AI tool comparison
Glean AI Workday Integration vs Sup AI
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Productivity
Glean AI Workday Integration
Enterprise AI search that finally speaks Workday's language
75%
Panel ship
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Community
Paid
Entry
Glean now natively indexes Workday HR and finance data, allowing enterprise AI agents to answer queries about org charts, payroll structures, and project data alongside the rest of a company's connected knowledge base. The integration eliminates the need for custom connectors or manual data exports to bring Workday context into AI-assisted workflows. It positions Glean as a unified semantic search layer across both structured enterprise data and unstructured documents.
AI Productivity
Sup AI
Runs 339 LLMs in parallel and downweights the hallucinating ones.
57%
Panel ship
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Community
Free
Entry
Sup AI is an ensemble AI assistant that runs your query through 339 language models simultaneously, measures per-segment confidence across all responses, and synthesizes a final answer that amplifies agreement and suppresses likely hallucinations. The team claims a 52.15% score on Humanity's Last Exam (HLE) — 7.41 percentage points above the single best model — which, if verified, would make it the highest-scoring system on the benchmark to date. The underlying mechanism works like an LLM panel: each model votes on sub-claims within the response, confidence is estimated by agreement density, and the final output surfaces high-confidence segments while flagging uncertain ones. It's designed to reduce hallucination rate on factual tasks, not improve reasoning per se — the models in the ensemble aren't doing collaborative chain-of-thought, they're voting on outputs. Sup AI was built by Ken Mueller (Stanford, CEO) and Scott Mueller (AI Research Scientist) and launched on Product Hunt today. Pricing starts with $10 in free credits, no auto-charge, with a credit card required to start. The HLE benchmark claim is the headline and will face scrutiny — if verified, this is a meaningful research result. If it's cherry-picked, it's still a usable product with a differentiated architecture.
Reviewer scorecard
“The category here is enterprise knowledge graph with connectors, and the direct competitor is Microsoft Copilot for Microsoft 365, which already does this for the M365 ecosystem. Glean's bet is that enterprises run heterogeneous stacks — Workday plus Confluence plus Salesforce plus Slack — and no single platform vendor owns all of it. That's a real bet, not a marketing bet. Where this breaks: the moment Workday ships its own native AI agent layer with deep semantic search (they've been telegraphing this for 18 months), Glean loses its most compelling connector. What kills this in 12 months isn't a competitor — it's Workday itself. But until that happens, the integration is real and the problem is real.”
“The benchmark result is legitimately impressive and the methodology is transparent. My concern is latency — querying multiple models and aggregating adds significant time. For research and high-stakes questions it is worth the wait. For everyday chat it is overkill.”
“The buyer here is the CHRO or CIO, and the budget comes from the enterprise software stack — not a discretionary AI experiment line. That's a real budget, written by someone with authority to commit six figures annually. The moat is connector depth: every new integration Glean adds increases switching cost because re-indexing across 15 enterprise systems is not a weekend project. The stress test is what happens when Workday, ServiceNow, and Salesforce each ship 80% of this functionality natively — Glean needs to be the cross-system layer that none of them can be by definition. That's a defensible wedge, but only if they keep the connector count above the threshold where a point solution becomes painful.”
“The thesis here is specific and falsifiable: enterprise employees will route more operational queries through AI agents than through direct SaaS UIs by 2028, and whoever owns the semantic index wins the interface layer. Workday data is structurally interesting because org-chart and payroll relationships are the connective tissue of almost every business process — an AI that understands headcount context can answer questions that no single-system agent can. The second-order effect is significant: if this works, HR data stops being siloed in Workday and becomes ambient context for every business workflow, which reshapes how companies think about data governance. The trend line is enterprise AI agent adoption, and Glean is on-time — not early enough to define the category alone, not late enough to be irrelevant.”
“Confidence-weighted ensembling is the quiet breakthrough everyone is sleeping on. Individual models plateau — but smart aggregation keeps pushing the frontier. Sup AI scoring 52% on Humanity's Last Exam when no single model breaks 40% proves the thesis.”
“The primitive is a managed connector that syncs Workday's object model into Glean's proprietary search index — which means you don't own the schema, you don't query it directly, and you are fully dependent on Glean's indexing pipeline for freshness and fidelity. There's no public API documentation showing how Workday entities map to Glean's knowledge graph, no published schema, and no developer-accessible endpoint to verify what got indexed. The DX bet Glean made is that enterprise buyers don't want to build this themselves, which is probably true — but the absence of any technical transparency about the integration means you're buying a black box and hoping the Workday objects you care about landed correctly. A skip until they publish the connector schema and query surface.”
“No API, no self-hosting option, and the ensemble approach means your per-query cost is 3-5x a single model call. The benchmark numbers are compelling but I cannot integrate this into a product. Ship an API and I will reconsider.”
“For creative work, ensemble outputs tend to regress toward the mean — you get the most-agreed-upon version of something, which is usually the least interesting version. This is a tool for factual accuracy, not creativity. I'd stick with a single strong model for writing.”
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