ProcureDesk
Not specified
Enterprise Procurement
Amazon Bedrock, AWS Bedrock Embedding Models (Cohere), AWS Lambda, Amazon API Gateway, Amazon CloudWatch, AWS IAM, Amazon Aurora PostgreSQL (vector) [or Pinecone vector database]
ProcureDesk
Not specified
Enterprise Procurement
Amazon Bedrock, AWS Bedrock Embedding Models (Cohere), AWS Lambda, Amazon API Gateway, Amazon CloudWatch, AWS IAM, Amazon Aurora PostgreSQL (vector) [or Pinecone vector database]
ProcureDesk is a procurement management platform that helps organizations control spend and accelerate order and invoice approvals through a unified purchasing interface. Users were slowed down by traditional workflows that required multiple steps and precise inputs to complete purchase requests. Avahi delivered an Al-powered conversational procurement agent MVP that translates natural language requests into structured requirements, recommends the best-fit items from catalogs and purchase history, and enhances purchase order descriptions to meet organizational standards. The result is a faster, more intuitive procurement experience that reduces friction while preserving purchasing compliance.
ProcureDesk is a leading enterprise procurement management platform focused on cost control, visibility, and streamlined approvals. The platform supports vendor integrations and simplifies procurement operations for organizations of all sizes by enabling item discovery and order placement through a unified interface.
ProcureDesk users often had to navigate rigid procurement interfaces that demanded exact product details and forced multiple steps to complete a purchase request. When a request was vague or missing key fields, the workflow stalled, increasing cycle time and user frustration.
ProcureDesk needed an interaction model that could understand natural language product requests, detect missing information, and guide users to complete compliant purchase requests quickly. The platform also needed to recommend items intelligently using vendor catalogs and historical purchases, and to improve purchase order descriptions to meet organizational standards. Without a more intuitive experience, users would continue to face friction, procurement teams would see more back-and-forth clarification cycles, and adoption could suffer even as compliance requirements remained strict.
AWS enabled ProcureDesk to introduce advanced conversational Al and retrieval capabilities without building and operating custom model infrastructure. Amazon Bedrock provided access to high-performing foundation models for understanding user intent, generating structured outputs, and improving purchase order descriptions.
AWS serverless services supported rapid development and scalable execution for conversational workflows. The architecture also enabled secure integration to ProcureDesk systems and observability for monitoring quality and performance.
ProcureDesk chose Avahi for proven experience building agentic Al systems on AWS that connect natural language interfaces to real business workflows. Avahi brought the ability to design a production-minded conversational intake experience, combining retrieval, ranking, and orchestration patterns that are critical for accurate product recommendations.
Avahi also aligned the solution to procurement realities, focusing on structured outputs, compliance-aware workflows, and seamless API integration so users could move from request to order placement with minimal friction.
Avahi built an Al-powered conversational procurement interface that turns vague user requests into structured procurement requirements and completes the purchase workflow through conversation. The solution uses Amazon Bedrock LLMs to interpret user intent, generate clarifying questions, and produce compliant, well-formed purchase order content. When a request is incomplete, intelligent clarification logic detects what is missing and proactively asks targeted follow-up questions to capture the required details.
To power relevant recommendations, Avahi implemented a semantic knowledge base that ingests vendor catalogs and purchase history into a vector database. AWS Bedrock embedding models (Cohere embeddings) create semantic vector representations that allow the system to match user intent to products even when users do not know the exact item name or SKU.
The search layer uses a multi-tiered approach that blends semantic retrieval with keyword-based search for precision. A re-ranking capability then selects the most relevant product options and returns recommendations enriched with pricing and supplier information. This improves recommendation quality and helps users choose compliant items faster.
AWS Lambda orchestrates the conversational workflow end-to-end, coordinating retrieval, ranking, and content generation steps. The backend is exposed through Amazon API Gateway, and the agent integrates directly with the ProcureDesk API to submit purchase orders from the conversation. Monitoring and logging are handled with Amazon CloudWatch, and AWS IAM enforces access controls and secure service-to-service permissions.
ProcureDesk gained a more intuitive procurement experience that reduces user friction while supporting purchasing compliance. The conversational agent streamlines how users request items, improves the quality of purchase order descriptions, and delivers smarter recommendations based on catalogs and historical purchasing behavior, helping users move from intent to order placement with fewer steps and less back-and-forth.
Let’s explore your high-impact AI opportunities together in a complimentary session
Let’s explore your high-impact AI opportunities together in a complimentary session
Let’s explore your high-impact AI opportunities together in a complimentary half-day session
