Your next customer
won’t be human.
AI agents are already shortlisting products, picking vendors and completing checkouts on behalf of your buyers. They don’t see your ads or your homepage — they see your machine-readable surface. ADO makes agents discover you, choose you, and finish the transaction.
Tested against ChatGPT · Claude · Gemini · Perplexity — measurable lift inside 90 days
01 — The Shift
You optimized for search.
Then for answers.
Now the buyer is an agent.
2000s — Search
SEO
Humans typed keywords. You ranked on a results page and fought for the click.
2020s — Answers
GEO
Humans asked AI. You fought to be mentioned and recommended inside the answer.
2026 — Agents
ADO
Agents research, decide and transact end-to-end. There is no click to win — only the decision. Either the agent can choose you and complete, or it moves on.
“Build a contact form with a backend.”
A developer says that to a coding agent. The agent shortlists three products and tries one. If your auth flow is human-only, your errors are opaque, or there’s no MCP server — it abandons you and integrates the next product. The sale is lost before your website ever loads.
“Polarized hiking sunglasses under $200.”
A shopper asks ChatGPT. The agent retrieves three brands, ranks them, and completes checkout inside the chat. If your product schema lacks the attributes it filters on, or agentic checkout isn’t enabled — you were never even considered. No visit, no retargeting, no signal.
02 — Why Now
The rails are live. Most products aren’t.
Live
Commerce protocols
ChatGPT Instant Checkout, Stripe ACP, Google UCP and AP2 are in production with major retailers today.
97M+
Monthly MCP downloads
MCP went from spec to standard in 12 months — 28% of the Fortune 500 are deploying it in production.
~2%
Agent task completion
Frontier agents complete only ~2% of enterprise SaaS tasks. The bottleneck is the product surface — not the model. That gap is your opening.
Aug ’26
Compliance deadline
EU AI Act enforcement begins, with penalties up to 7% of global revenue. Agent-ready means audit-ready.
03 — The Diagnostic
Where do you lose the agent?
Every agent decision moves through five layers. We instrument all of them, find the exact layer where agents drop you, and fix it — in order of revenue impact.
L1
Discovery
Does the agent know you exist?
Citations, training-data presence, llms.txt, structured data, third-party content the agent actually retrieves.
L2
Consideration
Do you make the shortlist?
AgentCard, capability copy, schema depth, comparison content, MCP registry presence.
L3
Selection
Does the agent commit?
Friction of first touch, auth flow, error semantics, time-to-first-success.
L4
Activation
Did the agent succeed?
Idempotency, structured errors, MCP completeness, checkout completion — verified by synthetic agent runs.
L5
Recurrence
Will the agent come back?
Continuous evals across model versions, regression detection, outcome-quality signaling.
04 — The Offer
Two surfaces. One outcome: chosen.
Make coding agents integrate you first.
When agents pick the stack, the product with the cleanest agent surface wins the install. We make that product yours.
What we ship
- → AgentCard, llms.txt and an agent-onboarding skill page your buyers’ agents can actually read
- → A scoped MCP server with OAuth-style delegation and short-lived tokens
- → Structured errors with recovery hints, idempotency keys on every write, predictable rate limits
- → A synthetic agent harness running Claude, GPT and Gemini against your live product weekly
- → Agent completion rate, published on a dashboard your CTO will actually open
Embedded engineer · 8-week engagement · platform license
Agent-completion lift in 90 days
Make shopping agents put you in the cart.
Agent-mediated checkout is live inside the chat. We reshape your catalog, policies and checkout so agents can shortlist you — and close.
What we ship
- → Product schema rebuilt with the technical attributes agents filter on — materials, specs, certifications
- → Agentic checkout enabled: ACP, UCP profile at /.well-known/ucp, AP2 mandate scopes for high-AOV SKUs
- → Returns, warranty and shipping moved into machine-readable policy — not buried in HTML
- → Real-time inventory and stock-by-variant exposed where agents look for it
- → Weekly synthetic shopping runs across ChatGPT, Claude, Gemini and Perplexity — share-of-shortlist, share-of-selection and cart completion on a CMO dashboard
Retainer · platform license
Agent-mediated revenue lift in 90 days
05 — How It Runs
No decks. Working endpoints.
Audit & Score
We run real agents against your product and score every layer, L1 through L5. You see exactly where the agent gives up — and what it picks instead.
Build
An embedded 42A engineer ships the agent surface with your team: schema, protocols, MCP, policies, checkout paths. No decks. Working endpoints.
Verify
Synthetic agents run your funnels weekly across the major models. Every claim we make is a number you can re-run.
Monitor & Defend
Models change fast. Evals re-run within 72 hours of every major model release, so a regression never quietly costs you a quarter.
06 — Why Trust It
Built on research, not vibes.
Every recommendation we ship traces back to a controlled study. Our AI research practice is led by Prof. Inbal Yahav, co-chair of the Coller AI Lab at Tel Aviv University.
“If we can’t measure it with a re-runnable eval, we don’t claim it.”
Multi-LLM hypothesis lab
Every change tested across ChatGPT, Claude, Gemini, Perplexity and DeepSeek — not tuned to a single model.
Causal inference, not correlation
We isolate what actually moves agent decisions, so you invest in changes that work.
Synthetic agents at scale
Thousands of simulated agent journeys against your live product, adapted from the leading agentic benchmarks.
Continuous, not one-shot
Evals re-run within 72 hours of every major model release. A model update never silently breaks your funnel.
Agent-readiness audit — limited slots per quarter
Be the answer
agents choose.
You’ll get your L1–L5 score, what agents pick instead of you, and the fix list — in two weeks.