AI Agents for Retail

AI Agents for Retail & Commerce

Autonomous merchandising agents, multi-agent supply chain systems, and grounded shopper assistants for brands, marketplaces, and omnichannel retailers — engineered to respect PCI-DSS, brand voice, and your existing WMS.

Scope an Engagement Explore Use Cases

Retail is a domain where brand voice, conversion, and margin are all under attack at once. Every shopper query deserves a better answer than a keyword search; every buyer deserves a planning surface richer than last year's spreadsheet; every returns queue deserves to be triaged by something that reads the photo. We build agentic AI for brands, marketplaces, and omnichannel retailers that treats the shopper as the customer and the merchandiser as the operator. We do not build chatbots that lie about inventory. We build agents that are grounded in your catalog, your promotion calendar, and your PCI-DSS boundaries — and that answer the way your brand actually speaks. We deploy multi-agent systems where a supervisor agent routes shopper intent to specialists — one grounded in catalog, one in promotions, one in inventory — and where the buying, planning, and merchandising surfaces get their own copilot agents rather than sharing a single overloaded one. The stack we run is Python + LangGraph for orchestration, Pinecone or Weaviate for catalog embeddings, and a dedicated observability layer so every autonomous action is traceable back to the retrieved evidence.

Where agents earn their keep in retail

Four high-leverage workflows where autonomous reasoning, when bounded correctly, returns real hours back to your team.

Grounded shopper assistant on storefront and post-purchase

Problem: Shoppers bounce when search fails, and post-purchase questions overload support agents. Both surfaces are answered by systems that don't know the catalog.
Solution: A Shopper Assistant Agent grounded in live catalog, inventory, and order data, with guarded responses on pricing and availability and no free-form promises.
Outcome: Improved storefront conversion on high-consideration categories and a drop in support contact rate on the most common post-purchase questions.

Merchandising copilot for buyers and planners

Problem: Buyers spend too much time in spreadsheets and too little time on assortment judgment. Planning cycles are long and opinion-driven.
Solution: A Merchandising Copilot Agent that answers catalog and performance questions in natural language, drafts assortment proposals, and cites the underlying data.
Outcome: Faster buyer cycle-time on assortment reviews and a sharper separation between data analysis and buyer judgment.

Returns and fraud triage across photo, comment, and order history

Problem: Returns teams triage thousands of claims per day across photos, customer comments, and order history. Fraud patterns surface too late.
Solution: A Returns Triage Agent that classifies claims, drafts dispositions, and escalates suspected fraud to a human investigator with evidence attached.
Outcome: Lower cost-per-claim, faster honest-return resolution, and earlier fraud-pattern detection.

Promotion planning and pricing scenario analysis

Problem: Promotion planners model scenarios in spreadsheets that lack elasticity, inventory, and margin context together.
Solution: A Promotion Planning Agent that runs scenarios grounded in real data and returns ranked options with caveats.
Outcome: Faster promo calendar close and a measurable drop in markdown exposure on poorly-modeled promos.

Autonomous merchandising copilot for assortment and pricing

Problem: Merchandising teams juggle assortment reviews, pricing decisions, and vendor negotiations across dozens of spreadsheets. Getting an evidence-backed recommendation on the spot is rarely possible.
Solution: An autonomous merchandising agent grounded in sell-through, competitor pricing, and inventory positions that drafts assortment proposals, flags underperforming SKUs, and surfaces markdown candidates with the underlying data attached.
Outcome: Buyers make decisions in hours rather than days, with reasoning that is auditable end-to-end and margin outcomes that the finance team actually trusts.

Multi-agent supply chain exception handling for retail operations

Problem: Every retailer runs a spreadsheet of exceptions — late shipments, stock-outs, missing purchase orders — that a small ops team triages by copy-paste. Volume overwhelms the team long before it overwhelms the systems.
Solution: A multi-agent supply chain system where a triage agent classifies each exception and hands off to specialist agents (carrier follow-up, PO reconciliation, allocation adjustment) that draft the outbound message and stage the corrective action for human approval.
Outcome: Retail supply chain teams close exceptions with a fraction of the manual effort and see systemic issues (vendor SLAs, allocation errors) surface faster than they would from a dashboard.

Storefront conversion agent for high-consideration commerce

Problem: Beauty, fashion, and specialty categories lose shoppers who cannot get their exact question answered. Static PDPs and generic chat produce bounce, not conversion.
Solution: A storefront agent grounded in product attributes, reviews, and inventory state — narrow scope, tight guardrails, PCI-DSS boundary respected — that answers with citations back to the catalog.
Outcome: Improved conversion on the categories where shoppers actually ask questions, without the brand-voice risk of a generic third-party chatbot.

Agents we deploy in retail

Each agent is a scoped, typed, evaluable piece of software — not a prompt. We ship them behind approval gates and measure them continuously.

Shopper Assistant Agent

Grounded in catalog, inventory, and order data; guarded on pricing and availability.

Merchandising Copilot Agent

Natural-language catalog and performance Q&A; drafts assortment proposals with citations.

Returns Triage Agent

Classifies claims, drafts dispositions, escalates suspected fraud with evidence.

Promotion Planning Agent

Runs elasticity and margin-aware promo scenarios on real data.

Product Content Agent

Drafts localized product descriptions and enrichment against your brand style guide.

Merchandising Copilot Agent

The autonomous merchandising agent for buyers and planners. Grounded in catalog, sell-through, and competitor data. Drafts assortment reviews and markdown proposals with sources cited.

Supply Chain Exception Handler Agent

The multi-agent supply chain triage layer for retail ops. Classifies exceptions, routes to specialist agents (carrier, PO, allocation), and stages corrective actions for human approval.

Looking for the engineering behind these patterns? Read our approach to agentic custom software engineering and autonomous agent design patterns.

Governance

Built for PCI-DSS and brand-safe shopper surfaces

Shopper-facing agents are deployed with PCI-DSS cardholder-data separation as a first-class constraint — agents never handle PAN data, and tokenization boundaries are enforced at the tool layer. Brand-voice guardrails are codified as evaluation suites, not just style prompts, and every shopper-facing response is evaluated against them continuously. Accessibility and consumer-protection compliance are part of the definition of done.

See our broader approach in the enterprise AI governance framework and agentic transformation strategy.

Representative scenarios

How we would approach engagements in retail

Illustrative scoping patterns — not testimonials or client disclosures. Every real engagement is shaped by the customer's data, team, and regulatory posture.

How we would approach a shopper assistant for a mid-size DTC brand

Start with a single high-consideration category. Ground the agent in catalog and inventory, strictly guard pricing and availability claims, and instrument conversion lift before expanding.

How we would approach a merchandising copilot for a multi-brand retailer

Onboard one buyer team as the product owner. Refuse to ship until the semantic layer exists. Measure cycle-time on assortment reviews weekly.

How we would approach returns triage for a marketplace

Pick the top two claim categories by volume. Ship the Returns Triage Agent behind a human-approved disposition queue. Only once override rate stabilizes does disposition automation widen.

How we would approach promo planning for a retail planning team

Shadow-run the Promotion Planning Agent against the last two seasons. Compare its rankings against actual outcomes. Only graduate to live planning once the ranking correlation is clear.

Services that fit

Most retail engagements combine one or two of our service lines.

/services

Cognitive Web Portals

Enterprise RAG portals and natural-language gateways — we turn your enterprise data into an interactive, self-service AI assistant grounded in your own knowledge.

/services

Autonomous Agentic SaaS

Agentic SaaS development and autonomous platform engineering — we build SaaS products whose core loop is powered by LangGraph and CrewAI agents that execute workflows, not just manage them.

Engineering reading

Deep-dives from the Inductivee engineering blog that go deeper on the patterns behind the agents above.

Data Engineering

Generative BI: Connecting LLMs to Your Enterprise Data Warehouse

Natural language queries against a data warehouse sound simple. Making them accurate, safe, and performant in production requires a semantic layer, schema understanding, query validation, and careful access control architecture.

RAG & Retrieval

Semantic Search for Enterprise Knowledge Bases: Engineering Beyond Full-Text

Full-text search returns documents that contain your query terms. Semantic search returns documents that address your intent. The engineering gap between these two — embeddings, hybrid retrieval, re-ranking — is where enterprise knowledge management is being rebuilt.

Multi-Agent Systems

Agentic AI Examples in the Enterprise: Five Production Architectures

Demos are not deployments. These five enterprise agentic AI examples — autonomous procurement, customer-intelligence, RAG-grounded compliance, generative BI, and AI-native SaaS — show what production-grade architectures actually look like.

Multi-Agent Systems

Tool-Calling Architecture: Designing Reliable Function Execution for AI Agents

Tool calling is where most production agent failures originate. Here is the architecture for reliable, idempotent, observable tool execution — with error recovery patterns that actually work at scale.

Frequently asked

Will the shopper assistant ever quote a wrong price or promise inventory we don't have?+

Not in any deployment we ship. Price and availability are guarded tool calls with strict response templates — the agent cannot free-form answer either question.

How do you keep brand voice consistent?+

Brand voice is codified as an evaluation suite, not just a style prompt. Every candidate prompt and model is evaluated against it before release.

Can you integrate with Shopify, Salesforce Commerce, or BigCommerce?+

Yes — we integrate at the documented API layer of the platform and respect your existing app-ecosystem boundaries.

What about PCI-DSS — does the agent ever touch cardholder data?+

No. The agent runtime is outside the cardholder data environment. Payment flows stay inside your tokenized path and never enter the agent's context.

How do you handle returns fraud?+

The Returns Triage Agent flags suspected fraud and routes it to a human investigator with evidence — photo, order history, and pattern context. It never auto-denies a claim unilaterally.

What is a multi-agent supply chain platform and when do retailers need one?+

A multi-agent supply chain platform is an orchestration layer where specialist agents (exception triage, carrier follow-up, PO reconciliation, allocation) collaborate through a supervisor agent. Retailers need one when a single copy-paste team is drowning in exceptions, or when systemic issues are only visible after they have already cost margin. We build these as LangGraph state machines on top of the retailer's existing WMS and ERP, not as replacements for them.

How does an autonomous merchandising agent differ from a BI dashboard?+

A dashboard answers the questions you already thought to ask. An autonomous merchandising agent surfaces the questions worth asking, drafts a proposed action, and cites the data behind it. The difference is decision throughput. Dashboards remain useful for measurement; merchandising agents accelerate the actual buyer workflow.

What guardrails do you put on a shopper-facing retail agent?+

Grounding in the live catalog with no fallback to model-guessed inventory or pricing, guarded responses on availability and shipping promises, PCI-DSS-safe conversation logging, brand-voice constraints enforced by an output classifier, and a human handoff on any query the agent scores below its confidence threshold. Agents that lie about stock are worse than none at all.

Build your retail agent stack with us

We scope in weeks, not quarters. Tell us the workflow that costs you the most hours and we will come back with a buildable plan.

Start a Conversation Other Industries