SIGNLD vs Glean: documents are not the same as your numbers

Glean searches and summarizes unstructured content, documents, messages, and tickets, across the workplace apps your team already uses. SIGNLD connects to your operational and financial systems, resolves entities such as invoices and work orders across them, and returns a numeric answer with a source citation, not a document summary.

TL;DR

Glean is enterprise work search: it indexes Google Drive, Slack, Confluence, Jira, email, and tickets, respects existing permissions, and builds a people graph so employees can find who knows what. It retrieves and summarizes what has already been written down. SIGNLD models the numbers behind your business, resolving an invoice to a work order to a ledger line into entities and metrics, and answers questions those documents were never written to answer. Companies asking "what did we decide" want Glean. Companies asking "what is actually happening in the numbers" want SIGNLD.

For the wider context, see our roundup of the best business knowledge graph platforms in 2026.

What is Glean actually built for?

Glean is a workplace search and assistant platform that indexes the unstructured content spread across a company's SaaS tools. It connects to Google Drive, Slack, Confluence, Jira, email, support tickets, and similar systems, then builds a permission-aware index so an employee searching for "renewal terms for our largest client" gets the document, thread, or ticket where that answer already exists, without seeing content they are not authorized to view.

Alongside search, Glean builds a people graph: it tracks who wrote what, who is an expert on a topic, and who to ask when a document does not exist. Its assistant summarizes retrieved content and answers questions by pulling from what has already been written. That is the mechanism, and it is a genuinely different one from modeling numeric business data. Glean does not parse an invoice line, join it to a purchase order, and calculate a variance. It finds and summarizes the documents that already describe those events in prose.

What is SIGNLD actually built for?

SIGNLD is a decision intelligence platform from Inzata Analytics built to answer questions about the numbers running through a business, not the documents describing them. It connects read-only to more than 800 systems: ERP, CRM, field service, finance platforms, and the spreadsheets operations teams maintain by hand. Those connections feed a Knowledge Graph that resolves entities across systems, so an invoice, the work order that generated it, and the ledger line it settled into are recognized as related facts about the same underlying event.

When a CFO or COO asks a question in plain language, SIGNLD returns a Decision Brief: a ranked answer, links back to the specific source records, and a confidence signal. It works without a warehouse, and spreadsheets count as first-class sources. The target user runs a company with 10 to 500 employees, 10 to 40 or more siloed systems, and no dedicated data team able to join those systems by hand every time a question comes up.

Where the two genuinely differ

The clearest difference is what each product treats as its unit of truth. Glean's unit is a document, message, or ticket, something a person wrote. SIGNLD's unit is a modeled entity such as a customer, order, or invoice, with metrics computed from structured records. Asking Glean "why did margin drop in the northeast region" returns whatever a person happened to write about it, if anyone did. Asking SIGNLD the same question returns a calculation traced to the ledger and order records that produced the number.

A second difference is permission architecture. Glean's people graph and permission-aware retrieval exist because institutional knowledge is scattered across individuals and their access varies by role, which is the right model for document search. SIGNLD's traceability exists to let a business owner verify a number, not to route a search result around access control on prose.

A third difference is what happens when nobody wrote it down. Glean can only surface knowledge that already exists in some document or message. If margin dropped and nobody wrote an explanation anywhere, Glean has nothing to retrieve. SIGNLD computes the answer directly from the transactional systems, whether or not a human ever described the event in writing.

	Glean	SIGNLD
what it models	documents, messages, tickets, and people, indexed with source permissions preserved	business entities, metrics, and their relationships across finance, CRM, and operational systems
who builds it	IT connects content sources, the index and people graph build from that content	no one authors it, entity resolution runs as part of each read-only connection
time to first cross-system answer	fast for document questions, unavailable for questions that require joining transactional records	minutes after the relevant systems connect
source traceability	links back to the document, thread, or ticket it summarised	citations back to the source record in the originating system, with a confidence signal
where inference runs	Glean's hosted infrastructure across selected commercial model providers	a single-tenant AWS Bedrock instance, private LLM powered by AWS Bedrock, never trained on your data
who it is for	employees searching institutional knowledge across Slack, Drive, Confluence, and Jira	CEOs, CFOs, and COOs asking questions that span invoices, orders, and ledger lines

Who has to build the model?

Glean requires connecting source systems and configuring permission mappings, largely automated through its connectors, plus tuning for which content sources matter most.
Glean's people graph builds itself from usage and authorship patterns rather than a manually defined schema.
SIGNLD's Knowledge Graph is built from the read-only connections to operational and financial systems, resolving entities such as customer or invoice automatically.
Neither product requires a data engineer to hand-write a data model before the first useful result appears.
A business owner using SIGNLD reviews the source citation behind a number the same way an employee using Glean reviews the linked document behind a search result.

How long until you get a cross-system answer?

Glean answers "cross-system" questions in the sense of searching across many document sources at once, and that search typically returns in seconds once connectors are configured, which itself is usually a matter of an IT team enabling app integrations. What it returns, though, is the best matching document or summary of several documents, not a computed number joined across financial and operational records.

SIGNLD answers a different kind of cross-system question: one that requires resolving the same entity across systems that were never designed to talk to each other, then computing a metric from the result. Connect a first system in about 15 minutes and get a first answer the same day. For a question like which customers have both an overdue invoice and an open support ticket, SIGNLD returns the answer with source links in minutes, because the entity resolution already happened in the graph before the question was asked.

Where Glean is the better choice

Glean is the stronger choice for finding institutional knowledge that already exists somewhere in writing. If an employee needs the current version of a pricing policy, the thread where a decision got made, or the person who last worked on a client account, Glean's permission-aware search and people graph solve that faster than anyone manually searching five tools. Its assistant is genuinely useful for onboarding, for reducing repeated Slack questions, and for surfacing prior discussion before a meeting. For any question whose answer is "someone already wrote this down, we just cannot find it," Glean is the right tool, and SIGNLD does not attempt this at all. Its permission-aware retrieval also matters in larger organizations where access control across dozens of content sources would otherwise be managed by hand, one folder at a time.

What running both looks like

The two tools answer different categories of question, so running both is common rather than redundant. Glean becomes the front door for "has anyone already answered this" questions: policy lookups, prior decisions, who owns a project. SIGNLD becomes the front door for "what is actually happening in the numbers" questions: margin movement, customer risk, supplier delays, anything that requires joining transactional records nobody summarized in prose.

A practical split for a company running both: point employees to Glean for institutional and procedural knowledge, and point CFOs, COOs, and operations leaders to SIGNLD when the question is quantitative and spans systems. Related reading on the modeling side: what is a knowledge graph for business, Decision Brief in the concepts glossary, what traceable AI for business analytics means, the difference between a dashboard and a decision, and how SIGNLD builds a knowledge graph.

Your data stays yours with SIGNLD: connections are read-only, inference runs on a single-tenant AWS Bedrock instance, and nothing you connect trains a shared model. See the full connector list at /connectors and how entities and metrics get defined at /concepts.

Key takeaways

Glean is enterprise work search: it indexes Google Drive, Slack, Confluence, Jira, email, and tickets, respects existing permissions, and builds a people graph so employees can find who knows what.
The clearest difference is what each product treats as its unit of truth.
Glean is the stronger choice for finding institutional knowledge that already exists somewhere in writing.
Glean's pricing is not fully public and is typically structured as a per-seat fee combined with a platform or implementation charge, negotiated per account.
If the question in front of you needs a number computed from records across systems, not a document someone already wrote, Try SIGNLD free and connect a system to see a traceable answer today.

FAQ

Is Glean a good alternative to SIGNLD for financial questions?

No. Glean retrieves and summarizes documents, messages, and tickets, it does not join an invoice to a work order to a ledger line or compute a metric from transactional records. For a question like "why did margin drop," Glean can only return what someone already wrote about it, if anything.

Does SIGNLD do employee search across Slack and Confluence?

No. SIGNLD connects to operational and financial systems to model entities such as customers, orders, and invoices, and it does not index workplace messages or wikis. For finding institutional knowledge already written down, a tool like Glean is the right fit.

Can Glean answer a question that spans multiple financial systems?

Glean can search across many connected apps at once, but the result is a document or summary, not a calculated number. If the answer requires resolving the same customer or transaction across a CRM, an ERP, and a spreadsheet into one figure, that is a modeling task Glean's search does not perform.

How much does Glean cost?

Glean's pricing is not fully public and is typically structured as a per-seat fee combined with a platform or implementation charge, negotiated per account. Contact Glean directly for current figures, since published list pricing is limited.

Which tool should a CFO use to check why a metric changed?

A CFO checking why a number changed should use a tool that computes the metric from source records and links back to them, which is what SIGNLD does. Glean is better suited to finding whether a colleague has already written an explanation somewhere in existing documents.

Can SIGNLD and Glean run in the same company without overlap?

Yes. Glean serves as workplace search for documents, messages, and institutional knowledge, while SIGNLD answers quantitative cross-system questions by modeling entities and metrics. Most companies running both see little overlap because the underlying content and the unit of answer are different.

Try SIGNLD free

If the question in front of you needs a number computed from records across systems, not a document someone already wrote, Try SIGNLD free and connect a system to see a traceable answer today. For more comparisons like this one, Browse all articles.