What We Learned Turning Scattered Use Cases Into a Searchable System

You're on a call with a customer. They mention a problem — maybe it's log consolidation, maybe it's compliance reporting — and you know Elastic has solved this before. But where's the proof? It's in a PDF from last quarter. Or a slide deck someone shared in Slack. Or in the head of an SA who left the team six months ago.

That's why I built Civic Signal: an internal platform that takes all of our public sector use case knowledge — the PDFs, decks, docs, and tribal memory — and turns it into something you can actually search, filter, and ask questions against.

Here's what I learned building it.

Lesson 1: The Problem Isn't Missing Data — It's Scattered Data

We didn't have a knowledge gap. We had a retrieval gap. Important use case stories existed across dozens of formats and locations. Finding the right one before a customer call meant pinging Slack channels, digging through Drive folders, or just hoping you remembered who worked on it.

The first insight was simple: we didn't need more content. We needed one place to put it, with structure that makes it findable.

Lesson 2: The Hardest Part Is Making Messy Documents Behave

Building the UI was straightforward. The real challenge was ingestion — taking a chaotic mix of PDFs, Word docs, PowerPoints, spreadsheets, and Google Drive files and turning them into clean, consistent records.

Every file type has its own quirks. A single deck might contain five different use cases. A PDF might bury the customer name on page three. So the pipeline had to extract text, split multi-use-case documents apart, strip out boilerplate, and figure out what each record actually is — the customer, the industry, the Elastic products involved, the categories it belongs to.

None of that was glamorous work, but it's the foundation everything else depends on.

Lesson 3: Duplicates Will Destroy Your Data If You Let Them

Once you start ingesting content from multiple sources, duplicates are inevitable. The same customer story shows up in a slide deck and a case study PDF. If you just auto-merge or auto-overwrite, you lose nuance. If you block all duplicates, you miss updates.

We built a conflict detection layer that flags potential duplicates and lets the user decide: create a new record, merge specific fields, overwrite entirely, or skip. It adds a step to the workflow, but it's the difference between a dataset you trust and one you don't.

Lesson 4: Search Has to Work Like People Think

Elastic AEs aren't writing complex queries. They need to type "VA network monitoring" and get the right results immediately. So we built search with weighted relevance across titles, descriptions, and customer names, with filters for industry, product, and category, and aggregations that power a dashboard showing trends over time.

The key learning: search quality isn't just about the engine. It's about how well your data is structured upstream. Good mappings and consistent tagging do more for search quality than any algorithm tweak.

Lesson 5: AI Is Most Useful When It's Grounded in Your Data

We added an AI chat layer using Kibana Agent Builder with ES|QL tools. That means when you ask "What categories are growing?" or "Which products show up together most often?" — the answers come from querying our actual dataset, not from a general-purpose model making things up.

We also added voice dictation for faster input when you want to capture a use case on the fly.

The lesson: AI gets practical when you constrain it. Point it at a specific dataset with specific tools, and it becomes genuinely useful instead of impressively vague.

What's Live and What's Next

Today, Civic Signal handles end-to-end ingestion with deduplication, search with filtering and dashboard analytics, Google Drive import, internal auth-gated access, and an Agent Builder integration.

Next, we're tuning hybrid retrieval (combining keyword and semantic search), improving extraction for tricky document formats, and adding confidence scoring so you can see how much the system trusts each record.

Why This Matters

Civic Signal turns institutional memory into something operational. Instead of asking around — "Has anyone seen a use case like this?" — you can search for it directly, with structure, speed, and traceability.

That changes how we prepare for calls, how we learn from each other's wins, and how we scale what works across the team.