Anaconda

Success story snapshot

Challenge: Anaconda's documentation ran on a brittle homegrown toolchain that seemed inexpensive, but the hidden costs were steep. Internal teams spent hours handling broken builds and manual site-wide changes, while developers struggled to find the information they needed.

Solution: Rather than hire a dedicated engineer to keep the homegrown system alive, or move to a tool like Docusaurus and still own all the upkeep, Anaconda chose Mintlify, that pairs docs-as-code workflows with AI-native features like semantic search and MCP servers. Mintlify delivered a more upscale version of what the team was already considering while offloading the engineering work entirely.

Key Results:

• ~6,500 monthly AI Assistant queries, each one a question answered without a support ticket
• 5x faster content publishing
• 2-3x faster site-wide documentation updates
• 48 hours/month troubleshooting time savings
• ~$200,000+/year headcount cost savings

Documentation as a growth lever

Anaconda is the trusted foundation for AI-native development, empowering over 50 million developers, data scientists, and enterprises to discover, manage, and deploy open-source data science and machine learning packages. Because users so often start by exploring the product themselves, the quality, accuracy, and discoverability of Anaconda's documentation is critical to the company's success.

"Docs are often the first thing engineers and data scientists turn to when evaluating our tools and deciding whether to adopt or recommend our platform," says Idan Englander, Technical Writing Manager at Anaconda. "Great documentation is non-negotiable for Anaconda because, for many customers, it's the front door to our entire business."

A toolchain held together with duct tape

Before Mintlify, Anaconda's documentation workflow was a patchwork of disparate tools and manual processes: write in reStructuredText, store files across multiple repos organized by product versioning, build HTML pages with a Sphinx theme, and publish through ReadTheDocs. While this approach seemed inexpensive and gave the team control, the hidden costs were enormous.

Broken builds interrupted work constantly, with the team losing roughly 48 hours per month to troubleshooting alone. Any site-wide change required updating three separate repositories, and domain name changes per repo meant the same work over and over. “We were spending our time and resources building and maintaining knowledge infrastructure, rather than improving content and the developer experience itself,” recalls Englander.

Search functionality was, in the team's own words, "abysmal." Developers who couldn't find what they needed either bounced or filed a support ticket, slowing adoption while piling work onto support. On top of that, the team had zero visibility into how users engaged with the content. And they knew they were nowhere near ready to optimize for their fastest-growing traffic segment: AI agents.

Why Mintlify—and not a rebuild

Anaconda knew something had to change and began evaluating its options. Keeping the homegrown stack alive would require hiring a dedicated engineer just for the documentation team. However, annual engineer salaries in Austin can run upwards of $185K. And with the company growing so quickly, how long before they'd need a second, and a third?

“We decided we didn't want to keep building and maintaining the knowledge infrastructure ourselves, especially given all of the new and constantly evolving AI requirements,” shares Englander. “But most off-the-shelf options we looked at, like Document360, didn't offer a big enough uplift to justify the switch. Until we found Mintlify.”

Mintlify's Intelligent Knowledge Platform makes managing documentation and other content truly seamless, with a git-based workflow, fast preview deployments, and self-updating knowledge. Since mid-March 2026, Anaconda has relied on automated workflows that run 10-17 times a month to catch and fix broken internal links, MDX parse errors, and anchor link issues. Meanwhile, out-of-the-box support for responsive semantic search, MCP and llms.txt, and embedded AI assistants keeps all content automatically structured and optimized for both humans and AI.

From cost center to growth engine

Using Mintlify has taken the day-to-day writing and review process for Englander's team from fragile to fluid. Writers can now share a full preview of their changes with anyone in the company through a single link before pushing live, which has proven extremely helpful across a wide range of use cases. Broken builds have stopped interrupting core documentation work, and site-wide changes no longer require manually updating three separate repos.

Rather than dedicating headcount, and then more of it, to knowledge infrastructure, Anaconda now depends on Mintlify to handle that layer entirely, saving more than $200,000 a year in salaries alone. “Choosing Mintlify has allowed us to reallocate headcount that would have otherwise been dedicated to managing knowledge infrastructure to building our platform and improving our content, which is where it belongs,” says Englander.

For the first time, the team has real insight into the user experience and how documentation moves the needle. Mintlify's analytics dashboard now feeds a regular cadence of quantitative and qualitative review, helping Englander produce data-driven reports on how docs drive onboarding and adoption. Search that was once "abysmal" now actually works and, paired with the embedded AI Assistant, lets developers get answers on the spot instead of filing tickets. AI Assistant usage has climbed from roughly 4,600 queries a month in Q4 2025 to about 6,500 in Q1 2026, a ~41% jump each one a question answered without a support ticket.

As users increasingly turn to AI agents instead of reading docs directly, Mintlify's MCP servers, llms.txt support, and content negotiation allow those agents to pull and process Anaconda's documentation on their own. “Mintlify surfaces Anaconda's name, services, and links in agent-generated answers to people who might never have found the docs otherwise, which is a huge win,” says Englander.

The bottom line

Anaconda turned documentation from a cost center riddled with technical debt into a modern, AI-native experience that actively drives product adoption. “Thanks to Mintlify, our product-led growth motion is stronger than ever before,” says Englander. “We now have the intelligent knowledge infrastructure in place to effectively scale and future-proof our business.”

With that foundation in place, Anaconda's team can now focus on empowering the company's 50 million+ developer community to build with confidence. And they're not slowing down. The team is investing in making their docs fully agent-ready, implementing Skill MD files and visibility components so users can connect directly to their MCP server, and exploring natural language analytics querying to turn usage data into content decisions. For Anaconda, Mintlify has become more than a docs tool. It's the infrastructure for how their teams create, maintain, and expose knowledge at scale.