About Ballast

Ballast is a high-performance AI retrieval engine built to solve one problem. Getting your AI agents to use your real data without slowing down, losing context, or forcing you to maintain a zoo of services. It runs as a single Go binary, ships with an embedded SvelteKit UI, and includes native MCP tools so any agent can instantly search, sync, and retrieve your company’s knowledge.

The goal is simple. Make retrieval boring and predictable so your AI agents can finally behave like they have a memory instead of amnesia.

My Role

I built Ballast from the ground up. Architecture, code, docs, MCP design, integrations, Temporal workflows, SvelteKit UI, everything. It started as a personal need for a retrieval layer that didn’t break under real workloads, and grew into a full platform that handles sync, search, embeddings, indexing, rate limiting, and resilience automatically.

My responsibility was to make retrieval feel effortless for developers and fast for agents while keeping the entire system simple to deploy and maintain.

Technology Stack

• Go (go-zero framework)
• SvelteKit + TypeScript
• Qdrant (vector search)
• PostgreSQL
• Redis
• Temporal
• NATS
• OpenAI, Cohere, Jina (embeddings and reranking)

Key Achievements

• Built a single-binary retrieval system with real-time sync and 42 data source integrations.
• Designed and implemented a native MCP server so Claude and other agents can use Ballast with zero setup.
• Created a hybrid semantic search engine with fusion, reranking, smart filters, and sub-10ms latency.
• Shipped a clean, embedded Web UI for management and debugging without adding any extra services.

Links & Resources

• Product Website
• Documentation
• GitHub Repository