The data layer that runs itself. Schema-drift caught before it breaks production. Pipelines that self-heal. Quality monitoring as a continuous agent — not a Monday-morning fire-drill.
A schema changes upstream. A downstream consumer breaks. An engineer traces lineage manually, patches the job, and redeploys. Four hours gone. Repeat next week.
An agent monitoring the schema contract caught the drift. Quarantined the anomalous rows, opened a remediation PR, posted to the on-call channel with proposed fixes — before a single dashboard broke.
Not generative dashboards. Not chat-with-your-data. Production-grade agents with defined contracts, observable behaviour, and a clear hand-off back to your team when they hit their edge.
Agents watch contract boundaries between systems, detect upstream schema changes the moment they land, and either auto-remediate or open a structured remediation task before downstream breaks.
A new ingestion request expressed in plain English ("pull yesterday's invoices from SAP, dedupe against DATEV, push to BI") becomes a typed, tested, monitored pipeline — generated and reviewed, not hand-coded from scratch.
Continuous anomaly detection on every row, not a nightly check. Bad rows are quarantined with provenance preserved — the agent doesn't decide for you, it surfaces the decision with evidence.
Agents that ingest, chunk, embed and re-embed your knowledge corpora as the source documents change. Drift in source content propagates to the vector layer without a quarterly re-build sprint.
Data contracts as executable agreements, not Confluence pages. Producer changes are validated against consumer needs before the merge — not after the dashboard dies.
Every agent has a clear edge. When it hits ambiguity it pauses, escalates with context, and waits. Your data team owns the decisions; the agent owns the toil.
Custom AI agent ingests press, social, blogs and dark-web sources at scale, detects relevance against client briefs, and routes alerts. Built the concept, brand, product UI and Web + Mobile end-to-end.
Agent auto-aggregates sales from brand websites via AI, normalises against a credit-card-rewards catalog, and surfaces personalised offers. White-label deploy in 14 days.
Brand, web, product UI and pitch deck for AI agents that turn operational data into auditable decisions. One year of work, €3.5M seed round closed at the end.
Platform giving property owners exact info on energy consumption, CO₂ emissions and climate risk — agents continuously ingest and normalise energy data across heterogeneous building stocks.
Brand, website and product design for an AI-powered portal that turns donor data into actionable fundraising intelligence. Agents handle ingestion, scoring and outreach prioritisation.
Industrial AI agent populated from product documentation, engineering data and support history — the agentic layer that keeps installer-facing answers grounded as the underlying docs evolve.
You own a data layer that has grown over years — multiple sources, partial documentation, fragile contracts. You don't need another dashboard; you need the layer to run itself between Mondays.
Pipeline breaks every week. Schema drift caught by complaints, not by monitors. RAG corpora go stale and nobody knows. Quality work eats 40% of senior data-engineering time. Time to stop staffing the toil and start automating it.
Provider-agnostic and stack-agnostic. We build on what you already have — dbt models, Great Expectations contracts, OpenLineage traces — and add the agentic layer where it makes the biggest dent.
We start with a Discovery Sprint to find the highest-leverage pattern in your pipeline (not a generic playbook). Then NORD CYCLE™ Build phase: production-grade agent in 90 days, your team trained to own it.
A Discovery Workshop with your data team — half-day to two days, on-site, joint use-case scoping. Daniel runs the first conversation personally. No deck.