SIGNLD vs Tellius: automated findings vs traceable answers

Tellius automates insight discovery and root-cause analysis once your data is connected and loaded into its platform. SIGNLD is a decision intelligence platform that builds a private Knowledge Graph across systems with no modelling phase, and every answer cites the specific source record it came from.

TL;DR

Tellius is an agentic analytics platform built to investigate a dataset automatically, surfacing root causes and finished briefings without a person writing a query. SIGNLD is a decision intelligence platform from Inzata Analytics that connects read-only to 800+ business systems, builds a private Knowledge Graph with no modelling phase, and answers a plain-language question with a Decision Brief: finding, evidence with citations to source records, confidence signal, recommended action. Choose Tellius when your data already lives in a connected warehouse and you want automated root-cause investigation across it. Choose SIGNLD when a CFO needs an answer that spans several disconnected systems and has to trace back to the original record before anyone will act on it.

For the wider context, see our roundup of the best decision intelligence platforms in 2026.

What does Tellius do well?

Tellius, through its Kaiya agent, is built to investigate a question the way an analyst would: it plans a multi-step analysis, reasons across a dataset, and returns a finished brief rather than a chart to interpret. The platform's automated insight discovery and root-cause analysis genuinely reduce the manual work of slicing a dataset a dozen ways to find what moved a metric. Its positioning against chat-based AI failing on multi-table queries is a fair critique of shallow conversational tools, and Tellius's answer, deeper investigation across connected tables, is a real strength.

For a team that already has its data warehoused and connected, Tellius's ability to run a repeatable investigation and return the same answer every time it runs is valuable, particularly for FP&A and RevOps teams tracking recurring questions like churn drivers or pipeline shifts. Its AutoML and vizpad features extend that further into automated modelling and narrative generation, which is a broader analytics surface than a pure question-answering tool.

Where Tellius assumes work has already happened is in getting the data connected in the first place. Its investigation runs on data that is loaded and modelled into the platform, which means the harder cross-system connection work, resolving the same customer or vendor across a CRM, an ERP, and a spreadsheet nobody else sees, still needs to happen before Kaiya can investigate across it.

What is decision intelligence, and how is it different?

Decision intelligence, done well, has two separate jobs: connecting data across systems that were never built to talk to each other, and reasoning across that connected data to produce a specific, evidenced answer. Tools in this category vary in how much of the first job they do for you versus how much falls on a data or analytics team beforehand.

SIGNLD is built around doing the connection work itself. It connects read-only to the systems a company already runs, from ERP and CRM down to the spreadsheets a finance team keeps for reconciliation, and resolves the same customer, vendor, or invoice across all of them inside a private Knowledge Graph, without a separate loading or modelling phase. A CFO asks a question in plain language and gets back a Decision Brief: the finding, evidence with links to the underlying source records, a confidence signal, and a recommended next step. Inference runs on a single-tenant AWS Bedrock instance, a private LLM never trained on your data.

The practical distinction is where the traceability ends. Tellius traces an insight back to the row in the connected dataset it investigated. SIGNLD traces it back one step further, to the original record in the originating system, so the citation points at the actual invoice or contract, not just a table row that was copied from it.

The same question, asked in both tools

Take a question a CFO actually asks: why did customer acquisition cost jump last quarter, and which channel or contract term is responsible. The answer could sit in ad spend records, a CRM's opportunity data, a signed vendor contract with a new pricing tier, or a headcount shift in the sales team. Each of those often lives in a different system.

In Tellius, Kaiya can investigate this well once ad spend, CRM, and headcount data are connected and loaded into the platform. It plans the analysis, tests the candidate drivers, and returns a finished brief with the answer traceable to the rows it examined inside that connected dataset. The quality of that investigation depends on those systems already being joined and modelled inside Tellius, which is real setup work if they were not connected together before.

In SIGNLD, the CFO asks the question directly: why did customer acquisition cost jump last quarter. SIGNLD's Knowledge Graph already links ad spend records, CRM opportunity data, vendor contracts, and headcount because those systems were connected read-only in advance, with entities resolved automatically and no separate loading step. The Decision Brief comes back with a ranked finding, for example that a new vendor pricing tier signed mid-quarter accounts for most of the increase, with a citation to the specific contract clause and the ad platform invoice, a confidence signal, and a recommended action such as renegotiating that tier. The CFO can click through to the original contract, not just a copied row.

Both platforms genuinely automate the investigation once the data is available. The difference is how much connection work happens before that investigation starts, and how far the citation reaches once it finishes.

What you build vs what you ask

	Tellius	SIGNLD
what it models	root causes and drivers surfaced automatically across a connected, loaded dataset	business entities, metrics, and relationships resolved automatically as systems connect, no loading step
who builds it	a data or analytics team connects and loads sources before Kaiya investigates them	no one authors it, entity resolution runs as part of each read-only connection
time to first cross-system answer	fast once sources are connected and modelled inside the platform	minutes after the relevant systems connect
source traceability	traceable to the row in the connected dataset the investigation examined	citations back to the original source record in the originating system, with a confidence signal
where inference runs	Tellius's Kaiya agent, operating on data loaded into the platform	a single-tenant AWS Bedrock instance, private LLM powered by AWS Bedrock, never trained on your data
who it is for	FP&A, RevOps, and analytics teams with data already connected and a recurring investigation to run	a CEO, CFO, or COO at a 10 to 500 employee company with no dedicated data team

Pricing and who ends up owning it

Tellius does not publish self-serve pricing on its site, and enterprise analytics platforms in this category are typically sold through a direct sales process scoped to data volume, users, and connected sources. Check Tellius's current published materials for specifics before budgeting. Ownership of the deployment tends to sit with an analytics or data team, since sources need to be connected, loaded, and kept current for Kaiya's investigations to stay accurate.

SIGNLD's plans are listed on /pricing, with a Free Forever tier and a Growth trial that needs no credit card. There is no loading or modelling step to budget for separately, because entity resolution happens as part of connecting a system rather than as a project a data team runs beforehand. Ownership sits with the person asking the question, since there is no connected-dataset estate for someone else to maintain on their behalf.

Where Tellius is the better choice

Tellius is the better choice for a team that already has its data connected and modelled, and wants automated, repeatable investigation across it, especially for FP&A and RevOps functions tracking the same kind of question month after month. Its ability to plan a multi-step analysis and return a consistent finished brief on a recurring basis is a genuine strength for a company that has already solved the harder problem of getting its systems talking to each other. If your data team has built that foundation, Tellius makes the investigation on top of it faster and more consistent than a person running it manually.

SIGNLD's /why-us page is explicit that its focus is the connection work most companies have not done yet, not out-investigating a platform built for an already-connected dataset. The two solve adjacent problems: one automates the investigation once data is joined, the other does the joining and the investigation together with citations to the original record. A CFO at a smaller company without a data team to build that connected dataset needs the second, and that is the specific gap this comparison is about.

Related reading: What is a knowledge graph for business, How SIGNLD builds a knowledge graph, what traceable AI for business analytics means, Decision Brief in the concepts glossary, and the difference between a dashboard and a decision cover the surrounding concepts in more depth. See also /concepts and /connectors for how SIGNLD's Knowledge Graph and system connections work.

Key takeaways

Tellius is an agentic analytics platform built to investigate a dataset automatically, surfacing root causes and finished briefings without a person writing a query.
Decision intelligence, done well, has two separate jobs: connecting data across systems that were never built to talk to each other, and reasoning across that connected data to produce a specific, evidenced answer.
Tellius is the better choice for a team that already has its data connected and modelled, and wants automated, repeatable investigation across it, especially for FP&A and RevOps functions tracking the same kind of question month after month.
Tellius traces an insight back to the row in the dataset it investigated inside the platform.
If your data still lives in disconnected systems and nobody has loaded it anywhere for an investigation to run against, that gap is what SIGNLD is built to close first.

FAQ

Is Tellius a decision intelligence tool?

Tellius describes itself as an agentic analytics platform, focused on automated insight discovery and root-cause analysis across connected data. It shares goals with decision intelligence, particularly automating the investigation step, but it depends on data already being connected and loaded rather than doing that connection work itself.

Can SIGNLD replace Tellius entirely?

Not always. Tellius's recurring, repeatable investigations on an already-connected dataset stay valuable for FP&A and RevOps teams tracking the same question over time. SIGNLD is built for companies that have not connected their systems yet and need both the connection and the answer, with citations reaching back to the original record.

Does SIGNLD need my data loaded into a platform first, like Tellius does?

No. SIGNLD connects read-only to your systems, including spreadsheets, and resolves entities across them automatically as part of that connection. There is no separate loading or modelling step before asking a question.

How far back does source traceability go in each tool?

Tellius traces an insight back to the row in the dataset it investigated inside the platform. SIGNLD traces an answer back one step further, with a citation to the original record in the system it came from, such as the specific invoice or contract clause.

Who should buy SIGNLD instead of Tellius?

A CEO, CFO, or COO at a 10 to 500 employee company running many disconnected systems with no dedicated data team, who needs a specific, evidenced answer that cites the original source record rather than an investigation that assumes the data is already connected.

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If your data still lives in disconnected systems and nobody has loaded it anywhere for an investigation to run against, that gap is what SIGNLD is built to close first. Try SIGNLD free and connect a system in minutes, or Browse all articles for more on how decision intelligence platforms differ in where they start.