01Prompt Graph
Cluster the language buyers use across intent, competitor comparisons, regions, and answer engines.
Prompt familyIntent stageEngine coverage
Platform
Run the weekly AI answer review.
See what changed, decide who owns the fix, approve the next action, and brief leadership with proof instead of screenshots.
Risk found
Owner assigned
Approval needed
Proof ready
Platform Primitives
The operating architecture behind answer movement.
Haynechi feels premium because the product model is specific. These primitives define what the system observes, creates, and proves.
02Answer Ledger
Store answer snapshots with sentiment, cited sources, brand position, competitor language, and risk history.
Answer stateSource setNarrative drift
03Agent Runtime
Turn evidence into supervised work: briefs, source plans, schema updates, PR targets, and commerce fixes.
InputsApprovalsRun outputs
04Proof Layer
Connect answer movement to crawler behavior, referral quality, campaign activity, and executive reporting.
MovementCrawler eventsReadouts
System Loop
From market evidence to supervised work.
Prompts, answer snapshots, source pages, crawler events, competitor language
Prompt graph, answer ledger, citation graph, agent runs, workspace history
Action backlog, briefs, source plans, campaign assets, board-ready readouts
Answer Operations
The answer room.
One question, one source path, one move, one proof readout.
Question in review
best enterprise AEO platforms
Current answer
ChatGPT
Perplexity
Gemini
AI answers mention Haynechi, but cite the partner review before the owned guide.
ChatGPTPerplexityGemini
SentimentPositive
CitationMixed
OwnerContent
Owned
Owned guide
Appears in ChatGPT and Perplexity
Earned
Partner review
Rising, but framing is soft
Risk
Outdated listicle
Gemini still cites old copy
Approve comparison refresh
Content
Ready
Ask PR for source correction
PR
Review
Watch crawler revisit window
Web
Queued
Answer delta
+2 engines
owned source present
Open risk
1 source
Gemini drift remains
Visit quality
tagged
assistant visits separated
Boundary
sample
not customer proof
Proof Console
Connect answer movement to crawler behavior and visit quality.
This sample product surface shows how Haynechi connects bot events, source freshness, answer changes, referrals, and operator readouts without claiming unverified customer outcomes.
Crawler + Referral TimelineSample
09:10
GPTBot requested comparison guide
Fresh crawl
owned page
10:35
Perplexity cited partner review
Citation shift
earned source
12:20
AI Overview omitted entity page
Coverage gap
technical fix
14:50
Claude referral reached pricing path
Quality visit
pipeline page
Freshness WindowsSample
Comparison guide
recrawled
Integration page
stale
Partner review
rising
Product FAQ
unseen
Proof LedgerSample
Refresh comparison page
answer includes owned source
weekly readout
Add entity schema
AI Overview resolves brand name
technical review
Pitch source correction
outdated listicle displaced
PR review
AI Shopping Shelf
See which products AI systems include, misread, or ignore.
This sample product surface shows how commerce teams would track SKU inclusion, product attributes, retailer citations, and review signals without presenting sample rows as customer outcomes.
SKU Visibility
Sample catalog
SKU-104
Hydrating serum
Included
3 engines
attribute gap
SKU-217
Repair cream
Competing
2 engines
review drift
SKU-342
Travel kit
Missing
0 engines
retailer gap
SKU-509
SPF moisturizer
Included
AI Overview
price mismatch
Retailer Citations
Sample
Owned product page
trusted
Retailer PDP
mixed
Marketplace listing
risk
Review roundup
rising
Signal Queue
Sample
Attribute
fragrance-free missing
Catalog
Review
sensitive skin concern
CX
Retailer
bundle unavailable
Commerce
Schema
GTIN not resolved
Web
Complete Platform
Measure the answer. Move the answer. Prove the movement.
Haynechi turns AI-search visibility into an operating cadence for marketing, content, PR, commerce, and executive teams.
Monitor
Daily
Answer Engine Intelligence
Track visibility, sentiment, citations, and competitive share across the answer engines your buyers already use.
Explore Answer Engine Insights
Discover
Graph
Prompt Demand Graph
Model the language buyers use with AI systems, then prioritize topics by demand, difficulty, intent, and revenue fit.
Explore Prompt Volumes
Act
Runs
Marketing Agents
Deploy supervised agents that convert visibility gaps into briefs, pages, social concepts, PR targets, and sales enablement.
Explore Agents
Attribute
Events
Agent And Crawler Analytics
Connect crawler activity, referral traffic, conversions, and content changes to answer-engine movement.
Explore Agent Analytics
Commerce
SKU
AI Shopping Visibility
Measure SKU-level inclusion, attribute accuracy, review sentiment, and retailer citation quality in AI shopping answers.
Explore ShoppingSignal Console
A command center for zero-click marketing.
The product surface is built around the work: prompts become clusters, clusters become actions, actions become measured answer movement.
- Prompt clusters scored by demand, visibility, sentiment, and conversion intent.
- Citation source maps that separate trusted mentions from narrative drift.
- Agent workflows that create briefs and campaigns with approval gates.
Visibility72.8+8.4
Share18.6%+2.1
Citations4,892+18%
Risk11-4
Recommended actions
Refresh the comparison page cited by Claude and Perplexity.
Publish a source-backed glossary for the highest intent prompt family.
Engine coverage
Integrations
Built to plug into the marketing stack.
Send scores and recommendations into the systems where teams already plan, publish, analyze, and report.
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