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Platform Overview

A platform for bigger questions.

Connected data. Deep analysis. Applications and AI. One platform.

DataWalk is a data and analytics platform built around an enterprise knowledge graph. Connect business data, follow relationships, and reuse the analysis in applications and AI.

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An iceberg split by the waterline: a small tip labelled 'what you work with' sits above a much larger mass below, 'what the platform handles', holding the connected-data graph, security, governance and workflow.
The Challenge

Don't let the stack decide the question.

A relationship question can become a data project before anyone can investigate it. The answer might require another source, another identity match, another join, or a new application to put it to work.

The question should shape the analysis, but the technology usually sets the limits. Don't let it.

Less rework. More answers.

  1. Bring sources together with shared business definitions.

  2. Connect records belonging to the same entity.

  3. Reuse relationships across investigations.

  4. Run graph, relational, and search analytics together.

  5. Ask the next question without starting over.

The Platform

The platform you'd have to build.
Already built.

Building the components is one thing. Keeping them working together as your data, questions and applications change is another. DataWalk supplies the connected analytical foundation. Your team defines the business model, maps sources and configures the work that matters.

Read our position on "Build v Buy"

Technology fit check What data technology fits your problem? Answer four questions about your data and requirements. See which technologies are worth evaluating.
Question 1 of 4What are you trying to understand?
Question 2 of 4What's making the work difficult?
Question 3 of 4How will the results be used?
Question 4 of 4What would AI need to know to answer questions about your business?
The Solution
01 · Connected data

Meaning first.

Define the people, organizations, events and relationships that matter to your business. Map source data into that model. Resolve records that belong to the same entity, with evidence behind the match. A relationship can carry its own properties, such as an ownership stake and date.

  • Business ontology
  • Semantic ETL
  • Entity resolution
  • Evidence
02 · Connected analytics

Change the analysis. Keep the engine.

Search for a company, follow its ownership links, and narrow the results within the same analysis. Users move naturally from one question to the next, without switching tools or navigating separate systems. Search, graph and relational analysis work directly within the DataWalk database, removing the complexity of integrating and maintaining separate engines.

Less integration. Less duplication. Less complexity.

  • Search
  • Graph
  • Relational
  • One physical state
03 · Reusable work

Turn an analysis into something you can reuse.

Save the steps behind an analysis so others can rerun and extend it. Configure rules, scores and alerts for recurring work. Build operational applications from connected datasets, analyses and views already in place. Your team defines the logic and its refresh processes.

  • Saved analyses
  • Rules and scoring
  • Alerts
  • Applications and APIs
04 · AI & Context

Give AI the same business context as your analysts.

Connect AI assistants and agents to the same enterprise knowledge graph and ontology your analysts use. Agents can run the same analyses and get repeatable analytical results. DataWalk permissions govern which tools and data they can access.

Explore AI & Context

In Practice

Stop starting over.

One investigation can establish useful work for the next. The underlying relationships, analytical steps and configured definitions remain available for further questions.

  1. 01 · Start

    Start with a search or a list of accounts.

    Use the resulting set to follow owners and counterparties.

  2. 02 · Connect

    Follow owners and counterparties.

    Explore indirect connections across the graph.

  3. 03 · Refine

    Apply conditions to select a network.

    Filter and calculate to identify what warrants review.

  4. 04 · Reuse

    Save the analytical steps.

    Give the next review a foundation to extend.

Evaluator Questions

What you need to know.

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Why is DataWalk different?

DataWalk gives you search, relational, and graph analysis on the same connected data, with a much smaller footprint than you'd expect with this much analytical power. There are no separate systems to keep in sync and no integration layer to rebuild every time a source changes. The platform is simple enough for your own team to run and keep its source connections current, using the expertise you already have rather than specialist engineers.

And it isn't a tool for one use case. Once your data is connected, the next team doesn't start over. Whether it's fraud, compliance, investigations, or something no one has thought of yet, they build on the same data, models, and connections already in place. Each new use makes the platform more valuable, not more expensive to run.

Does DataWalk replace our warehouse or lakehouse?
No. Existing systems can remain data sources. DataWalk ingests the selected data needed for connected analysis and maintains its own analytical representation. Scope the sources and refresh requirements for your workload.
What does the platform provide, and what do we configure?
DataWalk provides data modeling, entity resolution and analytical capabilities, alongside native business-user and programming interfaces. Your team defines the business logic, data mappings, access controls and analyses, with matching rules configured to your requirements. Custom interfaces or workflows may require additional development.
Do saved analyses stay up to date as data changes?
Yes. Saved analyses preserve their steps and can run against updated data. Ingestion, derived calculations and alerts follow the processes and rules you configure, keeping results current without having to rebuild the analysis.
Do you run a separate search engine?
No. The search index lives inside the DataWalk database, so there's no separate search service to run or keep in sync. Search for a company, follow its ownership links, and narrow the results in the same analysis.
Does the knowledge graph have to fit in memory?
No. Persistent storage holds the graph, and RAM supports active queries. Worker-local disks keep frequently used data close to where queries run. Size RAM, worker-local disks, and persistent storage separately for your workload. See Architecture & Deployment for deployment details.
What can an AI agent access?
Enabled analytical tools and data permitted to the connected user. The access controls do not guarantee an agent's interpretation of the resulting information.

Bring the question you stopped asking.

Show us the data, the connections you need to follow, and where the analysis gets difficult. See how DataWalk would approach it.