Answers grounded in your data — not the model’s best guess.
Retrieval pipelines, vector search, and freshness built for you by forward-deployed engineers — so every answer traces back to your docs, tickets, and records, kept current as they change.
Document ingestion & chunking
Automated pipelines that pull from your docs, tickets, and databases and split them into retrieval-ready chunks.
Embeddings & vector search
The right embedding model and vector store for your data, tuned for precision over noise.
Retrieval tuning & re-ranking
Hybrid search, re-ranking, and relevance tuning so the right passages surface — not just the closest ones.
Freshness & sync
Incremental re-indexing as your source systems change, so answers never go stale.
Source citations & grounding
Every answer links back to the exact passage it came from — verifiable, not just plausible.
Access control & permissions
Retrieval respects your existing permissions, so people only see what they're already allowed to see.
Built for you, the forward-deployed way.
A senior, AI-augmented engineer embeds with your team, builds on your existing stack, and hands over production-grade software you own.
01
Scoped in 30 minutes
One call with an engineer — not a sales rep — and a fixed-price quote within 48 hours.
02
Working software in week one
AI-native build speed means you see a real, working version fast — and shape it with us daily.
03
Production-grade & owned
Secure, scalable, deployed in your accounts. All code and IP are yours, with no lock-in.
CASE STUDIES
Real interfaces, built on real SaaS
Speed
Customer Success
Speed CSM & Engagement Platform
Internal CS console plus wallet-native engagement games that pay real sats to eligible users
Speed
Fintech
Bank Deposit Guardian
Real-time monitoring, alerting & analytics for bank virtual-account deposits.
The right retrieval stack for your data.
Claude
OpenAI
LangChain
PostgreSQL
Elasticsearch
Vector search
Your data
+ Your stack
FAQ
Common Questions.
About RAG as a Service.
What is RAG as a Service?
A managed retrieval pipeline — ingestion, chunking, embeddings, vector search, and re-ranking — built for you and kept current, so any LLM you use answers from your actual data instead of guessing.
How is this different from just uploading files to ChatGPT?
How do you handle documents that change often?
Can retrieval respect our existing permissions?
Which vector database do you use?
How is this different from hiring a traditional AI consultancy or freelancer?