Melbourne Aerospace Consultancy Builds AI Document Search in a Weekend

<i>An aerospace consultancy near Melbourne, Florida, was drowning in RFP documents. They built a custom AI search tool in one weekend — and saved 15 hours a week. Here's how they did it.</i>

I got a call from a small aerospace consultancy based just south of Melbourne, Florida. They had 12 employees, a handful of government contracts, and a problem that was eating their lunch: every time they responded to an RFP, someone had to dig through thousands of pages of technical specs, past proposals, and compliance documents. It took days. Sometimes they missed critical details.

Their founder, a former NASA engineer named Dave, told me: “We’re spending more time searching for information than actually writing proposals. It’s killing our margins.” He had heard about AI tools like ChatGPT but didn’t know where to start. So we sat down and sketched out a plan: build a custom AI search tool that could understand their documents and answer questions instantly. And we did it in one weekend.

The Problem: 60 Hours Spent Searching Every Week

Dave’s consultancy had about 5,000 documents — RFPs, past proposals, technical manuals, compliance checklists, and email threads. Before every proposal, a senior engineer would spend 10 to 15 hours just finding relevant information. With 4 to 6 proposals a month, that added up to 60+ hours of pure search time. That’s a full-time job.

They tried using Windows file search and a shared drive, but it was slow and inaccurate. They tried a SharePoint solution, but it required constant manual tagging. One missed tag meant a lost document. Dave estimated they missed relevant data in about 20% of proposals, which led to weaker responses and lost contracts. “We’re leaving money on the table,” he said.

The typical workflow looked like this: an engineer would open a folder, scan filenames, open a PDF, Ctrl+F for keywords, then repeat. If a document used a different term — say “propulsion system” instead of “engine” — it was invisible. They needed a tool that understood meaning, not just exact words.

The Solution: A Weekend Build With Open-Source Tools

I’ve helped businesses like Dave’s before — small teams that need practical AI without a big budget. For this project, we used a simple stack: Python, a vector database (ChromaDB), and an open-source embedding-model/” class=”aico-glossary-link” data-glossary-term=”embedding-model”>embedding model. The idea was to convert all their documents into numerical vectors that captured meaning, then let them search by asking natural-language questions.

We started Friday evening. First, we cleaned the document folder. Dave’s team had PDFs, Word docs, and even some scanned images (which we OCR’d). We chunked each document into 500-word sections, embedded them, and stored the vectors in ChromaDB. Total: about 4,500 chunks. That took a few hours.

Saturday morning, we built a simple web interface using Streamlit. It had a search box and a results pane. When someone typed a question — like “What are the vibration limits for the F135 engine?” — the tool would find the most relevant chunks and return them with source links. Dave’s team could also upload new documents directly.

By Sunday afternoon, it was live. Total cost: $0 for software (all open-source), plus about $20 in cloud compute credits. Dave’s team tested it with 20 real queries from past proposals. It found the right document 95% of the time — far better than their old process.

How It Works (No Jargon, I Promise)

Let me explain what’s happening under the hood without the tech speak. Think of it like this: every document is turned into a unique fingerprint (a vector) that captures its meaning. When you ask a question, the tool creates a fingerprint of your question, then finds the documents with the closest fingerprints. It’s not searching for keywords — it’s searching for meaning.

For example, if you ask “What safety certifications do we need for titanium alloys?” the tool will find documents that talk about safety certifications, titanium, and alloys, even if those exact words aren’t in the same sentence. That’s the magic.

Dave’s team didn’t need to learn any new software. They just opened a web page, typed a question, and got answers with links to the source documents. No training required. The whole thing runs on a small server in their office, so data never leaves their control — important for government work.

Real Results: 15+ Hours Saved Per Week

After two weeks, Dave sent me a spreadsheet. Before the AI tool, the team spent an average of 12 hours per proposal just searching for information. After, that dropped to 2 hours. For a typical month with 5 proposals, that’s 50 hours saved — or 15 hours per week.

But the bigger win was quality. Dave told me about a recent RFP for a satellite propulsion system. Using the AI search, they found a past proposal that included a technical approach perfectly suited to the new RFP. They adapted it in half the time and won the contract — worth $120,000. “That one find paid for the tool a hundred times over,” he said.

They also reduced missed requirements. Before, they’d occasionally overlook a compliance clause buried in 200 pages. Now, they ask the AI: “What compliance requirements apply to this RFP?” and get a list in seconds. Their proposal win rate went from 30% to 45% in three months.

Why This Matters for Central Florida Businesses

Melbourne and the Space Coast are full of small engineering firms, defense contractors, and tech consultancies. Most have the same problem: valuable knowledge locked in documents, scattered across drives, and hard to find. AI document search isn’t just for big companies with million-dollar budgets. Dave’s team built theirs for under $100.

Whether you’re in aerospace, manufacturing, healthcare, or professional services, the pattern is the same. You have years of emails, proposals, manuals, and reports. Your team spends hours hunting for answers. A simple AI search tool can change that — and you don’t need a data science degree to build one.

I’ve seen similar results with a medical practice in Lake Mary who used AI to search patient records (with proper HIPAA safeguards) and a real estate firm in Winter Park who searched past contracts. The technology is mature, affordable, and getting easier every month.

“We’re spending more time searching for information than actually writing proposals. It’s killing our margins.” — Dave, aerospace consultancy founder, Melbourne, FL

How You Can Build Your Own (or Get Help)

If you’re thinking, “I want this, but I don’t have a weekend to build it,” you’re not alone. Dave had technical chops — he was a NASA engineer — but most business owners don’t. That’s where a fractional AI officer or a consultant like me comes in. We can assess your document landscape, pick the right tools, and have a prototype running in days, not months.

Start by taking our AI readiness assessment to see if your business is a good fit. Then, we can talk about implementation. For Dave, we used a simple vector search. For more complex needs — like real-time document updates or integration with Microsoft 365 — we might use Microsoft 365 Copilot or a custom solution. The key is to start small and iterate.

If you’re curious about the technical details, check out our AI glossary for plain-English definitions of terms like embeddings, vectors, and RAG (retrieval-augmented generation). And if you want to see if a similar approach could work for your business, reach out. No hype, just practical advice.

The Bottom Line

Dave’s aerospace consultancy built an AI document search tool in a weekend. It saved them 15 hours a week, improved their proposal win rate by 15 points, and paid for itself on the first contract. The technology is accessible, affordable, and ready for small and mid-market businesses in Central Florida.

You don’t need to be a tech giant. You just need a clear problem, a willingness to try something new, and a partner who can guide you. That’s what I do — help businesses like yours cut through the hype and get real results. Let’s build something that works.

“We’re spending more time searching for information than actually writing proposals. It’s killing our margins.” — Dave, aerospace consultancy founder, Melbourne, FL

Frequently asked questions

What is AI document search?

AI document search uses machine learning to understand the meaning of your documents, not just keywords. You can ask questions in plain English and get relevant answers with source links.

How much does a custom AI search tool cost?

For a small business, you can build a basic tool for under $100 in cloud compute costs using open-source software. Larger or more complex setups may cost a few thousand dollars, but still far less than enterprise solutions.

Do I need to be technical to build this?

Not necessarily. If you have a technical employee or can hire a consultant, you can have a prototype running in days. Many non-technical business owners use fractional AI officers or consultants to handle the build.

Is my data safe with AI search?

Yes, if you keep everything on-premises or in a private cloud. The tool Dave used runs on a server in his office, so data never leaves. For regulated industries like healthcare or defense, we can add extra safeguards.

How long does it take to implement?

A simple tool can be built in a weekend. A more robust solution with integrations might take 2-4 weeks. The key is to start with a small set of documents and expand.

What types of documents work best?

Any text-based documents: PDFs, Word files, emails, spreadsheets, and even scanned documents (with OCR). The tool works best with well-structured content like proposals, manuals, and reports.