Answers, pulled
from what you already know.
Graph + RAG over your internal knowledge, decision-support copilots, and LLM-powered analytics. Turn the documents, tickets, and logs you already have into answers your team can use.
What the
mission actually looks like.
Most businesses are drowning in information they can’t query. Policies in PDFs. Decisions in chat threads. Customer history in tickets. Numbers in warehouses only analysts can reach. The data exists. The answers don’t. We build the layer that turns your unstructured knowledge into a system your team can actually ask. Graph + RAG pipelines over your docs, tickets, and wikis — because flat vector search alone misses the relationships that make an answer correct. Decision-support copilots that cite their sources. Natural-language analytics over the warehouse. Evaluation harnesses that catch hallucinations before your users do. We treat retrieval as a real engineering problem — because it is.
You'll leave orbit
with all of this in hand.
The instruments
we bring aboard.
- Knowledge graphs
- Graph + RAG
- Vector search
- Hybrid retrieval
- Rerankers
- Frontier LLMs
- Rerank models
- Embedding models
- Multimodal
- Routing
- Document parsing
- OCR
- Eval harnesses
- Golden sets
- Regression suites
How the mission
unfolds.
- T−04Source AuditWe catalog the knowledge that matters and where it actually lives — including the parts nobody wants to admit live in DMs.
- T−03Eval SetWe build a golden set of real questions with known answers. Every change from here is measured against it.
- T−02Pipeline BuildChunking, embedding, retrieval, reranking, generation. Each stage is measured; nothing is vibes-based.
- T−01RolloutLaunch with citations and a feedback button. Every thumbs-down becomes a new eval case.
- T+00OrbitContinuous re-indexing, eval regression, and expanding the corpus as your team trusts the system.
You'll get the most
out of this missionif…
- ✓Your team spends hours answering questions that could be answered by your own documents.
- ✓You’ve tried a flat RAG demo and it was ‘fine’ — you need graph-aware retrieval that actually reasons across your docs.
- ✓You need AI answers with citations, not black-box summaries.
- ✓You care about eval rigor and catching regressions before users do.
- ✓You want natural-language access to a warehouse only analysts can query today.
Ready for this mission?
Tell us the problem. We'll reply within one business day with a scoped flight plan or a clear pass.
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