The difference: indexing what exists vs. fixing what is wrong
An enterprise search product answers the question “where is it?” across as many systems as possible. That is valuable — and it is not the problem most Microsoft-365-centric organisations have. Their problem is that the content contradicts itself: five versions of the same procedure, copies in forgotten sites, 2019 files that still rank first. Any search or assistant built on top of that inherits it.
TensorLot treats this as a data-quality problem first. It connects sources read-only, gives every document a health score (0–100), records contradictions, duplicates and outdated content as findings with severity and affected documents, and guides the clean-up with action rules (URL prefix › library › site › tenant) — while the person or agent acts with their own rights. Only then is the knowledge served: to people through Tensor Bot with citations, and to agents through one MCP endpoint with 11 governed tools.
The consequence runs in two directions at once. A corpus without duplicates and contradictions produces answers you can rely on — and it produces them with fewer tokens: retrieval that pulls four near-identical versions of a document into context pays for all four on every query, then asks the model to reconcile them. In TensorLot pilot deployments (2026) curated, deduplicated context meant up to 70 % fewer tokens per answer. Reliability and cost are the same lever.
What to evaluate before comparing vendors
- Where your knowledge actually lives. Microsoft-365-centric estates have different requirements than estates spread across dozens of SaaS tools.
- Whether your content is trustworthy. If a Copilot or search pilot produced confidently wrong answers, the corpus is the problem — connector breadth will not fix it.
- Data residency. EU hosting and GDPR posture are procurement gates in most European enterprises, not preferences.
- Time to first evidence. How long before you know what is actually wrong with your content? (TensorLot: a 2–4 week proof of concept on your own documents, with a full quality report.)
- Agents. If you plan to run agents in Copilot Studio, Claude or your own framework, what do they read from — and is every call scoped, governed and logged?
- Ongoing inference cost. Every duplicate and superseded document that enters a context window is billed on every query; corpus quality is a recurring cost line, not a one-off.
Comparison
| Glean | TensorLot | |
|---|---|---|
| What it is | Enterprise search & AI assistant across work apps | Enterprise AI knowledge foundation: data quality, clean-up, governed serving layer |
| Semantic search / cited answers | Yes | Yes — Tensor Bot, every answer with its sources |
| Duplicate & near-duplicate detection | Not a primary capability | Core capability — findings with canonical candidate |
| Contradiction detection between documents | Not a primary capability | Core capability — pairs side by side, confidence, verdict |
| Outdated / stale content | Limited | Yes — health score per document and tenant, outdated flagged |
| Fixing the source of the problem | No | Guided remediation + action rules; person or agent acts with own rights |
| Microsoft 365 / SharePoint | Connector-based | Sites, libraries, site collections; read-only, incremental sync, permissions mirrored |
| Other sources | Broad SaaS coverage | Confluence, Jira, GitHub |
| AI agents | Glean agents | Open MCP server: 11 governed tools, default-deny, one endpoint, audit log per call — Copilot Studio, Claude, any MCP client |
| Model / platform | Glean | Any — Copilot Studio, Claude, OpenAI, Azure OpenAI, LangGraph |
| Effect on inference cost | Not addressed | Deduplicated, curated context — up to 70 % fewer tokens per answer (2026 deployments) |
| Diagnostic before rollout | Pilot (see vendor) | Proof of Concept, 2–4 weeks, full quality report on your own documents |
| Pricing model | Quote-based, per-seat (see glean.com) | Proof of Concept · Knowledge Management · Enterprise — on request; not per-token |
| Hosting / residency | See vendor | EU; private cloud / own VPC on request |
Where Glean fits
Glean is the stronger fit for organisations whose knowledge is spread across a long tail of SaaS applications — Slack, Salesforce, Zendesk, Notion and many more — and who want a single assistant layer across all of them. Breadth of coverage is the problem Glean is built for.
For estates centred on Microsoft 365, Confluence, Jira and GitHub — which covers most enterprise engineering and knowledge work — coverage is not the constraint. The constraint is that the content contradicts itself, and that every contradictory copy is retrieved and billed on every query. That is the layer TensorLot adds; and where both exist, TensorLot can sit underneath: it cleans and governs the knowledge, Glean searches across apps.
TensorLot vs Glean — frequently asked questions
Is TensorLot an alternative to Glean?
For organisations whose knowledge sits in Microsoft 365 / SharePoint, Confluence, Jira and GitHub — yes. TensorLot adds the consistency analysis (duplicates, contradictions, outdated content, health scores) that enterprise search products do not perform, serves cited answers to people and an open MCP server to agents, and is priced per plan (POC, Knowledge Management, Enterprise) rather than per seat. For knowledge spread across dozens of SaaS tools, Glean’s breadth is the stronger fit — or both are used together.
Can TensorLot and Glean be used together?
Yes. TensorLot works on the content layer — it cleans, scores and governs the documents and serves them with citations — while Glean searches across applications. Nothing is moved; TensorLot connects sources read-only.
Which systems does TensorLot connect to?
Microsoft 365 / SharePoint (sites, libraries, site collections), Confluence, Jira and GitHub — read-only, with incremental sync and permissions mirrored from the source system.
How does TensorLot reduce AI token costs?
Duplicate, near-duplicate and superseded documents are retrieved into the context window on every query and billed every time. TensorLot identifies that redundancy, marks the canonical version and serves curated context — in pilot deployments (2026) up to 70 % fewer tokens per answer, on any model.
Why do enterprise search tools return wrong answers?
Because they retrieve what exists. Most enterprise content sets contain duplicates, superseded versions and direct contradictions. Retrieval returns one of them; the user has no signal that the others disagree. TensorLot detects those conflicts as findings before they become answers.