DATA AND CONTEXT FOR YOUR AI SYSTEMS.
We mine, capture and gather data across sources and formats, turning it into structured context for your AI agents, applications and teams.
A stronger foundation for the AI systems you already use.
01 / DATA FOR YOUR SYSTEMS
Your use case defines the data. From web content and documents to APIs, datasets and operational signals, we mine, capture and gather the information you need, then structure it with clear definitions and documented sources your team can inspect.
Give research and prospecting workflows a consistent view of companies: identities, activities, websites and relevant business contact channels.
Add geographic context to market analysis with commercial locations, regional information and property data within an agreed scope.
Bring hiring signals into your research and AI workflows. Employers, advertised roles and recruitment activity, organized for analysis.
These are examples of what we deliver. We scope data across other domains and formats around your use case, with coverage, field availability, source dates and limitations documented for each engagement.
02 / CONNECTED INTELLIGENCE
Vectors match meaning. Graphs connect entities. Retrieval-augmented generation (RAG) brings relevant evidence into your AI system’s context. We prepare the business information these approaches need to work together.
Combine semantic similarity with explicit relationships using vector search and Neo4j knowledge graphs.
Use a range of LLMs and fast inference infrastructure, selected for quality, latency, and the requirements of each workload.
Use reviewed corrections and evaluation results to improve extraction, matching, and retrieval over time.
Build self-healing workflows around monitored jobs, safe retries, validation, and escalation when human judgment is needed.
Give research, sales and operations agents structured business context with documented fields, sources and access boundaries.
Help your teams and AI tools identify relevant prospects through company relationships, operational needs and agreed criteria.
Connect information on suppliers, competitors and partners for research assistants and commercial analysis.
Understand a segment through readable reports and structured records your teams and applications can use.
04 / OUR METHODOLOGY
We start with what your system needs to understand. Together, we define the question, coverage and data structure, then prepare information your AI can use and your team can review.
Define the market and select relevant sources.
Align formats, identifiers, and field definitions.
Match records and map meaningful relationships.
Check consistency and document limitations.
Provide reports and structured data in agreed formats.
05 / THE DELIVERABLE
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 sampleGRAPH + VECTOR SIMILARITY
A shared investor. A similar need. An unexpected prospect.
| 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
ChatGPT’s public launch in 2022 opened a new chapter for enterprise AI. We focus on what your systems need beyond the model: structured, connected business data that supports useful answers and informed action.
CompanyPERSPECTIVES
01ENTERPRISE AI5 min read
Why structured, traceable business data belongs at the centre of an agentic strategy.
Read perspective
02CONNECTED INTELLIGENCE5 min read
How vector retrieval, knowledge graphs, and RAG can work together to make research more useful.
Read perspectiveLET’S CONNECT
Tell us what your AI system or team needs to understand. We’ll agree the data, coverage, structure and delivery around your use case.