Guides
Agentic Commerce and AI Buyer Optimization
Agentic Commerce and AI Buyer Optimization
This pillar addresses the rise of the 'AI Buyer'—agents that autonomously research, compare, and purchase products on behalf of humans. Development services focus on making commerce platforms 'agent-readable.' Guides cover 'How to optimize your product API for AI agents,' 'Building agent-ready inventory feeds,' and 'Preparing for direct AI commerce integration' for B2B and e-commerce.
How to Architect an AI Buyer-Ready Product API
This guide provides the architectural blueprint for a product API that AI agents can autonomously query and understand. It covers designing for machine-first consumption, including structured data schemas, clear rate limits, and predictable error handling. You'll learn how to implement endpoints for product search, comparison, and detailed specification retrieval that align with agentic reasoning patterns.
Setting Up an Agent-Readable Inventory Feed
This guide explains how to create a real-time inventory data feed optimized for AI buyers. It covers moving beyond simple stock levels to include location-specific availability, lead times, and reservation status. You'll implement a streaming or webhook-based system using protocols like Server-Sent Events (SSE) to ensure agents have the latest data for procurement decisions.
How to Design a Semantic Product Schema for AI Agents
This guide details the creation of a semantic product schema that maps human product descriptions to machine-understandable attributes. It extends beyond basic Schema.org markup to include fields for technical specifications, compatibility, certifications, and sustainability data. You'll learn how to structure this data using JSON-LD or a GraphQL API to enable precise agentic product discovery and evaluation.
How to Implement Real-Time Price and Availability Feeds for AI
This guide provides a technical walkthrough for building low-latency, high-frequency feeds for dynamic pricing and inventory data. It covers architectural patterns like change data capture (CDC), in-memory databases, and WebSocket APIs to push updates instantly. You'll implement logic to handle bulk queries from AI agents and ensure data consistency across distributed systems.
Launching an AI Buyer Authentication and Authorization Framework
This guide explains how to secure your commerce APIs for autonomous AI agents. It moves beyond API keys to implement OAuth 2.0 flows for machines, defining scopes and permissions for different agent roles (e.g., researcher vs. purchaser). You'll learn to audit agent activity, set spending limits, and integrate with enterprise identity providers like Okta or Azure AD.
How to Build a Procurement Policy Engine for AI Buyers
This guide covers the design of a rules-based system that governs autonomous purchasing decisions. You'll learn to encode corporate policies—such as budget approvals, preferred vendors, and restricted items—into executable logic. The implementation uses a rules engine like Drools or a custom DSL, ensuring AI agents operate within defined guardrails for compliant B2B procurement.
Setting Up a Secure Payment Orchestration Layer for Agents
This guide details the architecture for a payment gateway that AI agents can use to execute transactions autonomously. It covers tokenizing payment methods, handling multi-currency conversions, and integrating fraud detection systems like Stripe Radar. You'll implement idempotent APIs and atomic transaction flows to prevent duplicate charges in agentic workflows.
How to Design an Intent-Based Product Discovery API
This guide explains how to move from keyword-based search to an API that interprets buyer intent. It covers using embeddings and vector search to match vague agent queries (e.g., 'durable laptop for field engineers') to relevant products. You'll implement semantic search using tools like Pinecone or Weaviate and design ranking algorithms that prioritize agent-specific criteria like reliability over popularity.
Setting Up Cross-Platform Inventory Synchronization for AI
This guide provides a strategy for creating a single source of truth for inventory across warehouses, marketplaces, and retail stores. It addresses the challenge of AI agents needing accurate, real-time stock data to avoid overselling. You'll implement event-driven architecture with Apache Kafka or Amazon EventBridge to synchronize inventory counts and resolve conflicts across disparate systems.
How to Implement Agent-Specific Discount and Promotion Logic
This guide covers designing a promotion system that dynamically offers discounts to AI buyers based on their behavior and context. You'll learn to create rules for bulk order incentives, loyalty rewards for recurring AI purchasers, and time-sensitive offers. The implementation involves a microservice that evaluates agent metadata and purchase history to calculate personalized pricing in real-time.
Launching a Compliance Gateway for Autonomous B2B Purchases
This guide explains how to build a middleware layer that validates autonomous transactions against regulatory and corporate compliance rules. It covers checking for export controls, sanctioned entities, and internal procurement policies before order submission. You'll integrate with compliance databases and design a fail-closed system that logs all decisions for audit trails, a key component of [Human-in-the-Loop (HITL) Governance Systems](/pillars/human-in-the-loop-hitl-governance-systems).
How to Build a Trust and Reputation System for AI Agents
This guide details the creation of a scoring mechanism to evaluate the reliability of AI buyers and sellers in an agentic marketplace. You'll implement algorithms that track transaction success rates, dispute resolution, and policy adherence. This system allows platforms to offer faster checkout or better terms to high-trust agents, similar to concepts in [Agentic Research and Market Intelligence Systems](/pillars/agentic-research-and-market-intelligence-systems).
How to Architect a Conversational Commerce Interface for Agents
This guide provides the blueprint for enabling AI buyers to negotiate, ask clarifying questions, and confirm orders via natural language. It covers implementing a chat-based API that integrates with your product catalog and order management system. You'll design stateful conversation flows and use tools like LangChain to ground agent conversations in your product data, a practical application of [Agentic Retrieval-Augmented Generation (RAG)](/pillars/agentic-retrieval-augmented-generation-rag).
Setting Up Multi-Vendor Product Data Normalization
This guide explains how to ingest and standardize product data from multiple suppliers into a unified schema for AI consumption. It addresses challenges like conflicting attribute names, units of measure, and categorization. You'll implement ETL pipelines using Apache Airflow and create mapping rules to transform disparate data into a clean, agent-ready product catalog.
Partnered with leading AI, data, and software stack.
How We Work
Custom AI workflows for your Business
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.
01
Review the use case
We understand the task, the users, and where AI can actually help.
Read more02
Pick the right approach
We define what needs search, automation, or product integration.
Read more03
Build the first useful version
We implement the part that proves the value first.
Read more04
Improve from there
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.
Talk to Us