top of page
AI & Machine Learning - Ilion Analytics.jpg

AI & MACHINE LEARNING

PRODUCTION AI FOR FINANCIAL SERVICES - NOT PROOF OF CONCEPTS

Every financial institution has an AI strategy. Very few have AI in production. The gap isn't ambition — it's execution. Models that work in a notebook stall when they hit data quality, governance, integration and compliance review.

​

We close that gap. We've built and deployed our own AI-driven platform internally, and we're in final cloud testing on AWS for our first client-facing AI agent deployment. Our approach is grounded in what it actually takes to get AI live in a regulated environment — not what looks good in a demo.

​

WHAT WE  BUILD

AI agent platforms

We design and build bespoke AI agent platforms tailored to each client's environment. Our agents handle multi-step workflows autonomously — reading documents, extracting structured data, making recommendations, and routing decisions for human review. Each platform is built around your data, your systems and your governance requirements.

Our own internal AI agent platform is in production today, and our first client deployment is in final testing on AWS.

Document intelligence

We deploy AI that reads, classifies, and extracts information from unstructured documents at scale — policies, contracts, regulatory filings, claims forms and financial reports. Our solutions use ML-enabled document content extraction and frontier LLMs to replace manual document review with automated pipelines that are faster, more consistent and auditable.

NLP and complaint analytics

We build natural language processing solutions that analyse free-text data across complaints, customer feedback and internal communications. Our NLP work for a major insurer automated complaint triage and classification — routing cases to the right team faster and surfacing systemic issues that were previously invisible.

AI IN REGULATED ENVIRONMENTS

Financial services AI isn't just a technology problem — it's a governance problem.

 

Every solution we build accounts for the reality of operating in a regulated industry:​

​

  • Explainability — Models and agent decisions that can be interrogated and explained to regulators, auditors, and boards. No black boxes.

  • Data governance — Clear lineage from source data through to model output, aligned with your existing governance framework.

  • Human-in-the-loop — AI that augments decision-making, with appropriate human oversight at every critical step.

  • Audit trails — Full logging of inputs, outputs, and decisions for regulatory review.

​

OUR AI STACK

We work with frontier AI models and cloud-native tooling, adapted to your existing environment:

​

  • Large Language Models: Azure OpenAI, AWS Bedrock, open-source models (Llama, Mistral)

  • Document Extraction: ML-enabled content extraction, OCR and classification pipelines

  • Agent Orchestration: LangChain, LangGraph, custom agent frameworks

  • Data Platforms: Azure, AWS, Databricks

  • Deployment: Containerised services on AWS, API endpoints, integrated into existing workflows

​​

​​

​

HOW THIS CONNECTS TO OUR OTHER SERVICES

 

AI doesn't work in isolation. Most AI initiatives also require solid data foundations and clear business context. Our engagements typically draw on:

​

  • Data Engineering — building the pipelines and data quality frameworks that AI depends on

  • Data Analytics — turning model outputs into dashboards, reports, and decision tools

  • Risk Analytics — applying AI to credit, market, and operational risk use cases

 

This is the advantage of working with a firm that has data engineering, analytics and risk under one roof — your AI solution doesn't stop at the model.

bottom of page