Retail context product
Turn shopper intent into a profitable promise.
Connect product, price, promotion, inventory, shopper, order, fulfillment, return, supplier, and margin context in the moment.
What you get
Grounded in your systems. Governed by your rules.
Ungoverned agents either see too little to be useful or too much to be safe. This is what keeps a retail agent both.
One view of the shopper. Only what your role allows.
Every agent reads live ERP, POS, e-commerce, and CDP state through the context layer—never a stale export. Role and purpose decide what it can see and do, so a merchandising agent gets product and inventory truth, not raw payment or loyalty PII.
Every action leaves a trail. Every deployment stays yours to govern.
Identity, policy decision, sources used, and the resulting action are all recorded. Deploy managed, or inside your own AWS, Azure, or GCP environment—governed by your identity, encryption, and data-residency controls.
Outcome story · Availability promise
The shopper wants it today. Can you promise it profitably?
Bodhi combines the exact variant, eligible price, live availability, shopper consent, capacity, delivery promise, fraud policy, and expected margin.
Shared contextProduct→Price→Availability→Shopper→Order→Fulfill→Return→Settle
- ResolveIdentify the right product, shopper, channel, location, and fulfillment intent.
- PromiseCheck inventory, reservations, transfers, capacity, delivery, and substitution options.
- ActReserve the best node, protect margin, monitor exceptions, and preserve the evidence.
Retail outcomes
Carry context from discovery to fulfillment and margin.
Product discovery
Resolve variants, attributes, compatibility, content, and supplier facts.
PIMDAMcatalogPrice + promotion
Apply eligibility, campaign, coupon, markdown, and margin rules together.
PricingpromotionAvailability promise
Use store, DC, reserved, in-transit, and capacity context.
OMSWMSinventoryUnified fulfillment
Choose ship, pickup, transfer, delivery, split, or substitution.
OMSTMSstoresReturns + recovery
Connect the order, payment, policy, fraud, disposition, and refund.
ReturnspaymentsMargin + reconciliation
Trace discount, tax, fee, payout, supplier funding, cost, and GL.
SettlementfinanceWho needs this context
Three teams, one shared failure mode.
A merchandising agent, a forecasting agent, and a personalization agent can each run on a capable model and still make wrong calls, because none of them shares a governed view of the SKU, the stock, or the customer.
PersonaWhat they needThe gap todayHow the context layer closes it
Merchandising / assortment manager
One governed, current definition of an active SKU that every agent references, not a nightly batch export.
A product discontinued in the ERP can still show up as orderable online for days—nothing propagates the change in real time.
Event-driven assembly updates the SKU's context the moment the ERP discontinues it, so no agent can act on a stale export.
Inventory / demand planner
One inventory-truth signal, tagged with source and freshness, that every forecasting and reordering agent queries alike.
Sales, inventory, and supplier data live on different refresh schedules; a forecast can ignore a shipment already in transit.
Tags every inventory signal with source and freshness, then hybrid-retrieves the most current one—so a forecast can't outrun a shipment already in transit.
Personalization / customer care
One resolved customer identity, with consent status attached, shared across marketing, service, and loyalty agents.
The same shopper appears under different identifiers in 6 to 8 systems—so a return and a purchase can look like two people.
Resolves the shopper to one identity across all 6 to 8 systems before any agent acts, with consent status attached at resolution time.
Why retail is built this way
Every department solved its own version of the truth.
Each system is right—just not right together.
ERP, POS, e-commerce, and CDP platforms each hold structured, accurate data inside their own boundary. The gap opens at the seams, where an agent needs one fact that spans systems those platforms were never built to share.
- One governed SKU definition replaces separate versions in the ERP, the e-commerce catalog, and the POS
- Customer identity resolves to one record before any agent acts, no matter how many systems it's scattered across
- Every department's agent draws on the same context instead of rebuilding it three separate times
The cost of a wrong answer compounds fast, and quietly.
Inventory distortion and identity fragmentation don't show up as an outage—they show up as a discontinued item recommended with full confidence, or a loyalty tier a service agent can't see.
- A discontinued item can't stay orderable online once the context layer carries the change
- Inventory signals are tagged with source and freshness, closing the accuracy gap physical stores struggle with
- Reconciled, current data sharpens demand-forecasting instead of compounding the error
Engineered, not assembled
Depth becomes kept promises.
Personalization, agentic commerce, and supply chain automation are architecture decisions made here, not integration afterthoughts—validated against availability, fulfillment, and margin accuracy before anything ships.
Personalization needs agentic memory, not static retrieval.
Shopper context persists across sessions instead of resetting with every search—discovery works from governed memory, not just a bigger index.
Agentic supply chains need live, per-SKU context to hold a promise.
Demand-forecasting and replenishment agents work from point-of-sale, loyalty, and calendar signals in real time—the exact availability context an order promise depends on.
Governed definitions are table stakes for AI agents, not a nice-to-have.
Margin and availability logic is certified once and reused everywhere, so the same question about either one gets the same answer no matter how it's asked.
A recommendation is not just a response—it's a decision worth keeping.
The shopper's request, the constraints that applied, the recommendation, and the outcome are modeled as connected context—not disposable chat history—so agents and analysts can both learn from it.
How we validate: Every architecture decision is checked against availability, margin, and promise accuracy before it reaches an agent.
Why BodhiContextAI
Give agents the right context—and prove why it was right.
We are building the operating layer that structures complex business, customer, product, operational, and financial knowledge—making AI agents accurate, contextual, and directly useful for every permitted persona.
Real-time, hybrid truth
Route across SQL, keyword, semantic, graph, and live API context, kept current through events and CDC.
Role-bound MCP access
Expose context and tools through MCP so agents exchange evidence and act only within role, purpose, and permission.
Compounding knowledge
Every correction, decision, and evaluation strengthens the context layer—so the next agent starts ahead of the last one, not from zero.
Deploy where you govern
Run cloud-native with us or inside your cloud, close to the systems that remain authoritative.