actual questions buyers ask, in their own words, in their native language. Extracted from how AI models frame buying decisions.

Buyer questions, Expertise & Competence

  • Can we quantify detection coverage for the specific threat types targeting UK infrastructure?
  • What measurable improvement in false positive reduction and alert triage can we expect?
  • Will this AI platform reduce our mean time to detect and contain UK-based incidents?
Why this matters for bureaus: This is a narrative engine. The buyer language isn’t a data point you interpret , it’s a headline you can put directly into a content brief, an ad, or a landing page. No bureau is getting this from any other tool.

Risk & friction mapping

AI models don’t just add brands to a shortlist. They remove them. Seedli maps both buyer risks (fears that eliminate providers) and buyer hesitations (friction that stalls decisions), plotted on a severity x signal density matrix.

Core Strategic Weakness

High severity, high signal

Fix immediately

Friction & Clarity Issue

Low severity, high signal

Reduce with FAQ and clarity content

Rare but Catastrophic

High severity, low signal

Risk-proof before they surface

Low Priority

Low severity, low signal

Monitor

Why this matters for bureaus: In AI-powered cybersecurity, the “Rare but Catastrophic” quadrant holds five items: Governance or Compliance Failure, Hidden or Uncontrolled Costs, Lack of Competence, Performance Failure, Security or Data Breach. These are the risks that kill deals silently. You get a triage map: what to risk-proof, what to clarify, what to monitor.

Buyer journey with conversion strategy

Seedli maps the entire buyer decision funnel, from first awareness through final verification. Each journey step shows the actual buyer questions, progression signals, and hesitation signals, plus a Conversion Strategy with specific content actions.

D01
Early ExplorationAwareness friction

Improve educational content and pillar pages

D02
Provider DiscoveryEvaluation barrier

Publish structured comparison guides

D03
Comparison & ShortlistingDifferentiation failure

Invest in third-party validation and case studies

D04
Risk AssessmentElimination risk

Create risk-proof content and trust signals

D05
Internal AlignmentInternal alignment friction

Provide champion toolkit and internal pitch decks

D06
Final VerificationCommitment anxiety

Highlight guarantees and exit clauses prominently

Why this matters for bureaus: This is a complete content strategy roadmap delivered automatically. You walk into a client meeting with the roadmap already built, per stage, per friction type, per specific action.

Every model. Every response. One decision map.

AI models disagree. That’s the insight.

ChatGPTGeminiClaudePerplexityDeepSeekCopilot

Seedli tracks all six. Each model constructs decisions differently, different criteria, different risk factors, different winners. Some brands win in ChatGPT and lose in Gemini. Some are recommended by Claude but eliminated by Perplexity. These cross-model patterns are invisible if you only track one platform. Seedli surfaces them as a structured comparison so you can see where consensus builds, where models diverge, and which model-specific patterns are worth acting on.


One platform. Every client. Every market.

Seedli is project-based. Each client, each market, each competitive set is a separate project with its own decision map.

Multi-project architecture

Run ten clients across ten markets. Each project tracks its own competitive set, its own buyer questions, and its own stage-by-stage data. Scale the intelligence without scaling the work.

Bureau-ready output

Every insight Seedli generates is structured for client communication. Cross-stage overviews, criteria alignment gaps, elimination patterns, these are the deliverables you put in a strategy deck, not data you have to interpret and reformat.

Competitive differentiation for your bureau

Your competitors are still selling SEO audits and visibility reports. You're selling decision intelligence: a map of how AI models construct buying decisions about your client's brand, with specific gaps they can close. That's a different conversation, and a higher-value one.


We’re selecting bureau partners for early access.

Seedli is in its pilot phase. We’re working with a small number of bureaus to refine how decision intelligence integrates into real client engagements. If this is a conversation you want to be part of, we’d like to hear from you.

Join the pilot →