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AI Agents for Retail

AI Agents for Retail & Commerce

Agentic systems for brands, marketplaces, and omnichannel retailers — shopper assistants, merchandising copilots, and operations agents that respect PCI-DSS and your brand voice.

Scope an EngagementExplore 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.

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.

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.

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.

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.

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