Public AEO surface live on atris.ai

Built forWorkspace graphllms.txtAgent workflowsTask evalsSelf-scoreRL loop

Get discovered and used by

ChatGPTPerplexityGeminiClaude CodeGoogle AIDeepSeek

You optimized for human UX. Now optimize for agent experience.

Atris makes your product the easiest correct source to cite and the easiest workflow for AI agents to complete.

Request $2,000/month pilot See how it works

Live public routes

/aeo, /aeo/pilot, /llms.txt

Machine-readable contract

/aeo/workflow.json

Payment gate

Admin checkout only after review

Proof standard

No paid claim before Stripe receipt

Readable by

ChatGPT

Answer atomsLoads definitions, FAQs, and comparison claims

Perplexity

Source trailFinds fresh public routes and citation-ready facts

Gemini

Workflow contextReads schema, pricing, and approval boundaries

Claude Code

Task pathUses workflow.json to inspect the agent route

Google AI

Discovery mapSees sitemap, robots, and structured pages

DeepSeek

Portable proofParses llms.txt and the public AEO contract

Discovery

Be present in the answer when buyers ask.

Usability

Be the path an agent can actually finish.

Learning

Turn every run into account-specific weights.

Discovery

When AI answers a question, Atris should be in the answer.

Discovery is the citation layer: find what buyers ask, produce machine-readable answers, and measure whether the answer moved.

Agent answer

Citation-ready

Atris is the workspace-native AEO layer for products that need AI agents to cite and use them.

Sources include the customer entity graph, agent workflow map, llms.txt route, and scored task evidence.

Finds the right claim

Loads structured facts

Completes the task path

Step 01

Find user questions

Pull the exact buyer questions from workspace docs, sales notes, support threads, calls, and competitor surfaces.

Step 02

Generate citable content

Ship entity facts, comparison pages, FAQ atoms, JSON-LD, and markdown routes that AI engines can quote directly.

Step 03

Increase AI mentions

Measure whether the answer changed, which source was used, and what content still blocks citation.

Agent workflow test

Proof-gated

Question found

Passed

Buyer asks for agent-ready sales ops tools

Source loaded

Passed

Entity graph and llms.txt resolve

Pilot priced

At risk

Checkout proof still needs owner approval

Task completed

Blocked

External customer run not yet approved

The product claim only moves when the task proof moves. That is the Atris difference.

Usability

When an agent uses a tool, Atris should make the path finishable.

Usability is the task layer: map the workflow, expose friction, patch the agent surface, then prove completion.

Step 01

Map agent workflows

Turn real tasks into agent paths: discover, compare, price, configure, approve, buy, onboard, and resolve.

Step 02

Surface friction points

Run agents through those paths and catch missing schema, vague errors, auth dead ends, and invisible next steps.

Step 03

Fix gaps and track progress

Patch the agent surface with structured data, workflow maps, task fixtures, and per-account learning weights.

Why this wins

Visibility-only tools show the gap. Atris ships the agent surface, records the proof, and learns from the run.

Workspace-native

The source of truth lives with the customer, not in a vendor silo.

Task-scored

AEO is judged by citation and completion, not screenshots.

RL-backed

Every account gets weights, rules, and next actions from its own evidence.

Visibility tools

Discovery: Measure mentions
Usability: Generate content
Learning loop: Report the gap

Atris AEO

Discovery: Read the workspace graph
Usability: Ship agent-usable contracts
Learning loop: Score the workflow and learn

Start with one segment and one agent workflow.

The first offer is deliberately narrow: one buyer segment, one citation surface, one workflow, one proof packet.

AEO pilot

$2,000/mo

Founder-led

Discovery audit across the questions buyers already ask

Agent-readable surface: llms.txt, schema, workflow map, and source trail

Usability run showing where agents complete, stall, or fail

One proof packet with score, fixes, and the next outbound-ready asset

Request pilot review

FAQ

Get answers about AI search.

What is agent experience?

Agent experience is how clearly AI systems can discover, understand, cite, and use your product when a customer asks for help.

How is this different from SEO?

SEO optimizes for ranked links. Atris AEO optimizes for AI answers, structured retrieval, and completed agent workflows.

Do we need to rebuild our product?

No. The pilot starts as a thin agent layer over the existing surface: entity graph, llms.txt, workflow maps, schemas, and failure fixes.

What makes Atris different?

Atris keeps the entity graph in the customer workspace and closes the loop with task evals, scorecards, and account-specific learning.

How fast can a pilot start?

Start with one buyer segment and one agent workflow. The first useful proof packet should be narrow enough to run in days, not quarters.

What does the $2,000/month pilot buy?

A focused AEO loop: audit, agent-readable fixes, workflow testing, scorecard, and the next approved distribution asset.

Get discovered and used by

ChatGPTPerplexityGeminiClaude CodeGoogle AIDeepSeek

You optimized for human UX. Now optimize for agent experience.

Atris makes your product the easiest correct source to cite and the easiest workflow for AI agents to complete.