About Apollo

Why Apollo?

Picture this: you’re drowning in a sea of PDFs, technical docs, and meeting notes. You need one answer. Traditional search gives you 10,000 results. RAG systems hallucinate. Vector databases return vaguely related nonsense.

We named this Apollo because we wanted something that actually hits the target.

🏹 The Archer

Apollo was known for his archery—every arrow, perfect aim. That’s the vibe we’re going for here.

When you ask a question, you shouldn’t get back “here’s 47 possibly relevant chunks.” You should get the right answer, sourced from the right document, at the right level of detail. Like an arrow finding its mark, not a shotgun blast hoping something sticks.

Precision matters when you’re dealing with enterprise knowledge, legal docs, or technical specifications. Close enough isn’t good enough.

🔮 The Oracle

Fun fact: Apollo ran the Oracle at Delphi. People would trek up a mountain to ask questions and get cryptic prophecies. We’re doing that, minus the mountain and the cryptic part.

Modern embeddings and neural reranking let us understand intent, not just keywords. You can ask “how do I prevent memory leaks in the inference pipeline?” and get actual answers about memory management—not every doc that mentions the word “pipeline.”

The system reads between the lines. That’s the oracle energy.

☀️ The Light-Bringer

Apollo’s other gig was being the sun god—bringing light, clarity, all that good stuff.

Ever tried finding a specific detail in a 500-page compliance manual at 2 AM? That’s what we’re solving. Shine light on the exact information you need, when you need it. No more ctrl+F through endless pages. No more “I know we documented this somewhere…”

Clear answers. Clear sources. Clear context. That’s the whole point.

🚀 Apollo 13 — “Houston, we have a solution”

Okay, we also really like the Apollo 13 reference. When everything goes wrong—oxygen tank explodes, power systems fail, CO₂ levels rising—you don’t give up. You engineer a solution with duct tape and math.

That’s the philosophy here. When naive retrieval fails, when semantic search drifts, when GPU memory runs out—Apollo adapts. Hybrid search with neural reranking. Intelligent chunking. Memory-efficient inference. Query expansion. The works.

Failure is not an option. (We had to.)

TL;DR: We built a RAG system that actually works. Named it after the god of accuracy and clarity because marketing is half the battle. Added GPU acceleration because waiting 30 seconds for answers is unacceptable.

Ships with real-time inference, hybrid search, and a docs site that doesn’t make you want to cry.

The Mission

Look, RAG is everywhere now. But most implementations are… let’s be diplomatic and say “suboptimal.”

We wanted to build something that:

  • Actually retrieves the right documents (not just semantically similar ones)
  • Runs fast enough to feel instant (GPU acceleration, optimized inference)
  • Doesn’t hallucinate (proper grounding, source attribution)
  • Scales to real workloads (thousands of documents, concurrent users)

Apollo is what happens when you stop accepting “good enough” and start demanding “actually good.”

This is what information retrieval should have been all along.