How Anaconda eliminated versioning chaos and built one source of truth with Aleph

Anaconda is a decades-old data science company with roots in open source, now bringing those roots into open source AI at scale. With a sprawling stack of software tools generating data across the organization, Anaconda's finance team needed a way to consolidate everything they were working with into a single, trusted view.

But as the finance team's reporting demands grew, scattered Google Sheets, brittle versioning, and a constant struggle to share results with stakeholders meant the existing setup couldn't keep up. That's when Anaconda found Aleph.

Challenges before Aleph:

• Fragmented systems: As a data science company, Anaconda runs on a wide stack of software tools, each producing its own data. Pulling that data into a single place the finance team could actually work with was a constant battle.
• Versioning chaos: The finance team's main financial model was fed by a web of supporting Google Sheets. When a supporting sheet updated, the model updated, and there was no clean way to lock results in place before sharing them with leaders.
• Limited visibility for stakeholders: Finance was producing strong work in spreadsheets, but getting the results out to executives and other stakeholders in a stable, trustworthy form was slow and painful.
• No spreadsheet-native option in the market: Most FP&A tools the team evaluated lived in a separate web app, which meant retraining executives who were already working in Google Sheets. Anaconda needed a tool that met the team where they already were.

The solution and results after Aleph:

Today, Anaconda relies on Aleph as the connective tissue between every operating system the company runs on, and as the publishing layer for finance results across the business:

• Connected systems across the stack: Aleph plugs directly into NetSuite as the core ERP, and the team has continued adding sources on a self-serve basis. After a recent acquisition, Noah connected a QuickBooks instance in five minutes, with no involvement from IT. Aleph has grown from an FP&A tool into what Noah describes as a BI layer for all of Anaconda's operating data.
• Published, lockable results: Aleph gives the team the ability to publish a snapshot of their numbers, so leaders can pull down information knowing it won't shift on the fly. Versioning, historically the team's biggest headache, is solved.
• Push to Aleph from any spreadsheet: Noah's favorite feature lets him pull results from any shared spreadsheet and push them into Aleph without disrupting how stakeholders are already working. People keep their formatting and their workflows, and the data quietly flows across the org.
• Automated BVAs and AI commentary: Budget versus actuals reports that used to take real time to gin up are now a click away. Aleph's AI commentary automatically explains month-over-month and budget-versus-actual variances, surfacing why a vendor or department is off plan without the manual drilldown.
• Cross-functional adoption beyond finance: What started as a finance tool has spread to accounting, where the team uses Aleph as part of their month-end close review and quality assurance checks. New finance hires are trained on Aleph as part of onboarding.

The Impact

Aleph transformed Anaconda's finance team from spreadsheet-bound report builders into a centralized, cross-functional source of truth for the entire company:

• Time savings on ad-hoc reporting: With data flowing directly from source databases into the spreadsheets the team already lives in, ad-hoc requests that used to mean hunting across reports are now answered in the spreadsheet itself.
• Confidence in every number: Because Aleph points directly at the underlying databases, the team spends a lot less time auditing and reconciling. Numbers are trusted as soon as they're pulled.
• Self-serve scaling without IT: New systems get added in minutes, not weeks. A recent QuickBooks integration went live the day it was needed, with zero IT involvement.
• AI-powered analysis at scale: Variance deep dives that would have been too manual to run across every line item now happen automatically through AI commentary, freeing the team to focus on what the numbers mean rather than what they are.
• Adoption beyond finance: The accounting team adopted Aleph on its own to support month-end close review, an outcome the finance team didn't predict but is happy to see.

To learn more about Anaconda, visit https://www.anaconda.com