The missing context layer for AI in Data & Analytics
For data and analytics leaders focused on accelerating time to insights, we provide the infrastructure that gives your AI the rails to behave like your best analyst. Unlike generic copilots, we turn your existing analytics history, assumptions, and definitions into a durable context layer, so AI can reason consistently, trace its work, and produce results your team can trust
What puts your reputation at risk isn't mistakes, it's having to rebuild quality under time pressure
Human analytics is slow, but trusted
AI in analytics is fast, but requires human review to be defensible
The context layer for AI in Data & Analytics
Our system captures how analyses are executed and validated. We extract, preserve, and surface the structure and logic of analyses for your teams and for your AI
Your SQL Queries
Your entire team's analytical history. Every piece of code, from every study
Our Algorithms
We ingest, decompose, sequence, and deduplicate your queries, creating an analyzable data product
Outputs
AI-readiness assessment and analytics library, including context/RAG data for AI
What Changes With Momenta?
Reduced Time to Insight
Reuse proven analytical logic instead of rebuilding it—so studies move faster
Increased Trust
Assumptions and validation are traceable—so review is faster and easier to defend
AI Readiness
Models work from structured logic, not just examples—reducing review burden and error risk
If you fed your entire SQL history into an LLM tomorrow, would it provide a strategic insight or a high-confidence hallucination?
Send us your query history and we will tell you in 3 days
Questions Teams Ask Us
Not at all. Momenta layers context on top of your existing stack—warehouses, BI, and repos. No new YAML ceremony
Yes, our entire infrastructure is build on the AWS cloud with company and user encryption
With your context captured, NL→SQL is grounded in your approved logic, not a generic model—reducing hallucinations
No! You only need to send queries. The best file to send is an Excel or CSV file of SQL queries with the names of users who executed them, the date and time stamps of their executions, and whether or not they executed correctly. All of these fields should be captured natively by most data warehouse providers
Today we're the only ones adding tags and metadata to queries. Tomorrow we fully expect our users to add intelligent metadata that only a human can know – which internal project a query was for, which forecast it was used for, which KOL informed a decision