DATA AND CONTEXT FOR YOUR AI SYSTEMS.

Your AI systems.
Powered by
actionable intelligence.

Data, connected knowledge and timely signals for enterprise AI. We build the context your systems need to understand what matters and support confident decisions.

Explore our offering View a sample report

A stronger foundation for the AI systems you already use.

01Custom data products02Connected knowledge03Intelligence feeds48° N / 19° E

01 / OUR OFFERING

Context your systems can act on.

We turn fragmented information into a coherent understanding of your business and the world around it. Relevant facts, meaningful relationships and timely signals, prepared for your AI workflows with evidence your teams can trace.

01 / PREVIEW

SourcesStructureData product

Custom data products

Data designed around the decisions your systems support. We bring specialised information into a consistent, documented structure, from product specifications to research findings and operational records.

Defined scope · Consistent structure · Source evidence

02 / PREVIEW

FactsRelationshipsContext

Connected knowledge

Give information its meaning. Connect facts, documents and relationships so your teams and AI systems can understand requirements, dependencies and the context behind an answer.

Shared meaning · Relationships · Traceable context

03 / PREVIEW

ObservationsChangesBriefing

Intelligence feeds

Follow the changes that matter to your business, from product availability and technical updates to market developments. Define the signals, refresh cadence and delivery around your workflow.

Relevant signals · Change history · Agreed updates

Every engagement defines coverage, source evidence, access and delivery. Start with a prepared dataset and report; ongoing feeds are scoped around agreed sources, update frequency and service requirements.

02 / CONNECTED INTELLIGENCE

Relevant data. Connected context.

A useful answer depends on both relevance and relationships. Vector retrieval finds related information, knowledge graphs preserve its connections, and RAG brings selected evidence into the model’s context. We shape these methods around the question your system needs to answer.

CONTEXT FOR YOUR AI

Architecture illustrationIRONBAY / INTELLIGENCE SYSTEMS

Vector + graph retrieval

Combine semantic similarity with explicit relationships using vector search and Neo4j knowledge graphs.

The right model for the task

Use a range of LLMs and fast inference infrastructure, selected for quality, latency, and the requirements of each workload.

Learning through feedback

Use reviewed corrections and evaluation results to improve extraction, matching, and retrieval over time.

Resilience by design

Build self-healing workflows around monitored jobs, safe retries, validation, and escalation when human judgment is needed.

03 / BUSINESS APPLICATIONS

Data for your AI. Value for your business.

Discuss your data needs

Support product and technical decisions

Connect specifications, requirements and compatibility information so teams and assistants can build an informed shortlist with supporting evidence.

Bring enterprise knowledge into reach

Make the knowledge in documents, systems and specialist sources usable for research, support and operational AI workflows.

Recognise relevant change

Understand what changed, what it relates to and why it matters, with signals selected for your products, operations or market.

Discover commercial opportunity

Connect market evidence, business relationships and operational needs to uncover prospects and partnerships that a simple list would miss.

04 / OUR METHODOLOGY

From source to usable AI context.

We begin with a decision or workflow. Together, we define the information it needs, how quality will be assessed and how the result will be used. Each deliverable is built to be understandable, traceable and useful in practice.

Define

Agree the use case, information needs and source coverage.

Standardize

Align formats, identifiers, and field definitions.

Connect

Match records and map meaningful relationships.

Validate

Review evidence and test the result against the task.

Deliver

Agree formats, access and the approach to future updates.

05 / CONNECTED KNOWLEDGE IN PRACTICE

Your next client. Closer than you think.

A packaging customer can lead you to a biotech prospect. See how relationships and similar operational needs reveal an unexpected opportunity, and the context that could guide your prospecting agent to it.

Explore the sample

IRONBAY / ACCOUNT INTELLIGENCEGLOBAL

GRAPH + VECTOR SIMILARITY

From packaging to bioprocessing.

A shared investor. A similar need. An unexpected prospect.

CustomerMeridian
Packaging

InvestorVela Industrial
Partners

OpportunityHelix
Bioprocess

Potential account	Fit · 0–1	Priority
Helix Bioprocess	0.91	P1
Solara Battery Materials	0.88	P1
Pelagic Cold Chain	0.86	P1

OUR PERSPECTIVE

As intelligence becomes abundant, context becomes decisive.

ChatGPT’s public launch in 2022 opened a new chapter for enterprise AI. Our ambition is to give these systems a deeper understanding of the environments they operate in, through relevant data, connected knowledge and a clear view of what is changing.

Company

PERSPECTIVES

Thinking beyond the dataset.

All insights

Ordered records across transparent glass layers, illustrating structured context for AI.

ENTERPRISE AI5 min read

Your AI is only as useful as the business context it can access.

Why structured, traceable business data belongs at the centre of an agentic strategy.

Read perspective

Stone and glass forms joined by a network of fine rods, illustrating relationships in business data.

CONNECTED INTELLIGENCE5 min read

Beyond similarity: why relationships matter in business data.

How vector retrieval, knowledge graphs, and RAG can work together to make research more useful.