SIGNLD vs Stardog: enterprise graph tooling vs an answer layer

Stardog gives a team of knowledge engineers the tools to build a formal RDF and OWL graph, query it in SPARQL, and reason over ontologies they author by hand. SIGNLD skips the authoring step: it builds the graph from your connected systems automatically and returns a ranked, traceable answer to a plain-language question.

TL;DR

Stardog is an enterprise knowledge graph platform built on RDF, OWL, and SPARQL, with virtual graphs that query source systems without copying them and reasoning over ontologies your team writes. SIGNLD is a decision intelligence platform that connects to 800+ business systems read-only, builds the graph itself, and returns a Decision Brief with source citations and a confidence signal. Choose Stardog if you have knowledge engineers and a standards requirement. Choose SIGNLD if you need a cross-system answer this week and nobody on staff writes ontologies.

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

What is Stardog actually built for?

Stardog is a graph database and reasoning engine built for teams who want formal control over how entities and relationships are defined. It stores data as RDF triples, lets you write ontologies in OWL, and query everything in SPARQL, the W3C standard graph query language. A distinguishing feature is virtual graphs: Stardog can query relational sources live, mapping them into the graph on the fly, instead of copying every record into triples first.

Stardog Voicebox adds a natural-language layer on top, letting a user ask a question that gets translated into a SPARQL query against the modelled graph. That translation only works as well as the ontology underneath it. If the ontology is missing a relationship, or two teams named the same entity differently, the natural-language layer inherits that gap.

Stardog publishes a free developer tier and prices enterprise deployments by quote, which typically involves a sales conversation about data volume, number of named graphs, and support tier. The buyer is usually a data architecture or knowledge engineering team, not a business leader asking a question directly.

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 ERP and CRM down to the spreadsheets an operations team maintains, and resolves the same customer, job, or invoice across all of them inside a private Knowledge Graph. Nobody writes an ontology first.

A leader asks a question in plain language and gets a Decision Brief: the finding, the evidence with links back to source records, a confidence score, and a recommended next step. Inference runs on a single-tenant AWS Bedrock instance. You can connect the first system in 15 minutes and get the first answer in minutes, without a schema design phase.

SIGNLD's target user is a CEO, CFO, or COO at a company running 10 to 40 or more siloed systems with no dedicated data team, or a team that already has more requests than time.

Where the two genuinely differ

The core difference is who defines the model and when. Stardog requires a knowledge engineer to design classes, properties, and relationships in OWL before a query returns anything meaningful. That model is an asset: once built, it can enforce consistency across every downstream query and support rigorous reasoning, such as inferring that a subsidiary belongs to a parent company through a chain of stated rules.

SIGNLD resolves entities automatically as it ingests each connected system, matching records that refer to the same real-world thing without a human writing the matching rule first. This trades formal rigor for speed. Stardog's ontology can encode a compliance rule that must always hold; SIGNLD's graph surfaces a probable connection and shows its evidence so a person can confirm it.

A second difference is output shape. A SPARQL query against Stardog returns rows of triples, which a downstream application or Voicebox then has to render into something a business user can read. SIGNLD's output is already a Decision Brief: a written finding, ranked evidence, and a confidence signal, with no rendering layer required.

A few structural differences worth naming directly:

Stardog stores and reasons over triples you or your team define; SIGNLD builds the graph from connected systems without a modelling step.
Stardog uses SPARQL as its query language; SIGNLD accepts a plain-language question and returns a Decision Brief.
Stardog's virtual graphs query sources live without copying; SIGNLD also connects read-only and never requires a warehouse.
Stardog targets knowledge engineers and data architects; SIGNLD targets the CFO or COO who has the question.
Stardog's reasoning is formal and rule-based; SIGNLD's confidence signal is probabilistic and tied to source evidence.

	Stardog	SIGNLD
what it models	an RDF and OWL ontology authored by knowledge engineers, queried with SPARQL	business entities, metrics, and their relationships resolved automatically as systems connect
who builds it	a knowledge engineer or data architect writing the ontology, mappings, and virtual graphs	no one authors it, entity resolution runs as part of each read-only connection
time to first cross-system answer	weeks to months, after the ontology and source mappings are modelled	minutes after the relevant systems connect
source traceability	provenance through virtual graph mappings back to the federated source	citations back to the source record in the originating system, with a confidence signal
where inference runs	OWL reasoning in the Stardog engine, with Voicebox as the language surface	a single-tenant AWS Bedrock instance, private LLM powered by AWS Bedrock, never trained on your data
who it is for	enterprises with knowledge engineers and a formal semantics requirement	companies of 10 to 500 employees running 10 to 40+ systems with no dedicated data team

Who has to build the model?

In a Stardog deployment, a knowledge engineer or ontologist writes the schema: classes like Customer or Invoice, properties connecting them, and rules such as "a subsidiary inherits the risk rating of its parent." This work happens before the first useful query and continues as new data sources are added, since each source needs a mapping into the shared ontology.

In SIGNLD, nobody authors that schema. Connecting a system triggers automatic entity resolution: the same customer ID in the CRM and the same account number in the accounting system get linked without a person writing the join. A leader still reviews the evidence behind an answer, but the model-building labor that Stardog assigns to a specialist team does not exist as a separate project step in SIGNLD.

How long until you get a cross-system answer?

With Stardog, time to a first cross-system answer depends almost entirely on ontology maturity. A team with an existing, well-maintained ontology can add a new virtual graph source and query across it in days. A team starting from nothing is looking at a multi-month modelling project before Voicebox or SPARQL returns anything a business user would trust, because the ontology has to represent the relationships between systems before a query can traverse them.

With SIGNLD, the first system connects in 15 minutes and the first answer follows in minutes, because entity resolution happens automatically as data streams in. A question spanning three or four systems, such as why regional margin moved last month, does not wait on a separate mapping project for each new source.

Where Stardog is the better choice

Stardog is genuinely better for organizations that need formal ontology modelling, standards compliance, and rule-based inference at scale. If your industry requires RDF and OWL for interoperability, or your team already maintains a mature ontology that encodes regulatory logic, Stardog's reasoning engine does something SIGNLD does not attempt. Virtual graph federation across many relational sources, without copying data into a new store, is also a genuine strength when a data architecture team wants that control.

Teams with in-house knowledge engineers who want to encode precise, auditable inference rules, such as "flag any transaction where the counterparty's ultimate parent is on a restricted list," get more from Stardog's SPARQL and OWL foundation than from SIGNLD's automated resolution. That precision has a cost: it takes specialist staff and sustained maintenance to keep the ontology current as source systems change.

What running both looks like

Some organizations run Stardog for governed, ontology-backed reasoning used inside a product or a compliance process, and SIGNLD for the leadership team's day-to-day cross-system questions. Stardog's graph stays the system of record for formally modelled relationships; SIGNLD answers the question a CFO asks on a Tuesday without waiting for that model to be extended.

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 and the list of supported connectors for specifics, and /concepts for how the Knowledge Graph and Topic model fit together.

Key takeaways

Stardog is an enterprise knowledge graph platform built on RDF, OWL, and SPARQL, with virtual graphs that query source systems without copying them and reasoning over ontologies your team writes.
SIGNLD is a decision intelligence platform from Inzata Analytics.
Stardog is genuinely better for organizations that need formal ontology modelling, standards compliance, and rule-based inference at scale.
Stardog offers a free developer tier and prices enterprise deployments by quote based on data volume and support tier, so a public number is not available.
If your team does not have a knowledge engineer and still needs a cross-system answer this week, start with the tool that builds the graph for you.

FAQ

Is Stardog a good alternative if I do not have a data team?

Not on its own. Stardog's value depends on someone writing and maintaining an OWL ontology, which is specialist work. Without a knowledge engineer, the graph stays empty of the reasoning it is built to provide. SIGNLD is built for exactly this gap, since it resolves entities automatically without requiring anyone to author a schema first.

Does SIGNLD use SPARQL or RDF?

No. SIGNLD builds its Knowledge Graph automatically from connected systems and exposes it through plain-language questions and Decision Briefs, not a query language. There is no schema for a user to write or maintain.

Can Stardog and SIGNLD read the same source systems?

Often yes. Stardog's virtual graphs query relational and warehouse sources live, and SIGNLD connects read-only to over 800 business systems including spreadsheets. The systems can overlap; what differs is whether a person has to model the relationships first.

What does Stardog cost compared to SIGNLD?

Stardog offers a free developer tier and prices enterprise deployments by quote based on data volume and support tier, so a public number is not available. SIGNLD's current plans are listed on /pricing and never require negotiating graph volume.

Which one is better for compliance reasoning?

Stardog, when the requirement is formal and auditable, such as inferring regulatory status through a documented rule chain in OWL. SIGNLD gives you a confidence signal and source evidence for a business decision, but it does not replace a rule engine built for regulatory proof.

Try SIGNLD free

If your team does not have a knowledge engineer and still needs 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.