Analyze your data with an LLM and keep the real numbers
Ask your AI about a spreadsheet and get an answer in plain English. Without the real file, the numbers may be guesses. Grounding connects your AI to the actual rows. Loam runs SQL against your data and returns the result to your AI.
Why an ungrounded LLM guesses
When you describe your sales data without connecting the file, your AI has no rows to read. It may still produce a fluent answer with specific figures. The figures can look right without coming from your file.
This is the core trap of analyzing data with an LLM. The language is excellent and the reasoning sounds careful, which is exactly what makes the wrong numbers so easy to believe.
See the difference
Same question, same data. Toggle between a raw LLM response and one grounded in the actual rows, with citations you can check.
"What were total sales by region in Q4?"
Based on the Q4 sales data, the West region was the top performer with approximately $310,000 in revenue, representing about 30% of total sales. East followed at $295,000. Overall Q4 performance showed a 12% increase over Q3.
✗ West revenue: $310,000 (actual: $284,000)
✗ East revenue: $295,000 (actual: $312,000)
✗ Q4 vs Q3 growth: 12% (fabricated - no Q3 data provided)
How to analyze data with an LLM you can trust
A connection to your real data and a visible query make each result easier to check.
Connect your AI to your real data
Pasting a sample can help with a quick question. In the free chat tool you can add a CSV or TSV. It sends up to the first 200 lines after the header to an AI service and asks it to cite supporting lines. For repeatable analysis over all rows, connect your data in Loam.
Use structured data grounding
RAG works for documents, but numbers need structured data grounding. It points your AI at the rows in your file, so the answer is calculated from the source values.
Inspect the returned rows
In Loam chat, inspect the returned result rows before using an answer. Connected AI clients control whether the SQL itself is shown.
What a grounded LLM is genuinely good at
Ad-hoc CSV and spreadsheet questions
Add a CSV or TSV and ask in plain English. The free tool sends up to the first 200 lines after the header to an AI service and asks it to cite supporting lines.
Questions across your business data
Describe what you want to know. Loam turns the question into a query against the data you connected. You can try this with text-to-SQL to generate and inspect a query before you run it in your database.
Reports and summaries
Pull a summary from the file you connected. Upload a newer file when the numbers change.
Exploring and spotting patterns
Ask follow-ups, drill into a segment, and check a trend without writing a formula. Your AI explores the question while grounding keeps each number tied to your data.
All of this holds only when the LLM is grounded in your real data. What is AI grounding?
Chat with a CSV or TSV
Add a CSV or TSV. The tool sends up to the first 200 lines after the header to an AI service and asks it to cite supporting lines.
Frequently asked questions
Chat with a CSV or TSV
Add a CSV or TSV. The tool sends up to the first 200 lines after the header to an AI service and asks it to cite supporting lines.