SIGNLD vs RelationalAI: a graph you write vs a graph that builds

RelationalAI runs graph reasoning and optimization natively inside Snowflake, once a team declares the model in a relational modelling language. SIGNLD builds the graph itself from read-only connections and hands back a plain-language answer with no modelling step first. RelationalAI is a knowledge graph coprocessor that runs as a native app inside a customer's Snowflake account.

TL;DR

RelationalAI is a knowledge graph coprocessor that runs as a native app inside a customer's Snowflake account, where a team writes models and rules in a relational modelling language and runs graph algorithms next to the warehouse data. SIGNLD is a decision intelligence platform that connects read-only to 800+ business systems, builds the graph itself, and returns a Decision Brief with source citations and a confidence signal. Choose RelationalAI if your data lives in Snowflake and you have engineers to write the model. Choose SIGNLD if the question spans systems never loaded into a warehouse.

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

What is RelationalAI actually built for?

RelationalAI is built for teams already running analytics in Snowflake who want graph reasoning and optimization without moving data out of the warehouse. It installs as a native application, so the graph computation happens inside the customer's own Snowflake account rather than in a separate service. A team declares entities, relationships, and rules in a relational knowledge representation language, and RelationalAI compiles that into queries that run against the warehouse tables directly.

This is a genuine strength: because computation runs inside Snowflake, there is no separate graph database to stand up and no export pipeline outside the warehouse's own governance boundary. RelationalAI also supports graph algorithms and constraint-based optimization, useful for problems like supply chain allocation or fraud detection.

The tradeoff is what it assumes going in. RelationalAI expects the relevant data to already be modelled in Snowflake, and expects a team of data or analytics engineers to write the declarative rules that define the graph. Pricing is quote-only through Snowflake Marketplace, tied to Snowflake compute. The buyer is a data platform team, not a CFO asking a direct 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 ERP down to the spreadsheets a finance team keeps updated by hand, and resolves the same customer, order, or invoice across all of them inside a private Knowledge Graph. There is no relational modelling language to learn and no rule set to author before the first answer comes back.

A leader asks a plain-language question and receives a Decision Brief: the finding, evidence with links back to the original 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, with no warehouse required.

SIGNLD's target user is a CEO, CFO, or COO running a company of 10 to 500 employees with no dedicated data team, often juggling 10 to 40 or more disconnected systems nobody has time to model.

Where the two genuinely differ

The starting assumption is the clearest split. RelationalAI assumes your data already sits in Snowflake, cleaned and structured enough for a team to write graph rules against it. SIGNLD assumes the opposite: that your data is scattered across systems that were never consolidated, including spreadsheets that never made it anywhere near a warehouse, and it does the consolidation itself as part of connecting.

The second difference is who does the work and when. RelationalAI's value shows up after a team writes the model, meaning entities, relationships, and optimization rules, in its modelling language. That model is precise and reusable once built, and it runs where the data already lives. SIGNLD's entity resolution runs automatically as each system connects, trading that precision for speed, since nobody has to declare in advance that an invoice number in accounting matches an order ID in the CRM.

A third difference is output. RelationalAI returns results an analyst still has to interpret, typically through Snowflake itself or a BI layer on top. SIGNLD's output is already a Decision Brief, a written answer a leader can act on directly, with evidence attached.

A few structural differences worth naming directly:

RelationalAI computes graph logic you declare inside Snowflake; SIGNLD builds the graph from connected systems with no declaration step.
RelationalAI requires data already modelled in the warehouse; SIGNLD works without a data warehouse at all, spreadsheets included as first-class sources.
RelationalAI's buyer is a data platform or analytics engineering team; SIGNLD's buyer is the executive who has the question.
RelationalAI runs computation inside the customer's Snowflake account; SIGNLD runs inference on a single-tenant AWS Bedrock instance.
RelationalAI's rules are declarative and precise once written; SIGNLD's confidence signal is probabilistic and tied to source evidence.

	RelationalAI	SIGNLD
what it models	entities, relationships, and optimization rules declared in a relational modelling language, run against Snowflake tables	business entities, metrics, and relationships resolved automatically as systems connect
who builds it	data engineers or analytics engineers writing the model inside Snowflake	no one authors it, entity resolution runs as part of each read-only connection
time to first cross-system answer	weeks, after Snowflake data is modelled and rules are written	minutes after the relevant systems connect
source traceability	traceable to the Snowflake tables the model was built on	citations back to the source record in the originating system, with a confidence signal
where inference runs	inside the customer's own Snowflake account as a native application	a single-tenant AWS Bedrock instance, private LLM powered by AWS Bedrock, never trained on your data
who it is for	Snowflake-native data platform teams with engineers to write the model	companies of 10 to 500 employees running 10 to 40+ systems with no dedicated data team

Who has to build the model?

In a RelationalAI deployment, an engineer writes the model directly: which entities exist, how they relate, and what rules govern optimization or inference. This assumes the underlying data is already in Snowflake in a usable shape, and it continues each time a new relationship needs representing.

In SIGNLD, nobody writes that model as a separate step. Connecting a system triggers automatic entity resolution, so an account number in billing and a customer name in a support ticket get linked without a person writing the match rule. A leader still reviews the evidence behind an answer, but the modelling labor RelationalAI assigns to an engineering team is not a project phase in SIGNLD. Read more on how this graph gets assembled at /concepts.

How long until you get a cross-system answer?

With RelationalAI, time to a first answer depends on whether the relevant data is already clean in Snowflake and how long it takes a team to write the declarative model. A team with mature Snowflake data can extend a model in days. A team starting without that foundation faces weeks of modelling work, since the graph logic has to be written before any query means anything.

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 spanning finance, support, and sales, such as which accounts are both late on invoices and opening more tickets than usual, does not wait on a modelling project, because entity resolution runs automatically as each system connects.

Where RelationalAI is the better choice

RelationalAI is genuinely the better choice for organizations that already run core analytics in Snowflake and need graph reasoning or optimization at scale, without moving data to a separate system. Problems like network optimization or fraud detection across millions of relationships benefit from compiling declarative rules into queries that run where the data sits.

Teams with data engineers who want precise, reusable graph logic embedded in their existing Snowflake governance get more from RelationalAI's native app approach than from SIGNLD's automated resolution. That precision requires real modelling time and Snowflake compute, but it produces a graph a platform team fully controls.

What running both looks like

Some organizations run RelationalAI inside Snowflake for engineering teams solving optimization problems against warehouse data, and run SIGNLD alongside it for questions that span systems the warehouse never touched. RelationalAI's model stays the system of record for the problems it was built to solve, while SIGNLD answers the question a CFO asks about a customer who is late on payment and filing tickets, without waiting for that ticket history to reach Snowflake first.

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

RelationalAI is a knowledge graph coprocessor that runs as a native app inside a customer's Snowflake account, where a team writes models and rules in a relational modelling language and runs graph algorithms next to the warehouse data.
In a RelationalAI deployment, an engineer writes the model directly: which entities exist, how they relate, and what rules govern optimization or inference.
RelationalAI is genuinely the better choice for organizations that already run core analytics in Snowflake and need graph reasoning or optimization at scale, without moving data to a separate system.
RelationalAI is priced through Snowflake Marketplace by quote, tied to Snowflake compute consumption, so a public number is not available.
If your data lives across systems that never reached a warehouse and you need a cross-system answer this week, start with the tool that builds the graph for you.

FAQ

Is RelationalAI a good alternative if I do not use Snowflake?

Not directly. RelationalAI is a native Snowflake application, so it assumes your data already lives in a Snowflake account and is modelled there. If your systems are spread across a CRM, an ERP, and spreadsheets that never reached a warehouse, SIGNLD is built to connect to those directly without requiring Snowflake at all.

Does SIGNLD need a data warehouse to work?

No. SIGNLD works without a data warehouse. It connects read-only to source systems, including spreadsheets as first-class sources, and builds the Knowledge Graph from those connections directly, which is a different starting point from RelationalAI's warehouse-native design.

Can RelationalAI and SIGNLD read the same source systems?

Sometimes, if those systems already load into Snowflake. RelationalAI operates on data once it reaches the warehouse, while SIGNLD connects directly to the originating system through one of its connectors. The overlap depends on how much of a company's data already lands in Snowflake.

What does RelationalAI cost compared to SIGNLD?

RelationalAI is priced through Snowflake Marketplace by quote, tied to Snowflake compute consumption, so a public number is not available. SIGNLD's current plans are listed on /pricing and do not require negotiating warehouse credits.

Which one is better for optimization problems like supply chain allocation?

RelationalAI, when the problem is a constraint-based optimization over data that already lives in Snowflake and an engineering team can write the model. SIGNLD gives a leader a Decision Brief with evidence and a confidence signal for a business question, but it does not replace a dedicated optimization engine.

Try SIGNLD free

If your data lives across systems that never reached a warehouse and you need a cross-system answer this week, start with the tool that builds the graph for you. Try SIGNLD free and connect a system in minutes, or Browse all articles for more on how the Knowledge Graph compares to other approaches.