Merchant intelligence for AI commerce
The merchant decision layer for AI commerce.
AgentGora turns shopper or agent intent, merchant policy, and live commerce signals into structured product and offer decisions. It powers merchant-owned discovery today and is designed for emerging agent-facing commerce.
Protect shopper relevance
Only consider products that genuinely satisfy the shopper's needs.
Encode merchant policy
Set the rules that must always hold, then the priorities — inventory, margin, promotions, launches — that decide between the options that pass.
Respond across surfaces
Return a product, offer, or action to a merchant surface or an AI agent — with a traceable reason.
Illustrative beauty decision
Four products can all fit. The merchant still has a decision.
Beauty makes the tradeoff tangible: dense assortments, frequent launches and promotions, meaningful inventory differences, and several genuinely substitutable products for the same shopper need.
Calm Barrier Gel-Cream
The shopper has several acceptable moisturizers. This one remains a strong fit and wins because the merchant's current policy favors healthy inventory and an eligible funded promotion while protecting a low-stock hero SKU.
Why it clears the relevance bar
- Lightweight gel-cream texture
- Fragrance-free
- Positioned for sensitive skin
- Within shopper's price / delivery constraints
Why it wins under policy
- Healthy inventory coverage
- Eligible funded promotion
- Meets current contribution objective
- No hard constraint is violated
The simple idea
Relevance sets the boundary. Merchant policy decides within it.
A shopper may have several genuinely good options. AgentGora first establishes the acceptable set, then applies explicit merchant policy inside that set — so commercial priorities never rescue an irrelevant product.
Establish relevance
Use shopper intent, preferences, constraints, and journey context to identify strong-fit products.
Apply merchant policy
Separate hard constraints from softer objectives — inventory health, margin, promotions, launches, fulfillment.
Decide within the guardrail
Select the relevant option that best satisfies the merchant's current policy.
What AgentGora is
A programmable merchant-side decision engine.
AgentGora gives merchants an explicit policy layer between intent and action: what must be respected, what should be prioritized, and how those priorities change with the state of the business.
Merchant optimization operates within shopper-relevance guardrails.
Every merchant has different priorities
Merchant policy isn't a single score.
AgentGora represents merchant logic explicitly — hard constraints, ordered objectives, softer preferences, and conditional rules — so priorities can change without retraining the underlying model.
Built for agent-facing commerce
When a shopper sends an AI agent instead of a browser.
Some agent traffic carries an explicit intent — a structured request from a shopping assistant or protocol. Some is just crawling the catalog, the way search bots always have, with no intent attached. AgentGora applies merchant policy either way: to a live request when intent is available, to the catalog and feed when it isn't.
EXAMPLE — WHEN INTENT IS AVAILABLE
What can this shopper buy?
The request carries intent and constraints rather than a page click.
Return a decision, not just a catalog row.
AgentGora applies relevance, merchant policy, and live business state before producing a structured response.
Important boundary: external AI platforms control their own ranking and presentation. AgentGora controls the merchant-side response when the merchant is asked to participate — through its own surfaces, direct APIs, or emerging agent-commerce interfaces.
Where AgentGora works
One policy and decision layer. Multiple interfaces.
Use the same merchant intelligence for human-facing discovery today and agent-facing commerce as those channels mature.
Today
- Search
- Collections
- PDP recommendations
- Landing pages
Emerging
- Structured agent requests
- Direct merchant endpoints
- Agent-commerce protocol integrations
Adjacent
- Promotions
- Bundles
- Dynamic offers
- Campaign decisions
A low-risk starting point
Find the opportunity. Then prove it.
Start with historical data to identify where alternative product-discovery decisions could have improved revenue, contribution profit, inventory outcomes, or promotion efficiency. Then validate the same decision logic on a controlled merchant-owned surface before extending it to new channels.
Audit
Find decisions with meaningful economic potential.
Pilot
Run a controlled pilot alongside the current experience.
Measure
Compare revenue, profit, conversion, and relevant KPIs.
Deploy
Scale only when the evidence supports it.
About AgentGora
Research depth. Commercial focus.
Negin Golrezaei MIT professor — AI, optimization, marketplaces, and online decision-making.
AgentGora was founded by Negin Golrezaei, an MIT professor whose work spans AI, optimization, marketplaces, and online decision-making.
Our mission is to give merchants an intelligent, programmable way to participate in the next generation of product discovery — across their own sites and emerging AI-agent channels.
Start a conversation
Bring merchant intelligence to every commerce surface.
Start with an opportunity audit or pilot on merchant-owned traffic today, and extend the same decision layer to emerging AI channels over time.