FAQ & contact
Start with your use case.
Every implementation begins with the problem you need to solve, the information you already hold, the decisions you need to support and the level of security your organisation requires.
placeholderWhat we can explore
Speak to us about a custom implementation
A first conversation usually covers:
- What data you already have
- What intelligence you need to produce
- Which entities and relationships matter
- What risks you need to monitor
- What level of security is required
- Whether the system should be hosted, private, hybrid or local
- What a first implementation could look like
Get in touch
Tell us about the problem
We will follow up to arrange a discovery call, workshop or NDA-led discussion. Nothing you share commits you to anything.
FAQ
Plain-English answers
Some questions are flagged as reviewed for claim discipline — we are deliberately careful about what we state publicly.
About the offer
Is Causality.tools a SaaS product?
Not in the standard one-size-fits-all sense. Causality.tools is a custom-implemented intelligence system. Each client implementation is shaped around its data, security needs, workflows and sector-specific requirements.
How is pricing calculated?
Pricing depends on implementation scope. Factors may include data volume, number of users, integrations, deployment model, security requirements, reporting needs, live monitoring requirements and whether models need to run locally on client-owned infrastructure.
The best starting point is a discovery call or workshop.
Can we start with one capability first?
Yes. Some organisations begin with a focused implementation — around Graph RAG, entity intelligence, risk reporting, a knowledge graph or a discovery workshop — before expanding into a wider system.
How it works
What is a causal knowledge graph?
A causal knowledge graph connects entities, events, evidence and risks in a way that helps users understand not only what is connected, but how one factor may relate to another.
In Causality.tools, relationships are built up from your data by decomposing, categorising and mapping information into connected structures.
What is Graph RAG?
Graph RAG is a way of improving AI answers by retrieving evidence from connected knowledge rather than relying only on isolated text search or a general-purpose AI model. It helps produce answers that are more grounded in the organisation's own information.
Is this just a chatbot?
No. A chatbot may be one interface, but Causality.tools is better understood as an intelligence layer. It connects data, documents, entities, relationships, geography, memory and workflows.
Can outputs link back to evidence?
Yes. Causality.tools is built around evidence-linked intelligence. Outputs can be designed to show supporting sources, entities, confidence scores and items requiring human review.
Will the system make decisions for us?
No. Causality.tools is designed to support expert decision-making. It can monitor, retrieve, map, summarise and escalate — but human judgement remains central.
What data sources can it use?
Potential sources may include documents, databases, APIs, spreadsheets, web sources, news streams and internal systems. Exact integrations are scoped during discovery.
Does the system use probability?reviewed
The system can assign confidence scores. We describe the platform in terms of reasoned, evidence-linked relationships and confidence — and we are careful with public wording around probability, which is reviewed by our technical team before use.
What does “real-time” mean?reviewed
In this context, it refers to live or frequently updated information streams, such as incoming news or web data. The exact update model is confirmed during scoping rather than stated as a fixed latency.
Security and deployment
Can it work with sensitive data?
Yes. The implementation can be shaped around your security needs. Options may include hosted, private, client-controlled, hybrid or local deployment models.
Can the AI models run locally?
Yes. For high-security use cases, LLMs can run on dedicated local infrastructure owned by the client, subject to scoping, hardware requirements and pricing.
Do you support confidential compute?reviewed
Causality.tools can be designed with confidential compute and trusted execution principles where appropriate. The specific technologies are confirmed during technical discovery rather than named here.
Getting started
How do we start?
Most clients begin with a discovery call or workshop. For sensitive projects, initial discussions can take place under NDA.