Beneath AnalyticsAI Consulting & Development
Applied AI
Applied AI puts machine learning and language models into the workflows where they create measurable value: recommendations, classification, document processing, and anomaly detection. Every engagement starts with a business problem, is trained on your data, and ships with monitoring so you know when model performance starts to drift.
Technology we use
- Automation & agentic AI
- ChatGPTClaudeGemini
What this includes.
We apply AI where it creates clear, quantifiable value: not as a capability showcase. Every AI engagement starts with a business problem and ends with a measurable result. GDPR-native delivery, EU data residency as standard.
Recommendation engines
Custom recommendation models trained on your product catalogue and customer behaviour. Higher relevance than off-the-shelf solutions, and owned by you.
Automated classification
ML models that classify, label, and route data at scale: support tickets, transactions, documents, and customer enquiries: without manual review.
LLM-powered workflows
Practical applications of large language models: document processing, structured data extraction, automated drafting, and intelligent search within your data.
Anomaly detection
AI-based monitoring that identifies unusual patterns in transactions, user behaviour, or operational data before they become incidents.
What changes when it lands.
Problem first, AI only if it fits
Approach
Measured on your data before launch
Validation
Monitoring for model drift
Operations
Common questions.
We define the input, the desired output, and how success is measured. Often a rule-based system or a simple statistical model performs better and is far easier to maintain, and we tell you when that is the case.
No. We build the model, integrate it into your workflow, and document it. If you have an internal team, we upskill them so the capability stays with you after handover.
Monitoring is part of the build. We track performance against your specific use case and set up alerts, so drift is caught by instrumentation rather than by a customer complaint.
Yes. EU data residency, private cloud deployment, and redaction of personal data before model calls are all standard options, and GDPR-compliant delivery is the baseline rather than an upgrade.
Related solutions.
- AI Consulting & Development
Data Science
Data science answers the questions your dashboards cannot: who will churn, what demand will be, which segments matter. Beneath Analytics builds churn models, forecasts, and segmentation on your own data, integrates them into the tools your team already uses, and validates them with properly designed experiments.
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AI Workflow Automation
AI workflow automation removes the manual work between your systems: compliance reports, risk summaries, inventory updates, and weekly reporting compiled by hand. Beneath Analytics maps the process, builds pipelines with n8n, Python, or dbt, then monitors them, so reports arrive on time without someone spending days assembling them.
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