In this edition of Can Your FileMaker Do This?, we’re diving into how semantic search can transform the way construction companies, field service providers, and manufacturing firms create quotes and job estimates.
If your team is constantly sifting through old estimates, quote templates, job specs, or proposal language, trying to reuse what worked last time—but without an easy way to find it—you’re not alone. Most organizations rely on a mix of shared drives, tribal knowledge, and Ctrl+F.
There’s a better way. And you can build it in Claris FileMaker.
What is Semantic Search?
Semantic search lets you search by meaning, not only exact words. Instead of typing a perfect phrase or remembering which folder something lives in, your team can use natural language queries—and still get accurate results.
Think of it as the difference between:
Keyword search: “custom ductwork”
Semantic search: “quote section we used for that HVAC job with oversized ductwork in the warehouse”
Even if the exact phrase wasn’t used, semantic search will find the closest match based on intent and context.
Use Case: Building Better Quotes Faster
Let’s say your estimator is prepping a quote for a new commercial HVAC install. They want to reuse part of a proposal from a job two years ago with similar scope and specs.
With semantic search built into FileMaker, they could type:
“HVAC quote for a large warehouse project with custom ductwork and multiple zones”
FileMaker, enhanced with semantic search, would return:
The specific quote paragraph about zoning and ductwork
A line item pricing block from a previous estimate
Related installation notes or drawings stored in container fields
Even client-specific terms used in similar contracts
No more hunting through files. No more emailing around. Just faster, smarter access to the right content.
How It Works (Behind the Scenes)
Semantic search is powered by a vector-based similarity model. Here’s how it integrates with FileMaker:
Index your past quotes and templates
Store vectors in FileMaker (using container fields)
Use a semantic embedding model to convert content into vectors
User enters a query
Their question is embedded the same way and compared to stored vectors
FileMaker returns relevant matches
Sorted by similarity—ready to reuse in a new quote
This can all be powered via integration with services like OpenAI, or hosted locally using a setup like our Local LLM for FileMaker tutorial.
Why This Beats Traditional Search
Real Benefits for Construction & Field Teams
Speed up quoting and estimating
Reduce errors by reusing proven language
Make junior estimators more productive
Standardize tone and terminology across your documents
Eliminate redundant work
Whether you build HVAC systems, manage commercial renovations, or run a field service company, this tool helps your team move faster and smarter—with FileMaker as your foundation.
Can Your FileMaker Do This?
If your team is still navigating shared drives, copy-pasting from old files, or relying on institutional memory to build client proposals, it might be time to level up.
With the right integration, FileMaker can support AI-powered semantic search to help you find content faster and deliver more tailored, winning proposals.
Want to see this in action? We’d love to show you how this works in a real-world FileMaker app. Let’s talk.