Semantic context
Metric definitions, schema descriptions and examples so the model understands your business, not just your tables.
Ask-Your-Data Analytics
Let anyone ask questions of your data in plain English.
I build conversational analytics on top of your data warehouse. Business users ask a question, get the right number and a chart in seconds, and keep asking follow-ups, while your analysts get their time back. It runs read-only, inside your guardrails.
Sound familiar?
Problems this solves
Analysts are buried in ad-hoc requests and the backlog keeps growing.
Dashboards answer the first question but never the follow-up.
Self-serve BI tools that business users never actually adopt.
What you get
Scope of work
A typical engagement covers the following, tailored to your stack and goals.
NL-to-SQL engine
Query generation with validation, read-only access and guardrails against expensive or unsafe queries.
Conversational follow-ups
Context carried across questions, so users can drill down, compare and filter naturally.
Charts & narrative
Answers come with the right chart and a short explanation of what changed and why it matters.
Speed at scale
Caching, semantic caching and latency tuning for real-time use under high concurrency.
KPI monitoring (optional)
Threshold and anomaly signals that flag unusual KPI movements and trigger an automatic explanation.
Use cases
Where this fits
- Commercial teams asking about sales, territories and performance
- Operations teams tracking KPIs without waiting for a dashboard
- Leadership asking ad-hoc questions before a meeting
- Automated variance and anomaly explanations
Toolkit
Technology I use
- Snowflake
- Databricks SQL
- PostgreSQL
- Azure SQL
- LangChain
- FastAPI
- Redis
- Semantic caching
I pick tools to fit your environment and constraints, not the other way round. Models and vendors stay swappable.
Related experience
Built this before
From enterprise platforms I've built. Client names are withheld.
Biopharmaceutical client
Healthcare Conversational Intelligence
Ask-your-data analytics over Snowflake
LLM-driven conversational workflows, built with LangChain and FastAPI, that turn business questions into contextual analytical requests over enterprise healthcare data.
<2sP95 latency under high concurrency
−40%analytics backlog
- LangChain
- FastAPI
- Snowflake
- NL-to-SQL
Retail client
AI Insight Portal
Decision intelligence platform
Monitors business KPIs across enterprise retail datasets and surfaces what changed, how much it matters and why.
- KPI signals
- Anomaly detection
- Variance analysis
- LLM insights
FAQ
Questions about ask-your-data analytics
How do you make sure the numbers are right?
The model works from your metric definitions, generated SQL is validated before it runs, and answers show the query and data behind them. We test against a set of known questions with verified answers.
Can it run on our existing warehouse?
Yes. It connects to Snowflake, Databricks SQL, PostgreSQL, Azure SQL and similar, with a read-only account and your existing access rules.
Talk to me about ask-your-data analytics
Tell me what you're trying to build. In a free 30-minute call we'll work out whether AI is the right tool, what it would take, and whether I'm the right person to build it.