Success Story: Intelligent Pricing Tool Delivers Insight of Costs and Margin

"Macula was the exact partner we needed to enable our modern data platform, build our pricing tool, and leverage AI/ML in our operations."

-- Paul Dandrow, CIO

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Challenge

Renovo works with hospitals and research labs to monitor, service and maintain each organization’s inventory of technical equipment, from mechanical scales to MRI scanners to ultrasonic cleaning equipment. The company delivers value to its customers through competitive pricing and secures much of its business via RFP (requests for proposals). Due to the size and complexity of its customers, an RFP might include upwards of 100,000 pieces of equipment.

To generate a proposal, the pricing team needed to match each item to a record in Renovo’s internal database, in order to find historical price data, used to inform their quote. A small challenge arose in this process; there’s no industry standard for identifying pieces of equipment, so an RFP might use the manufacturer name, an abbreviation, a model number, or any combination of those fields. The pricing team typically needed to manually search for and validate each item in the RFP, sometimes requiring weeks to generate a single response.

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Solution

Together with Renovo, Macula landed on a solution; a web app that intelligently matches RFP line items to database records and surfaces the most recent and relevant cost data to accurately price an item. As an RFP is being processed, if an item is not matched automatically, the app would provide multiple potential matches and a similarity score to aid the user in selecting the correct match. It would handle pricing by applying a user selected margin to pre-processed cost data, and once complete, users could adjust the prices returned and evaluate their contribution to the overall margin.

Macula built and deployed this intelligent matching tool leveraging a custom machine learning model built on top of OpenAI’s embedding algorithm and a Pinecone vector store. It was surfaced in a user-friendly web app, co-designed with the pricing team, called Bridge, and with the following capabilities:

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• Consistent, Accurate and Profitable Pricing: The new system ensures pricing consistency and reveals the actual margin driven by a selected price for an individual item, a key decision input that was previously completely obscured.  

• Centralized RFP Data: Renovo now benefits from a centralized repository for RFP data, providing a foundation for integration into upcoming projects focusing on further refining competitive pricing.

• Machine Learning Deployment: Macula successfully deployed a robust machine learning model in Databricks running on AWS infrastructure, streamlining how Renovo's pricing team matches items in their extensive database.

• User-Friendly Web App: The launch of a web app “Bridge” replaced a manual, Excel based tool with one that's purpose built for the Renovo pricing process.

• Drastic Reduction in Processing Time: Macula's solution reduced the time required to match RFPs (requests for proposal) from weeks to days, enabling Renovo to generate more responses, quickly and competitively.

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