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Technology built for financial-grade workflows.

Data intelligence, quantitative analytics, AI systems and policy controls, designed together so that every output is traceable and stays within policy.

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Architecture

One core for risk and AI.

Risk intelligence and AI intelligence share the same data, AI engine and policy layer, then deliver results through one controlled output layer.

Risk intelligence

  • Exposure
  • Limits
  • Monitoring

Market analytics

  • Positioning
  • Filings
  • Macro

AI intelligence

  • Research
  • Market
  • Risk

Fellow Data core

  • Data

    Ingest and normalise approved sources

  • AI engine

    Models, rules and quantitative analytics

  • Policy

    Permissions, limits and approvals

Output layer

Alerts, evaluations and reports

Your team

People who review and decide

All three product lines share one core for data, AI and policy, and deliver results through the same controlled output layer.

Capabilities

What the platform is built from.

  • Data intelligence

    Collect, normalise and structure data from multiple approved sources, so every model and agent works from the same facts.

  • Quantitative analytics

    Turn market and trading data into measurable risk and performance indicators, such as exposure, concentration and drawdown.

  • AI systems

    Combine machine learning, large language models, rules and deterministic controls, each where it is appropriate.

  • Agent orchestration

    Coordinate specialised AI agents across complex workflows, with each agent limited to its own role.

  • Policy engine

    Define what an agent can read and analyse, and check every output against it.

  • Auditability

    Record relevant events, decisions, system actions and outputs, so they can be reviewed later.

  • API-first architecture

    Connect Fellow Data technology with existing financial infrastructure through APIs.

  • Human oversight

    Keep people in control of high-impact decisions with approval steps and clear escalation.

The intelligence layer

How information becomes intelligence.

Inside every Fellow Data agent, information passes through seven layers before anything reaches your team.

  1. Data sources

    Approved APIs, databases, market data and information sources.

  2. Perception

    Collect and normalise the relevant information.

  3. Context engine

    Combine current events with relevant context, such as positions, limits and history.

  4. Reasoning engine

    Apply AI models, deterministic rules and quantitative analysis.

  5. Policy engine

    Determine which data, tools and outputs are permitted.

  6. Output

    Generate an analysis, an alert or a report.

  7. Audit log

    Record relevant events and outputs where appropriate.

Design principle

Why AI and deterministic rules work together.

Language models are good at reading unstructured information such as news and reports. Rules and quantitative models are better at limits, calculations and anything that must behave the same way every time.

Fellow Data combines them: AI interprets, rules and models measure, and the policy engine decides what may happen next. That is how risk management stays predictable while AI agents stay useful.

Keep exploring.

See the technology on your use case.

Tell us about your data sources and workflows. We will walk you through how Fellow Data would fit.

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