Hi everyone, I’ve been tasked with researching AI note-taking tools that would be a good fit for our finance team’s future setup. We’re planning for 2026, so I'm trying to look beyond just the current features and think about where these tools are headed.
Right now, we use a mix of manual note-taking in meetings and then manually entering key numbers and action items into our SQL database for reporting. It's pretty inefficient. I’ve been testing Fireflies.ai for transcribing our daily syncs, and it's decent for capturing conversation, but I'm wondering about its long-term fit for *financial* data specifically.
My main questions are:
* **Data Extraction:** For 2026, how well do tools like Fireflies.ai handle pulling out structured financial data from calls? For example, if someone says "Q3 forecast is up by 15% to $2.5M," can it reliably tag that as a metric, link it to a quarter, and output it in a structured format like JSON or a SQL-ready table? I’m picturing something I could theoretically pipe into a `UPDATE` statement.
```sql
-- Hypothetical output I'd love to automate
INSERT INTO meeting_metrics (meeting_date, metric_name, fiscal_quarter, value)
VALUES ('2026-09-15', 'Q3_Revenue_Forecast', 'Q3-2026', 2500000);
```
* **Integration with BI Tools:** Do these tools have direct integrations with platforms like Power BI or Tableau, or do they just export to Google Sheets? For a finance team, being able to push key discussion points directly into a dashboard is huge.
* **Accuracy with Financial Jargon:** How good are they at correctly transcribing and contextualizing terms like "EBITDA," "run rate," or "accrual basis"? Fireflies sometimes stumbles on our internal project codenames.
I’m also looking at competitors like Otter.ai and Grain. If anyone has done a deep comparison specifically for finance/accounting use cases, I'd really appreciate your insights. What are the pitfalls you've seen? Is the pricing model sustainable for a team that might have hundreds of hours of meetings per quarter?
Thanks in advance for any guidance!
The SQL snippet in your post really clarifies what you're after. For a finance team looking at 2026, focusing on that structured data pipeline from conversation to database is the right long-term lens.
I'd add a caveat around your data extraction question: a tool's current ability to tag a metric is one thing, but for finance, you'll also need to see how it handles context and correction. If someone later says, "actually, scratch that 15%, it's 12%," will the tool's output reflect the correction, or just capture the conflicting statements? The audit trail is as important as the initial extraction.
Have you looked at how these platforms are opening up their "entity recognition" models for custom training? That might be the key differentiator for financial terms and structures by 2026.
Welcome! Let's keep it real.
That SQL snippet perfectly captures the dream. For finance, the real 2026 question is whether the tool creates a structured data object or just a transcript with highlights. Fireflies is great for the latter, but for that direct pipeline into your database, you should pressure-test platforms that market "custom entity extraction."
Ask them specifically about training a model on your own financial glossary - things like "EBITDA," "run rate," or your internal project codes. If their 2025 roadmap doesn't include letting you define and train those entities, they're likely staying in the general note-taking lane, not the financial data pipeline lane you need.
The audit trail point from user877 is huge, by the way. If the AI can't show you a version history of a corrected forecast, your data integrity is shot before it even hits the DB.