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Backpack Models | Backpack Language Models are sequence models with an interface for interpretability. — 18 websites ranked by shared content topics, category and on-page relevance.

Each result shows its full tech stack, contacts and AI-policy — not just a name · Browse all sites in Artificial Intelligence →

Domain	Match	Title	Country/Lang	Category	AI files	Contact	AI-protection
vivritt.com ↗	66 match 1 shared topics	Vivritt AI — Interaction Intelligence for Enterprises	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
spiralthink.com ↗	66 match 1 shared topics	SpiralThink – Long‑Context AI Efficiency Layer for Large Language Models	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
spiralthink.ai ↗	66 match 1 shared topics	SpiralThink – Long‑Context AI Efficiency Layer for Large Language Models	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
vlmbook.com ↗	66 match 1 shared topics	Vision-Language Models — Building VLMs with Hugging Face	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
rolisz.com ↗	66 match 1 shared topics	Unlocking value from text with large language models - Roland Szabó	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
ssga.ai ↗	66 match 1 shared topics	SSGA.ai — Applied Artificial Intelligence for Modern Enterprises	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
mechanisticinterpretability.com ↗	66 match 1 shared topics	Mechanistic Interpretability Lab \| Understand AI. Build Safer Agents.	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
sparknlp.org ↗	66 match 1 shared topics	Spark NLP - State of the Art NLP Library for Large Language Models (LLMs)	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
spearfish.ai ↗	66 match 1 shared topics	Spearfish.ai — Contextual Intelligence for Contact Centers — Spearfish.ai	en	artificial-intelligence WordPress	robots llmsaihumans	emailphone	none
ayasdi.com ↗	66 match 1 shared topics	Artificial Intelligence for the Enterprise \| Ayasdi	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	partial · 8
4in-tech.com ↗	65 match 1 shared topics	4inTech — AI Transformation for Enterprises	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
meetgapless.ai ↗	65 match 1 shared topics	Gapless — Talk Across Languages, No Gaps \| Real-time AI Interpreter	en	artificial-intelligence	robotsllmsaihumans	emailphone	partial · 8
layerwise.ai ↗	65 match 1 shared topics	layerwise \| Interpretable AI Safety Research and Tooling	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
aacgulp.com ↗	65 match 1 shared topics	GuLP AAC - The first AAC device for Gestalt Language Processors	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
meansofproduction.io ↗	65 match 1 shared topics	Means of Production — $MOP AI Language Model	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
spinn3r.com ↗	65 match 1 shared topics	Datastreamer \| The AI Interface for Social Data - Datastreamer	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
3gp.ai ↗	65 match 1 shared topics	3GP.AI — Intelligent Solutions for the Modern Enterprise	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
caracalintelligence.com ↗	65 match 1 shared topics	Caracal Intelligence \| Risk Intelligence for Operators	en	artificial-intelligence	robotsllmsaihumans	emailphone	none

How the match score works

Each match is a 0–100 similarity score — the higher it is, the more two sites resemble one another. It’s computed automatically from our own crawl data (never from what a site says about itself) by combining several independent signals, so a high score means several of them point the same way:

shared topics how many of the same fine‑grained content topics (IAB sub‑categories) both sites cover — the main signal.
same category whether they sit in the same business / industry category.
content & title how closely their titles and page descriptions line up in wording and meaning.

No single signal decides the result — they’re blended together. Treat the score as a way to rank candidates rather than an absolute percentage; the chips on each result show which signals contributed.