When it comes to AI implementation, everyone wants to show off a flashy “Smart Home.” The instant insights. The smart recommendations. Personalized engagement that feels intuitive. Autonomous agents handling routine tasks. Conversational interfaces that "just understand" what users want and need.
But if you've tacked a bunch of flashy features on without focusing on the structure first, you might as well have built your AI house upon the sand.
Without structure — clear entities, relationships, and shared meanings — models guess. Search disappoints. Personalization gets weird. Editors waste time having to manually sort out confusion before they make judgment calls.
Taxonomy is the foundation that makes everything else possible: Better search, smarter engagement, and the intelligence that fuels your editors and marketers to make confident decisions.
It's the footings and framework. Not what anyone tours, but the structural integrity your system needs to support everything built on top of it.
What “taxonomy” actually means (in plain English)
For publishers, taxonomy is your map of meaning — a living structure that tells your systems what’s what and how things relate.
Think:
- Entities: authors, institutions, funders, topics, datasets.
- Relationships: who’s connected to what and how.
- Synonyms: the difference between “ML” and “myelogenous leukemia.”
- External IDs: ORCID, ROR, DOI — the glue that connects your data to the wider world.
- Governance: who maintains it, and how it evolves as fields shift.
It’s not just a list of keywords; it’s a shared understanding of how knowledge hangs together in your corner of the world.
The cost of building on sand
Taxonomy keeps your data, systems, and people aligned. When it's weak or missing, you pay for it everywhere:
- Search & discovery: Keyword soup. Users type “c. elegans behavior” and get a greatest-hits playlist of vaguely related content.
- Editorial triage: Scope and integrity checks become manual detective work; reviewer fit means emailing the same five overburdened experts. Your editors spend hours on tasks that could take minutes.
- Engagement: Audience segments are either creepy (“we saw you looked at X at 2:14am”) or too blunt (“biomed list, send to all”). You’re leaving money on the table, or alienating the people you’re most trying to reach.
- Quality & trust: Paper-mill tactics and ambiguous claims slip through because your system can’t tell what’s normal for this field, this author, this method.
None of that is an “AI problem.” It’s a structure problem.
Building on solid ground: Taxonomy in action.
Here's what happens when you get the foundation right.
Annex Business Media, Canada's largest B2B publisher with 58 brands spanning everything from agriculture to manufacturing to public safety, faced a technical hurdle: they'd grown through acquisitions, creating a patchwork of websites with inconsistent tagging. They knew their archive contained valuable signals about audience interests, but couldn't surface them systematically across their portfolio.
After implementing Alchemist Taxonomy to tag their content, Annex built "Own the Topic" - a new advertising product that identifies micro-audiences based on what readers actually engage with, not just keywords.
Motor control enthusiasts scattered across different industrial titles? Now a targetable audience segment. Small business owners interested in succession planning across markets? That became a paid event with 100 attendees. Fire safety training in public sector? Advertisers can now reach exactly the people engaged with that topic, not just everyone who visited the site.
With a taxonomy that understands the difference between "motor control systems" and "industrial automation," or "succession planning" and "business strategy," Annex can serve both audiences precisely and demonstrate the high-engagement metrics that premium advertisers demand.
Where Alchemist Taxonomy fits in (and why it matters)
At Hum, our north star is making publishers more intelligent — not just more automated.
Alchemist Taxonomy is the foundation that supports everything else.
- For Search (and Alchemist Search): Entity-aware tagging makes search results intuitive and browsing serendipitous — not lucky. When someone searches for "CRISPR applications in oncology," they get relevant content because the system understands the relationships between gene editing techniques, specific cancers, and research methodologies.
- For Engagement (Alchemist Engage): Clean, governed data defines high-signal, respectful audience segments that drive results. No more blasts to "everyone who viewed anything health-related."
- For Editorial Integrity (Alchemist Review): Structured relationships speed up triage, scope checks, and reviewer matching – with better results. When a submission comes in, the system immediately flags relevant conflicts, suggests appropriate reviewers based on their actual expertise (not just keywords), and highlights scope alignment.
When you have a strong taxonomy, you, your team, and your AI systems can make faster, more confident decisions across your organization.
Taxonomy first - then build up!
In a noisy AI year, the publishers winning aren't chasing every new model or tool and tacking it on top of their system. They're investing in the foundation first.
That’s what gives them the edge: search that feels intuitive, engagement that respects people, editorial work that focuses on judgment instead of janitorial tasks, and revenue opportunities that reveal themselves because your systems actually understand what you publish and who cares about it.