Sentra vs Glean: Company Brain vs Enterprise Search

TL;DR

• Glean is enterprise search done well. It finds documents fast across your connected tools with permissions-aware, hybrid semantic and lexical retrieval (kore.ai).
• Sentra is a memory layer. It resolves meaning at ingestion and writes facts into one bi-temporal graph your humans and agents share.
• Glean helps people find what exists. Sentra keeps what is true correct over time, with commitment tracking and contradiction detection Glean does not document.
• Pick Glean when employees cannot find documents. Pick Sentra when agents and teams share no memory and cannot track what is still true.
• They complement. Glean surfaces the document, Sentra tracks whether the fact inside it still holds.

What You're Actually Comparing

Glean and Sentra solve different problems, and treating them as rivals leads you to buy the wrong one. Glean is enterprise search. It crawls your connected tools, indexes the documents, and helps employees find what already exists with hybrid semantic and lexical retrieval trained on your company's terminology (gosearch.ai). When someone asks where the latest onboarding doc lives, Glean finds it fast and respects who is allowed to see it.

Sentra is a memory layer. Rather than finding documents, it captures interactions, decisions, and drift into one queryable graph that your teams and your agents share. Its graph records when a fact became true and when it stopped being true, so an agent never restates a deprecated policy as current. Glean tells you which document is newest. Sentra tells you whether the fact inside it still holds.

That distinction matters because AI agents now act on stored knowledge, not just read it. An agent that retrieves a stale fact at query time will execute on it. Glean keeps your documents findable. Sentra keeps your knowledge correct over time and shared across every human and agent that touches it.

Quick-Reference Comparison Table

How This Comparison Was Made

We weighted criteria toward the places where Glean and Sentra overlap or split apart. Document retrieval favors Glean, so we tested how well each surfaces existing files. Agent memory, temporal correctness, and org-wide knowledge sharing favor Sentra, so we examined how each keeps facts correct over time and how state moves between humans and agents. Every claim here traces to Glean's own documentation and reviews (gosearch.ai) and to Sentra's published architecture and MEME benchmark results.

Reactive Search vs. Write-Time Memory

The decisive question separating these two products is when each one does the work of understanding. Glean understands at query time. Sentra understands at write time. That single fork explains most of the differences downstream.

Glean uses an index-based architecture. It crawls your connected systems, copies the documents into a central index, then resolves meaning when you type a query. The search itself is strong, combining semantic and lexical matching trained on your company's terminology. The limitation lives in freshness. Results are only as current as the last index pass, so a document updated this morning may not surface accurately until the crawler catches up. Meaning gets reconstructed on every request rather than stored.

Sentra resolves semantics at ingestion. The moment a fact enters through Slack, email, or a doc, Sentra parses it against a per-organization ontology and writes it into the graph. As Sentra puts it, meaning is a primitive, not a side effect. You are not paying to rediscover what something means on every query, because the structure already exists in the graph before anyone asks.

That difference compounds when you account for change. A query-time system treats your data as a flat collection of embeddings where old facts and new facts sit side by side, equally weighted. Ask it a question and it can return a deprecated fact as if it were current, because nothing in the index records that the fact expired. Sentra's graph is built continuously, and old facts get invalidated rather than overwritten. The graph evolves as your company does.

For employees hunting a document, query-time search works well. For agents that need a fact to stay correct between the time it was written and the time it changed, write-time comprehension is the architecture that holds up.

Knowledge Graph: Personalization vs. Correctness Over Time

Glean and Sentra both run a "knowledge graph," but the two graphs solve different problems. Glean's Enterprise Graph maps relationships between people, content, and organizational context, then uses those signals to rank and personalize search results. When you query, Glean reads your role, your team, and your past behavior to push the most relevant document to the top. The graph exists to improve retrieval.

Sentra's graph exists to track correctness over time. Every fact carries when it became true and when it stopped being true, a property Sentra calls bi-temporal awareness. Old facts are invalidated rather than deleted, and new ones link to the evidence that replaced them. The graph does not rank documents. It records the state of what your company knows and watches that state change.

The practical difference shows up the moment a fact goes stale. Glean's graph will surface the newest document on a topic, ranked by relevance and your role. It assumes the freshest indexed copy is the right answer. Sentra reads the document differently. It flags when a specific fact inside that document is no longer current, because it knows the fact was superseded last month and by what.

Consider a pricing exception promised to a customer. Glean can find the deal doc and the Slack thread where the exception was discussed, ranked by who touched them. Sentra knows the exception was approved on one date and revoked on another, so an agent reading the graph never restates the revoked terms as live. Glean helps you find what exists. Sentra tells you whether what you found is still true. Glean's graph improves on flat search by adding personalization. Sentra's graph improves on flat search by adding time.

Agent Memory: Per-Query Retrieval vs. One Shared Org Graph

The agent-memory gap shows up the moment two agents need to share what they know. Glean Agents run over the same indexed document store that powers Glean Search, so each agent answers by retrieving from that index at query time. What one agent learns in a conversation does not become a fact the next agent can read. The knowledge lives in the source documents, and an agent only sees it after the next crawl and reindex.

Glean's own framing reflects this. The platform is described as "a potential component of enterprise agentic strategies" rather than a complete agentic memory layer, and Glean recommends that organizations planning production multi-step agent workflows "carefully assess the maturity" of those agent capabilities against their needs (kore.ai). Agents that retrieve from an index can find documents well. They do not hold a shared, persistent state that updates as work happens.

Sentra inverts that model. Every team, tool, and model reads and writes to one graph through REST or MCP, so what you teach one agent, every agent remembers. When a support agent records that a customer moved to a new plan, a sales agent reading the same graph sees the change without re-crawling Slack or email. The fact is written once and shared, not rediscovered per query.

That shared surface matters most when a human and an agent work the same problem. A person resolves a billing dispute in a conversation, and an agent picking up the thread later reads the resolution directly from the graph. With an index-based store, the agent waits for the conversation to be captured, indexed, and made searchable before it can act. The graph removes that lag by making the write the moment of comprehension.

Commitment Tracking and Contradiction Detection

Sentra tracks promises, blockers, and follow-ups from the moment they are spoken, which is a category Glean does not address at all. Its action memory captures a commitment as it happens and keeps the supporting evidence attached, rather than waiting for someone to log it in a ticket. Across one company's design partners, Sentra surfaced six commitments across four partners. Four shipped, one slipped (SAML SSO for Acme), and one was quietly dropped. None of that lived in a single document anyone could search.

Contradiction and drift detection work the same way, by watching the graph instead of waiting for a query. Sentra flags when a stored fact contradicts a newer one and when a promise diverges from the actual outcome. In one case it surfaced a verbal commitment to a 60-day MSA exception that was never written down, then connected it to a lost deal. A search tool cannot find a fact that was never recorded.

Glean has no documented equivalent, and that follows directly from its design rather than any oversight. Glean stores indexed copies of documents and resolves meaning when you run a query. A commitment spoken in a meeting and never written into a connected source leaves nothing for Glean to index, so there is nothing to retrieve. Glean answers the question you ask. Sentra raises the question you did not think to ask, because the commitment, the contradiction, or the two-week silence registers in the graph on its own.

Security, Compliance, and Deployment

Both products clear the bar enterprise buyers care about. Glean and Sentra each hold SOC 2 Type II and ISO 27001 certifications, so neither one forces a compliance compromise.

Glean extends its coverage to HIPAA and GDPR through Glean Protect, which adds prompt injection protection and data exposure detection on top of permissions-aware indexing (kore.ai).

Sentra goes further on deployment isolation. Beyond standard cloud, it offers isolated VPC and fully air-gapped on-prem options, and it does not train models on your data. Pick Glean if regulatory certifications like HIPAA decide your purchase. Pick Sentra if your security team requires the graph to run inside your own network with no outbound dependency.

Best For: Glean

Glean is the right call when your core problem is that employees waste time hunting for documents across SaaS tools. Its hybrid semantic and lexical search, trained on your company's terminology, returns permissions-aware results from Google Workspace, Slack, Jira, Salesforce, and dozens of other connected systems (kore.ai). The knowledge graph ranks those results by role and behavior, so a salesperson and an engineer asking the same question see what matters to each.

Glean also fits IT and HR support deflection, where Glean Assistant answers routine questions and points staff to the right policy or ticket. If your team relies on fast retrieval and is not yet running production multi-agent workflows, Glean covers the need well. Reviewers describe Glean Agents as maturing, so weigh that maturity against your requirements before betting agentic operations on it (kore.ai).

Best For: Sentra

Sentra is the right call when you run AI agents that need shared memory and that memory has to stay correct as facts change. A support agent, a sales agent, and a human rep all reading and writing to one org-wide graph means a decision one agent learns becomes context every other agent has. Per-session or per-agent memory cannot do this.

Sentra fits best where commitments, decisions, and drift across time create real operational risk. If a verbal promise of a 60-day MSA exception goes unwritten and costs you a deal, Sentra's action memory captures it from the moment it is spoken and surfaces it before it slips. The bi-temporal graph knows when a fact stopped being true, so your agents never restate a deprecated commitment as current. Pick Sentra when stale facts and lost promises cost you more than slow document search does.

Can You Use Both?

Yes, and for most enterprises that already run Glean, the two fit together cleanly. Glean indexes your SaaS tools and returns the right document fast, with source permissions intact. Sentra sits underneath as the memory layer your agents and teams write to, tracking whether the fact inside that document still holds.

The integration path is direct. Glean surfaces the design spec from last quarter. Sentra knows the deadline in it changed two weeks ago and flags the stale figure before an agent restates it as current. Glean answers "where is this." Sentra answers "is this still true."

Sentra connects through REST and MCP, so any agent that reads Glean's results can also check them against the org-wide graph. You keep Glean for retrieval and add Sentra for temporal correctness, commitment tracking, and contradiction detection. Neither replaces the other.

The Verdict

Pick based on the problem you feel today, not the one you might have later.

Choose Glean if your primary pain is that employees waste time hunting for documents across Slack, Drive, Salesforce, and Jira. Glean indexes those sources, respects existing permissions, and returns the right document fast. For knowledge workers and support deflection, it earns its keep.

Choose Sentra if your primary pain is that your AI agents and your teams share no memory and cannot track what is still true. Sentra resolves meaning at ingestion, stamps every fact with when it became true and when it stopped, and writes it into one graph that every agent and human reads from. When a commitment slips or a fact goes stale, Sentra surfaces it before it costs you a deal.

If both problems are real, run them in their respective lanes. Let Glean find the document on top. Let Sentra track whether the fact inside that document still holds. Glean answers "where is it?" Sentra answers "is it still correct, and who promised what?" The two questions are different, and so are the tools that answer them well.

FAQs

Is Sentra a Glean replacement?

No. Glean is an enterprise search platform that helps employees find documents across connected SaaS tools. Sentra is a memory layer that keeps facts correct over time and shares them across humans and agents, so you can run both for different jobs.

Can Sentra and Glean work together?

Yes. Glean surfaces the document a user searches for, and Sentra tracks whether the fact inside that document is still true. Sentra connects through REST or MCP and reads and writes to one org-wide graph that sits underneath your existing tools.

Does Glean have bi-temporal memory?

No documented bi-temporal awareness exists in Glean's published architecture, which stores indexed document copies rather than a temporal graph. Sentra records when each fact became true and when it stopped being true, so agents never restate a deprecated fact as current. Bi-temporal tracking is the core reason teams add Sentra alongside a search tool.

How does Sentra handle permissions?

Sentra writes interactions and facts into a single graph that your teams and agents query, with source-level evidence attached to every fact. Glean enforces document-level permissions from source systems during indexing, which is the right model for search. Sentra's job is keeping the underlying knowledge correct, which lets agents reason over what is current without re-crawling Slack, email, and docs.

What's the difference between Glean Agents and Sentra's agent memory?

Glean Agents orchestrate workflows over an indexed document store, and Glean itself is described as "a potential component of enterprise agentic strategies" rather than a complete agentic memory layer (kore.ai). Sentra gives every agent one shared graph, so what you teach one agent, every agent remembers. That shared surface lets multiple agents and humans hold the same state instead of rebuilding context per session.