Question 1

What is Dataplane?

Accepted Answer

Dataplane is an agentic AI platform for enterprise data. It builds a semantic model of your business — entities, relationships, and rules — and uses that model to catch data errors, attribute them to the rows at fault, and heal them in place.

Question 2

What is a data ontology, and why does Dataplane start there?

Accepted Answer

An ontology is a governed model of your business concepts and how they relate. Most companies keep that model in people's heads rather than in the data. Dataplane makes it explicit so that quality rules, master records, and AI agents all reason on the same definitions.

Question 3

How is Dataplane different from a data quality tool?

Accepted Answer

Most data quality tools report scores: null rates, duplicate counts, schema compliance. Dataplane attributes each error to the exact rows at fault and heals them in place. The output is corrected, auditable data rather than a dashboard.

Question 4

Does Dataplane replace my warehouse or ETL?

Accepted Answer

No. Dataplane sits alongside them. It reads from the systems you already run, applies the ontology and its rules, and writes trusted data back out to your warehouse and downstream consumers.

Question 5

What does 'data healing' actually mean?

Accepted Answer

Dataplane does not just flag an error. You describe how a problem should be resolved in plain language, and the correction is applied in place — documented, repeatable, and auditable.

Question 6

How does Dataplane handle duplicate records?

Accepted Answer

Dataplane finds duplicate products, suppliers, and locations across systems and proposes one canonical record. A domain owner confirms it, so the golden record is created rather than assumed.

Question 7

Can Dataplane fill in missing fields?

Accepted Answer

Yes. Dataplane extracts attributes from unstructured text and derives the values downstream systems require, conformed to your taxonomy.

Question 8

Can I see where a field came from?

Accepted Answer

Yes. Lineage traces every field from raw source through to dashboard, model, and agent, so upstream drift does not break you silently.

Question 9

Do I need to write code to use it?

Accepted Answer

No. Rules and corrections are described in plain language. Engineering governs and reviews them, but does not have to implement every change by hand.

Question 10

How does Dataplane improve AI agent accuracy?

Accepted Answer

Your business has an ontology and the LLM assumes a generic one. Where they disagree you get hallucinations and wrong answers. Dataplane exposes your governed ontology to the agent so it reasons on your model instead of guessing.

Question 11

Is this the same as RAG?

Accepted Answer

RAG retrieves text passages. Dataplane grounds agents in a structured model of your business, so the agent queries entities and relationships rather than hoping the right document chunk was retrieved. The two work together.

Question 12

What is an agent harness?

Accepted Answer

A harness wraps the model in permission checks, validation gates, and logging, so rules run as code instead of expensive generation. It makes agent behavior visible and lets outcomes feed back into the system.

Question 13

Who approves changes?

Accepted Answer

Domain owners confirm canonical records and healing rules. Changes are versioned and attributed, so self-serve speed does not cost you control.

Question 14

Is there an audit trail?

Accepted Answer

Yes. Every correction records the input, the rule applied, the rows affected, the result, and who approved it.

Question 15

How is my data protected?

Accepted Answer

Data is encrypted in transit and at rest, and access is scoped by role. Contact us for details on deployment options and our current compliance posture.

Question 16

How much does Dataplane cost?

Accepted Answer

Pricing depends on data volume and the number of connected systems. See the pricing page, or book a demo and we will scope it against your actual workload.

Question 17

How long does it take to see value?

Accepted Answer

We typically model one slice of your domain first and run it against your real data, so you can measure the lift before committing to a broader rollout.

Question 18

How do I try it?

Accepted Answer

Book a demo. We will model a slice of your domain and show results on your own data rather than a canned dataset.

Frequently Asked Questions