How it works

Own the agentic
customer journey.

Third-party agents now mediate the interaction between shoppers and your storefront. Toffee turns those agentic interactions into a continuous feedback loop. Agents do the talking, but you own the journey.

Book a 30-min call
What you get

Three layers that put you
back in control.

01

Agent-optimized storefront

A catalog-tuned relationship graph. Agents see your brand, aesthetic, and product relationships, not just a product feed.

structured catalog
relationship graph
agent-readable surface
02

Capture agent journeys

Map agentic interactions across sandbox testing and live traffic. See how journeys unfold and where friction or drop-off occurs.

agent tracing
journey mapping
drop-off analysis
03

Steer agent behavior.

Feedback signals adapt the representation layer and exposed tools, so future journeys follow your objectives and preferred conversion paths.

steering logic
feedback signals
preferred conversion paths
The graph

From static product data
to agent-ready meaning.

Toffee builds a merchant-specific Brand Commerce Graph. Clusters of nodes and edges that encode what your products mean, how they relate, and what agents are allowed to do with them, so agents retrieve relevant items faster, recommend stronger combinations, preserve aesthetic coherence, and support complete shopping journeys.

EDGES
complementssubstitutesbelongs-to-lookconflicts-withgood-for-occasioneligible-for-checkoutpreferred-for-profileconverted-after
cluster

Products and variants

SKUvariantsmediapricing
cluster

Brand identity

voicetieraestheticpositioning
cluster

Style universe

archetypeoccasionmoodfit
cluster

Relationships

complementssubstitutesbelongs-to
cluster

Policies and actions

promotionsreturnseligibility
cluster

Customer context

profilehistoryloyaltyconsent
cluster

Inventory and paths

stockfulfilmentcheckout
cluster

Merchant memory

tracessessionsoutcomes
The sandbox

Test agent behavior before
agents meet customers.

Capture and map agentic interactions across sandbox testing and live traffic to understand how agent journeys unfold and where friction or drop-off occurs.

STEP 01
Run intents
Test real shopping intents against your storefront: an occasion, a style direction, a constraint.
STEP 02
Inspect recommendations
See exactly what agents retrieve and why: graph-term matches, category paths, aesthetic and material signals.
STEP 03
Give feedback
Mark each recommendation good or bad, with optional notes. Every signal is captured against the journey.
STEP 04
Adapt
Feedback tunes the representation layer and exposed tools, so future agent journeys follow your preferred paths.
How it works

Continuous
representation
learning.

Every agent interaction sharpens the next one. Traces from real shopping journeys feed back into the graph as merchant-owned context. The longer Toffee runs, the better your representation gets.

01IntegratePIM, DAM, ERP, CRMs
02Synthesizebuild merchant hypergraph
03Exposeprotocols, scoped APIs
04Optimizeclassify, rank, frame
05Routemerchant-preferred rail
06Learnupdate the hypergraph
continuousrepresentationlearning
STEP 1
Integrate
PIM, DAM, ERP, CRMs
STEP 2
Synthesize
build merchant hypergraph
STEP 3
Expose
protocols, scoped APIs
STEP 4
Optimize
classify, rank, frame
STEP 5
Route
merchant-preferred rail
STEP 6
Learn
update the hypergraph
How we work

Three phases, built
with your team.

Every engagement runs through the same arc: integrate the catalog, shape agent behavior in the sandbox, then measure against benchmarks we define together.

01

Integrate

Catalogue integration & data mappingjoint
Structure & agent-optimized exposuretoffee
02

Sandbox

Sandbox environment setuptoffee
Agent tracingjoint
Steering-logic testing & tuningtoffee
03

Measure

Joint benchmark definitionjoint
Instrumentation & measurementtoffee
Reporting & optimizationjoint

Discovery is just the beginning.
What happens after?

Toffee is building the integration layer between agents and the systems they’re trying to use. Quietly, for a handful of partners, right now.