Beneath AnalyticsAI Consulting & Development
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.
Technology we use
- Automation & agentic AI
- n8n
- Data & warehousing
- BigQuerydbt
What this includes.
We build workflow automation and data pipelines that eliminate manual repetitive work: freeing your team to focus on decisions, not reporting. GDPR-native delivery, EU data residency as standard.
Reporting automation
Scheduled pipelines that pull, transform, and deliver reports without a person in the loop. Compliance reports, weekly summaries, board packs: automated end to end.
System integration
Connect your CRM, ERP, payment systems, and data warehouse. Data flows between systems automatically, eliminating manual export-import workflows.
Alert and monitoring workflows
Automated alerts when key metrics cross thresholds. Your team gets notified when something needs attention, not when it is already a problem.
AI steps inside workflows
Language-model steps where they save real time: sorting incoming requests, summarising documents, drafting first replies. Built on Claude, ChatGPT or Gemini, with a person approving anything that matters.
Data pipeline automation
ETL pipelines built with n8n, Python, or dbt that move data from source systems to your warehouse on a schedule or in real time.
What changes when it lands.
67% reduction, 3 days to 4 hours
Reporting time
Zero in 18 months
Compliance incidents
40% of time redirected
Team capacity
Common questions.
The one that costs the most person-hours every week and has clear, stable rules. We map the steps, data sources, and edge cases first, so the scope is understood before any build starts.
No. Automation connects what you already have through APIs. Buying a new platform usually creates another system to integrate rather than solving the integration problem underneath it.
Only where a fixed rule cannot do the job: reading free text, summarising a document, drafting a first reply. Everything else stays as plain, predictable automation, and a person approves any step with consequences.
Monitoring and error alerting are part of the build, with documentation that gives your team the playbook to fix it. You find out from an alert, not from a stakeholder.
The first report or workflow typically takes four to eight weeks, then one to two weeks for each additional one that reuses the same data sources.
Related solutions.
- Tracking & Analytics
Analytics & Business Intelligence
Analytics and business intelligence means Beneath Analytics turns your tracked data into decisions: attribution modelling, predictive models, and dashboards built around the questions your team actually asks and adopted by the people who use them.
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AI-Ready Data Platforms
AI-ready data platforms means Beneath Analytics builds the warehouses, pipelines, and governance that AI and analytics depend on: BigQuery and Snowflake warehouses with dbt transformation, data quality checks, and documented lineage you can defend. Pipelines scale with your data volume, so models and dashboards run on data your team trusts.
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