How to Use Ballast to Bypass the Usual Headaches of Getting Your AI Agents to Use Real Data

I don’t know about you but I’ve hit my limit with AI systems that almost work. In demos everything looks amazing, then the moment you point an agent at real company data, everything slows down, breaks, or vanishes into some mysterious pipeline that nobody understands.

You ask a question like “What did we decide about the Q3 rollout?” and your agent comes back with “I don’t have enough context to answer that.”

Of course you don’t. Because the context is sitting across twelve systems that don’t sync, don’t index well, and don’t talk to each other.

So let’s fix that today. Let’s use Ballast.

If you’ve never used it, Ballast is something I built to solve this exact pain. It’s a single binary that pulls in your scattered data, organizes it, embeds it, indexes it, and gives your AI agents a clean, reliable, stupidly fast way to work with your actual information.

No complicated setup. No juggling microservices. No hoping your sync job didn’t silently die last night.

We’re going to walk through it together and get something real running.

Let’s get started.

The Real Problem With AI and Real-World Data

AI agents aren’t magic. They’re only as good as the context you feed them.

If that context is:

• missing
• stale
• poorly chunked
• slow to retrieve
• split across ten systems with ten APIs

then the agent will act confused because it is confused.

Most Retrieval Augmented Generation setups fall apart not because the LLM is bad but because the retrieval layer is held together with duct tape.

Ballast exists to make the retrieval part boring and predictable so your agent can finally behave like it has a memory instead of amnesia.

What Ballast Actually Does

Here is the simplest explanation.

Ballast:

1. Connects to your data sources
2. Pulls in your content
3. Chunks and embeds it
4. Stores it in Postgres and Qdrant
5. Lets your AI agents query it in milliseconds

All inside a single Go binary.

And because it has a full MCP server built in, Claude Desktop or any MCP-capable agent can use Ballast without plugins or wrappers. It just shows up as a toolset and works.

But let’s not talk about it. Let’s use it.

Setting Up Ballast

Create a directory and clone the repo.

git clone https://github.com/almatuck/ballast.git
cd ballast

Start the infra services.

docker-compose up -d postgres qdrant redis temporal

Copy the example environment file.

cp .env.example .env

Add your API keys for whatever you want to sync. Slack, Notion, GitHub, Drive, Jira, whatever you use.

Run migrations.

make migrate-up

Build the binary.

make build

Then run it.

./ballast

That’s it. You’ve got a running system.

Ballast spins up:

• a Web UI
• a REST API
• an MCP server
• WebSockets
• a Temporal worker
• metrics
• everything else it needs

all inside that one compiled binary.

Open your browser to http://localhost:8080.

You’ll see the UI.

Clean. Fast. No nonsense.

Connecting Your First Data Source

Let’s sync something.

In the left menu click Sources. Choose one. Notion, GitHub, Slack, Google Drive, doesn’t matter.

Enter your credentials. Hit Sync.

You’ll see a real-time log of the sync process, thanks to WebSockets.

Ballast chunking is context-aware, so if you’re syncing documents, it respects paragraphs. If you’re syncing code, it chunk-splits based on syntax.

When the sync is done, Ballast automatically:

• embeds everything using your provider of choice
• stores semantics in Qdrant
• stores metadata in Postgres
• organizes everything into collections

No pipelines. No guesswork. It just works.

Searching Your Data

Go to the Search tab in the UI.

Type a query like:

“Where do we define the deployment strategy for microservices”

Hit Enter.

Results show up instantly. You can click into any document or snippet and see exactly where the information came from.

If you prefer the API, here’s the same query.

curl -X POST http://localhost:8080/api/v1/collections/search 
  -H "Authorization: Bearer YOUR_JWT_TOKEN" 
  -H "Content-Type: application/json" 
  -d '{
    "query": "deployment strategy for microservices",
    "collection_id": "col_123",
    "limit": 10,
    "search_type": "hybrid"
  }'

And now here’s where things get fun.

Giving Your AI Agent Access to Ballast Through MCP

Open Claude Desktop. Go to Settings, Developer, then Edit Config.

Add this:

{
	"mcpServers": {
		"ballast": {
			"url": "http://localhost:8080/mcp",
			"headers": {
				"Authorization": "Bearer YOUR_API_KEY"
			}
		}
	}
}

Restart Claude Desktop.

Now click the Tools icon. You’ll see Ballast.

Claude instantly knows how to:

• list your data sources
• list your collections
• search semantically
• trigger syncs
• and retrieve documents safely

All without you writing a single integration.

This is what removes the usual headaches.

Putting It All Together With a Real Example

Let’s ask Claude:

“Using Ballast, find everything across Notion, GitHub, and Drive related to our Q3 rollout and summarize the risks.”

Claude goes:

• through MCP
• calls Ballast’s search_collection tool
• pulls context across all sources
• reranks it
• builds a summary
• and cites the snippets

A minute ago your agent knew nothing.

Now it has the same context you do.

Why This Works So Well

Ballast removes the parts of RAG that normally cause pain:

• integrations
• chunking
• sync jobs
• embeddings
• indexing
• retrieval
• metadata
• rate limiting
• timeouts
• resilience
• unified search
• tool access
• slow pipelines

All of that becomes someone else’s problem. In this case, Ballast’s problem.

Your agent just gets clean, fast, structured data it can trust.

What You Can Build With This

Once Ballast is in place, you can build:

• an internal AI assistant
• a multi-tool agent that operates across your entire knowledge base
• a code search bot
• a workflow automation agent
• a support agent
• a research agent
• a context-rich coding assistant
• anything that needs reliable, low-latency retrieval

Because now the hard part is solved.

Wrapping Up

If you’ve ever tried to get AI to use real company data, you already know the pain.

Ballast removes most of it.

• one binary
• dozens of integrations
• fast search
• clean chunking
• reliable sync
• full MCP
• simple deployment
• predictable behavior

Your AI agents will finally feel like they’re plugged into your world instead of living in a blank room.

If you want me to write a follow-up tutorial on building your first agent with Ballast, or how Ballast does hybrid search under the hood, just say the word.

Happy building.