Automations
This pillar focuses on commerce workflows that expose product, price, and inventory data to external AI shopping agents while preserving brand control and transaction accuracy. Content should show retailers and commerce platforms how a custom backend workflow enables agentic buying, reduces misrepresentation risk, and creates a more durable architecture for AI-mediated discovery and checkout.
This foundational page details the custom architecture for exposing real-time product, price, and inventory data to external AI shopping agents via a secure, controlled API layer. It explains how to build a workflow that enables agentic buying while enforcing brand policies, preventing data misrepresentation, and ensuring transaction accuracy, thereby creating a durable backend for AI-mediated discovery and checkout.
This page outlines a multi-agent workflow that automatically synchronizes product catalogs across e-commerce platforms, marketplaces, and physical store systems. It covers the architecture for conflict resolution, change propagation, and real-time status updates, reducing manual data entry errors and ensuring consistent product information to support seamless agentic shopping across all channels.
This page details a workflow where AI agents automatically format and push enriched product data (descriptions, specs, media) to specific channels based on their unique requirements. It explains how to build a syndication engine that reduces time-to-market for new listings, improves SEO consistency, and ensures AI shopping agents receive the most accurate and compelling product information.
This page describes a workflow where specialized agents source, validate, and populate missing product attributes (materials, dimensions, care instructions) from supplier docs, images, and web sources. It covers the orchestration logic, quality gates, and integration with PIM systems to create richer, more searchable catalogs that improve conversion for both human and AI-driven shoppers.
This page explains how to automate the categorization of thousands of SKUs into a dynamic, multi-level taxonomy using LLMs and vector similarity. It details the workflow for continuous taxonomy refinement, new product onboarding, and ensuring consistent classification across channels—critical for accurate product discovery by both site search and external AI agents.
This page focuses on the critical backend workflow that aggregates Available-to-Promise (ATP) inventory from multiple warehouses, stores, and 3PLs and exposes it via a low-latency API for AI shopping agents. It covers the architecture for data fusion, cache invalidation, and reservation logic to prevent overselling and build trust with automated buyers.
This page details a workflow where agents continuously scrape and analyze competitor pricing, then trigger dynamic repricing rules or alert merchandisers. It explains the architecture for scalable data ingestion, anomaly detection, and integration with pricing engines to protect margin and win the buy box in agent-driven commerce environments.
This page outlines a workflow that uses customer behavior, cart value, and inventory levels to automatically generate and serve personalized promo codes. It covers the agentic logic for offer creation, budget management, and integration with checkout systems to increase average order value and clear slow-moving stock without manual intervention.
This page describes a workflow where agents analyze individual customer history, browsing intent, and real-time context to craft and deliver hyper-personalized offers via email, SMS, or in-session. It details the orchestration between CDP, recommendation models, and comms platforms to boost conversion rates and customer lifetime value autonomously.
This page explains the workflow to automatically apply and enforce different pricing strategies (MAP, MSRP, wholesale) across various sales channels like Amazon, Walmart, and direct DTC. It covers the agentic monitoring of listings, automatic correction of violations, and integration with channel management platforms to maintain brand integrity and avoid penalties.
This page details a workflow where AI agents forecast demand at each warehouse and store location, then automatically generate and route purchase orders to suppliers. It explains the integration of time-series forecasting, lead-time modeling, and ERP systems to reduce stockouts, lower safety stock costs, and maintain service levels for omnichannel fulfillment.
This page outlines a workflow that intelligently routes customer orders to the optimal fulfillment node (own warehouse, dropship supplier, 3PL) based on cost, speed, and inventory availability. It covers the multi-agent decision logic, API integrations, and exception handling required to reduce shipping costs and improve delivery promises automatically.
This page describes a real-time workflow where agents analyze incoming orders, warehouse layout, and picker locations to dynamically generate the most efficient pick paths. It details the integration with WMS, robotics control systems, and digital twins to reduce labor hours, minimize travel time, and increase warehouse throughput for omnichannel orders.
This page focuses on the complex calculation and exposure workflow that provides AI agents with a reliable, real-time promise of inventory across all nodes. It covers the logic for aggregating on-hand, in-transit, and allocated stock, factoring in lead times and cutoffs, to enable accurate delivery date promises during AI-mediated checkout.
This page details a workflow where agents analyze abandoned cart contents, user intent, and browsing history to trigger personalized recovery sequences. It explains the orchestration of SMS, email, and push notifications with dynamic incentives, integrating with CRM and checkout systems to autonomously recapture lost revenue.
This page explains a workflow where agents dynamically select the best shipping carrier and service level for each order based on cost, delivery promise, package dimensions, and destination. It covers the integration with carrier APIs, rate shopping engines, and order management systems to autonomously optimize last-mile logistics cost and customer experience.
This page details a workflow where agents determine accurate sales tax, VAT, or GST for each transaction by integrating with tax engines like Avalara or TaxJar, and automatically validate tax exemption certificates. It explains the architecture for handling complex multi-jurisdictional rules, reducing compliance risk and checkout friction for B2B and B2C buyers.
This page describes the end-to-end workflow for automating BOPIS orders: verifying local stock, reserving inventory, notifying store staff, and updating order status upon pickup. It covers the integration between e-commerce, OMS, and in-store POS systems to create a seamless, labor-efficient click-and-collect experience.
This page outlines a workflow where AI agents interact with customers to diagnose return reasons, check policy compliance, authorize returns, and generate prepaid labels or exchange options. It details the integration with CRM, inventory, and logistics systems to reduce customer service overhead and speed up the returns cycle.
This page explains a workflow where AI agents answer common product questions by retrieving data from knowledge bases and product specs, and triage complex issues to human agents with full context. It covers the conversational AI layer, intent classification, and integration with helpdesk software like Zendesk to scale customer support.
This page details a workflow where agents continuously monitor and analyze product reviews, social mentions, and support tickets to gauge sentiment and identify emerging issues. It explains how to route actionable insights to merchandising, product, and support teams, enabling proactive quality management and reputation defense.
This page outlines a workflow where AI agents guide new customers through product setup, feature discovery, and best practices via personalized emails, in-app messages, or chat. It details the journey orchestration, content retrieval, and integration with product analytics to improve activation rates and reduce early-stage churn autonomously.
This page describes a workflow where agents monitor customer behavior (browsing, purchases, engagement) and trigger highly targeted email campaigns (e.g., browse abandonment, post-purchase nurture, replenishment) with dynamic content. It covers the integration between CDP, ESP, and product catalog to automate 1:1 marketing at scale.
This page explains a workflow where AI agents personalize homepage banners, product recommendations, and promotional messaging in real-time based on user profile and intent. It details the architecture for edge-side decisioning, A/B testing, and integration with CMS and experimentation platforms to boost engagement and conversion rates.
This page details a workflow where agents manage the end-to-end lifecycle of SMS and push campaigns: from trigger detection and message generation to scheduling, sending, and performance analysis. It covers compliance checks, opt-in management, and integration with mobile marketing platforms to drive timely, high-converting engagements.
This page outlines a workflow where agents continuously analyze high-value customer segments, build lookalike models using first-party data, and sync these audiences to ad platforms like Meta and Google. It explains the data processing, model training, and API integration steps to autonomously expand reach and improve ad targeting efficiency.
This page details a B2B workflow where AI agents assist sales reps by automatically generating accurate, compliant quotes based on customer history, contract terms, and current pricing. It covers the integration with CPQ and CRM systems like Salesforce to reduce quote turnaround time, minimize errors, and accelerate the sales cycle.
This page describes a B2B workflow where agents monitor incoming orders against customer-specific contracts to enforce agreed pricing, payment terms, and shipping rules. It explains the logic for validating orders, flagging exceptions, and integrating with ERP and OMS systems to ensure revenue recognition and reduce manual order review.
This page outlines a workflow where AI agents parse complex RFQ documents from buyers, extract requirements, and automatically generate tailored proposals with pricing, specifications, and timelines. It details the document AI, knowledge retrieval, and template assembly needed to respond to large-volume bidding opportunities faster and more consistently.
This page explains a B2B workflow that automatically applies the correct tiered pricing or volume-based discounts to orders based on a customer's purchase history or negotiated agreement. It covers the integration between CRM, pricing engines, and checkout to eliminate manual discount calculations and pricing errors in high-volume wholesale transactions.
This page details a workflow where agents continuously query transactional data from multiple sources to generate and distribute real-time reports on sales, margin, and top-performing SKUs by channel. It explains the data pipeline, anomaly alerting, and integration with BI tools to give merchandising and finance teams instant operational visibility.
This page outlines a workflow where AI agents monitor sales, return, and payment data streams to detect anomalies like sudden revenue drops, fraudulent patterns, or system errors. It covers the statistical modeling, alert routing, and integration with incident management platforms to enable proactive issue resolution and loss prevention.
This page describes a workflow where agents manage the secure, privacy-compliant sharing of first-party data with advertising and retail media partners within a clean room environment. It explains the matching, aggregation, and query orchestration logic required to derive insights without exposing raw customer data.
This page details a workflow where agents forecast future demand, sales velocity, and margin for individual products using historical data, seasonality, and promotional calendars. It explains the integration with planning and allocation systems to autonomously inform inventory procurement, marketing spend, and merchandising decisions.
This page outlines a workflow where agents continuously scrape and synthesize data on competitor assortments, pricing, promotions, and customer reviews. It details the data normalization, trend analysis, and dashboarding to provide merchandising and strategy teams with an automated, always-on view of the competitive landscape.
This page details a vertical-specific workflow where AI agents recommend the best size for a customer based on their past purchases, returns data, body measurements (if provided), and brand-specific fit reviews. It explains the integration with product detail pages and checkout to reduce returns rates and improve customer satisfaction in apparel commerce.
This page describes a workflow for grocery where AI agents manage out-of-stock items by automatically suggesting and confirming suitable substitutions with customers based on category, brand preference, and price point. It covers the integration with inventory systems and conversational interfaces to maintain order value and customer experience during fulfillment.
This page outlines a workflow where AI agents analyze a customer's browsing and cart contents to compare technical specifications across similar electronics products and suggest higher-value alternatives or complementary accessories. It details the product knowledge graph and reasoning logic to drive average order value in complex, specification-driven categories.
This page details a workflow where AI agents analyze customer-provided images, past purchases, and skin tone descriptors to recommend matching foundation, concealer, or lipstick shades. It explains the computer vision and recommendation logic, integrated with virtual try-on tools, to reduce shade-related returns and build confidence in online beauty shopping.
This page explains a workflow where AI agents automatically verify if a specific auto part (e.g., brake pads, filters) is compatible with a customer's vehicle make, model, year, and trim. It details the integration with fitment databases and the shopping cart to prevent costly returns and incorrect installations in a high-risk e-commerce category.
This page describes a workflow where AI agents monitor and respond to product inquiries, complaints, and mentions in social media comments and direct messages. It covers the sentiment analysis, response drafting, and escalation routing to human teams, enabling brands to manage social commerce conversations at scale while protecting reputation.
This page details a workflow where agents continuously calculate and update CLV scores for all customers, then trigger tier-specific actions like loyalty rewards, exclusive offers, or VIP support routing. It explains the model integration, segmentation logic, and connection to CRM and marketing systems to autonomously maximize the value of the customer base.
This page outlines a workflow that integrates computer vision for virtual try-on (apparel, glasses, makeup) with AI agents that guide the user, capture preferences, and recommend sizes or styles. It details the orchestration between frontend AR/VR tools, product catalog, and cart to reduce uncertainty and increase conversion for fashion and accessory retailers.
This page explains the foundational workflow for creating a single customer view by using AI agents to safely match and merge identities from disparate sources (web, app, POS, CRM) while adhering to consent and privacy regulations. It covers the graph-based matching logic, data governance controls, and integration with CDP systems.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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