SIGNLD vs AtScale: cubes vs a business knowledge graph

AtScale puts one governed, dimensional semantic layer over a cloud warehouse so every BI tool and AI agent queries the same dimensions and measures. SIGNLD skips the warehouse and the modelling step entirely, connecting directly to the systems themselves to answer a question that spans them.

TL;DR

AtScale is a universal semantic layer that sits over a cloud warehouse, defining one governed set of dimensions and measures with aggregate acceleration so Excel, Tableau, Power BI, and SQL clients all see the same numbers. SIGNLD is a decision intelligence platform that connects read-only to 800+ business systems, builds a private Knowledge Graph, and returns a Decision Brief with source citations and a confidence signal. Choose AtScale if your warehouse data is modelled and you need one fast, consistent number across many BI tools. Choose SIGNLD if the question spans a system that was never loaded into the warehouse at all.

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

What is AtScale actually built for?

AtScale is built for organizations that already have a cloud warehouse full of modelled data and want to stop reconciling conflicting numbers across BI tools. It sits between the warehouse and the reporting layer, defining dimensions, measures, and hierarchies once, then serving that governed model to Excel, Tableau, Power BI, and any SQL client. Analysts and AI agents query one consistent definition of revenue or churn instead of each tool computing its own.

A genuine strength is aggregate acceleration. AtScale automatically builds and manages pre-aggregated tables behind the scenes, so a dashboard querying billions of rows returns in seconds instead of scanning the full warehouse each time. This keeps large-scale reporting fast without an analyst hand-tuning materialized views.

AtScale still needs the underlying data modelled and loaded into the warehouse first, and needs a modelling owner, usually a data or analytics engineering team, to define the semantic model and its hierarchies. Pricing is quote-only. The buyer is a BI or data platform team standardizing reporting, not a leader asking a one-off question.

What is SIGNLD actually built for?

SIGNLD is a decision intelligence platform from Inzata Analytics. It connects read-only to the systems a company already runs, from CRM and support tools down to the spreadsheets finance keeps updated by hand, and resolves the same customer, invoice, or renewal across all of them inside a private Knowledge Graph. There is no semantic model to define and no warehouse required before the first question gets answered.

A leader asks a plain-language question and receives a Decision Brief: the finding, evidence with links back to source records, a confidence signal, and a recommended action. Inference runs on a single-tenant AWS Bedrock instance, using a private LLM powered by AWS Bedrock that is never trained on your data. You can connect the first system in 15 minutes and get the first answer in minutes.

SIGNLD's target user is a CEO, CFO, or COO at a company of 10 to 500 employees with no dedicated data team, often running 10 to 40 or more systems that were never consolidated into a single reporting layer.

Where the two genuinely differ

AtScale answers questions inside the model it was given. If revenue, customer, and product dimensions are defined and the warehouse holds the data, AtScale returns a fast, consistent number to whichever BI tool asked. The question nobody can answer with AtScale is the one spanning a system nobody loaded, such as support tickets against renewals against invoices, because that data never reached the warehouse or the semantic model.

SIGNLD starts from the opposite direction. It connects directly to support tools, billing systems, and spreadsheets, resolving entities across them automatically, so the cross-system question is the default case rather than an exception requiring a new modelling project. This trades AtScale's speed at warehouse scale for the ability to reach data that was never centralized.

A second difference is output shape. AtScale returns rows and aggregates to a BI tool, which a person then reads as a chart or table. SIGNLD's output is already a Decision Brief, a written finding with evidence and a recommended action, with no dashboard required.

A few structural differences worth naming directly:

AtScale accelerates and governs queries against a warehouse someone already modelled; SIGNLD builds its graph from systems directly, warehouse or not.
AtScale serves BI tools and SQL clients; SIGNLD returns a Decision Brief a non-technical leader can act on directly.
AtScale needs a modelling owner to define dimensions and hierarchies; SIGNLD resolves entities automatically as systems connect.
AtScale's buyer is a BI or data platform team; SIGNLD's buyer is the executive who has the question.
AtScale's aggregate acceleration targets warehouse scale; SIGNLD's confidence signal is tied to source evidence across systems.

	AtScale	SIGNLD
what it models	dimensions, measures, and hierarchies defined once over a cloud warehouse	business entities, metrics, and relationships resolved automatically as systems connect
who builds it	a BI or data platform team defining and maintaining the semantic model	no one authors it, entity resolution runs as part of each read-only connection
time to first cross-system answer	weeks, after warehouse data is modelled and the semantic layer is defined	minutes after the relevant systems connect
source traceability	traceable to the warehouse tables the semantic model was built on	citations back to the source record in the originating system, with a confidence signal
where inference runs	query acceleration inside AtScale's engine, serving BI tools and SQL clients	a single-tenant AWS Bedrock instance, private LLM powered by AWS Bedrock, never trained on your data
who it is for	BI and data platform teams standardizing reporting over a modelled warehouse	companies of 10 to 500 employees running 10 to 40+ systems with no dedicated data team

Who has to build the model?

In an AtScale deployment, a data or analytics engineering team defines the semantic model: which dimensions exist, how measures aggregate, and how hierarchies like region or product line nest. This assumes the underlying data already sits in the warehouse in a usable shape, and it grows each time a new dimension needs representing.

In SIGNLD, nobody builds that model as a separate step. Connecting a system triggers automatic entity resolution, so a customer ID in the CRM and an account number in the billing system get linked without a person writing the join. A leader still reviews the evidence behind an answer, but the modelling labor AtScale assigns to a platform team is not a phase SIGNLD requires. See how this works in more detail at /concepts.

How long until you get a cross-system answer?

With AtScale, time to a first answer depends on how much of the relevant data is already clean in the warehouse and how mature the semantic model is. A team with an existing, well-maintained model can add a new measure or dimension and see it across every connected BI tool within days. A team without that foundation faces weeks of modelling work before a query against an unrepresented relationship returns anything trustworthy.

With SIGNLD, the first system connects in 15 minutes through one of over 800 supported connectors, and the first answer follows in minutes. A question like which renewing accounts also have open support tickets does not wait on a modelling project, because entity resolution runs automatically as each system connects, whether or not that system ever reaches a warehouse.

Where AtScale is the better choice

AtScale is genuinely the better choice for organizations with a mature warehouse and a BI standardization problem across multiple tools and departments. If Excel, Tableau, Power BI, and a handful of SQL clients are all computing slightly different versions of the same metric, AtScale's governed semantic layer and aggregate acceleration solve that directly, keeping large-scale reporting fast and consistent without hand-built materialized views.

Teams with a BI or data platform function that already owns warehouse modelling get more from AtScale's one-model, many-clients approach than from SIGNLD's automated resolution. That consistency depends on the underlying warehouse data being complete, which means a system left out of the warehouse stays out of AtScale's answers too.

What running both looks like

Some organizations run AtScale for governed, high-volume BI reporting across departments, keeping every dashboard consistent, and run SIGNLD alongside it for the leadership team's cross-system questions that reach beyond what the warehouse holds. AtScale's model stays the system of record for standardized reporting, while SIGNLD answers the question a CEO asks about a customer who is both up for renewal and filing tickets, without waiting for a new dimension to be added to the semantic layer.

Your data stays yours in SIGNLD through read-only connections, a single-tenant AWS Bedrock instance for the private LLM, and a policy of never training on customer data. See /security for specifics and /concepts for how the Knowledge Graph and Topic model fit together.

Key takeaways

AtScale is a universal semantic layer that sits over a cloud warehouse, defining one governed set of dimensions and measures with aggregate acceleration so Excel, Tableau, Power BI, and SQL clients all see the same numbers.
AtScale answers questions inside the model it was given.
AtScale is genuinely the better choice for organizations with a mature warehouse and a BI standardization problem across multiple tools and departments.
Sometimes, if those systems already load into the warehouse AtScale sits on.
If the question you need answered spans a system that never made it into your warehouse, start with the tool that reaches it directly.

FAQ

Is AtScale a good alternative if I do not have a warehouse?

Not really. AtScale is built to sit on top of a cloud warehouse and needs the underlying data modelled there first. If your systems are spread across a CRM, a support tool, and spreadsheets that never reached a warehouse, SIGNLD connects to those directly without requiring one.

Does SIGNLD replace my BI tool?

No. SIGNLD answers a specific cross-system question with a Decision Brief rather than serving dashboards to Tableau or Power BI the way AtScale does. Many teams keep a BI tool for standardized reporting and use SIGNLD for the question that spans systems the dashboard was never built to cover.

Can AtScale and SIGNLD read the same source systems?

Sometimes, if those systems already load into the warehouse AtScale sits on. AtScale operates on data once it reaches that warehouse, while SIGNLD connects directly to the originating system through one of its connectors. Coverage depends on how much of a company's data is centralized already.

What does AtScale cost compared to SIGNLD?

AtScale is priced by quote based on data volume and deployment scope, so a public number is not available. SIGNLD's current plans are listed on /pricing and do not require negotiating warehouse scale.

Which one is faster for a dashboard querying billions of rows?

AtScale, when the data already lives in a modelled warehouse. Its aggregate acceleration is built specifically for that scale of BI query. SIGNLD is built for a different job, a cross-system answer delivered as a Decision Brief rather than a high-volume dashboard query.

Try SIGNLD free

If the question you need answered spans a system that never made it into your warehouse, start with the tool that reaches it directly. Try SIGNLD free and connect a system in minutes, or Browse all articles for more on how the Knowledge Graph compares to other approaches.

SIGNLD vs AtScale: cubes, semantic layers, and the question nobody can answer

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