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Contact Us

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We help organisations turn complex data and software challenges into dependable foundations for AI, automation and reporting. Here is what that partnership feels like in practice.

15+YEARS CREATING SUCCESS WITH CLIENTS

DATA FOR AUTOMATION

Less manual work. More momentum.

Modern data platforms need continuous attention. Without clear ownership, monitoring and optimisation, small quality, performance and cost issues gradually become operational risks.

 Ardent takes responsibility for the ongoing health of the data estate, combining strategy, managed platform operations, migration expertise and continuous improvement across cloud and data environments.

25%

Reduction in data collection costs

Automated capture replaced costly manual collection processes, making the flow of business data more efficient.

0

Routine manual inputs needed for indexing

We built a serverless process that indexes incoming data automatically, removing repetitive handling from an ongoing workflow.

OUR APPROACH

From Data to Impact

From cloud platforms and data pipelines to real-time engineering, we connect the systems and information that keep automated processes moving reliably.

01 Map and assess

Trace systems, dependencies and failure points across the current automation estate.

02 Modernise foundations

Replace fragile infrastructure and prepare scalable, governed data environments.

03 Engineer and automate

Build dependable pipelines, event flows and repeatable platform delivery.

04 Monitor and optimise

Track reliability, performance and cost, then improve continuously.

CAPABILITIES

Engineering the Foundations for Reliable Automation

From architecture and governance to retrieval and model operations, we build the connected data foundations that help AI move reliably from experimentation into production.

Cloud Data Platform Modernisation

We remove legacy constraints and create flexible cloud foundations that allow automation to scale across the organisation.

✓

Legacy cloud migration

Replace ageing infrastructure with modern cloud environments that improve scalability, resilience and access.

✓

Multi-cloud portability

Reduce platform dependency and keep future cloud options open as business needs change

✓

Lakehouse architecture

Combine flexible storage with trusted governance and performance for analytics, AI and automation.

✓

Medallion architectures

Improve data quality and usability through clear, progressively refined processing layers.

Infrastructure illustration

Data Pipeline Engineering

We build dependable pipelines that keep data moving accurately between systems, teams and automated processes.

✓

Batch + streaming pipelines

Support scheduled and real-time data movement within one dependable engineering approach

✓

ETL / ELT modernisation

Replace fragile legacy processes with scalable, transparent and easier-to-maintain data flows.

✓

Data orchestration

Keep complex workflows running in sequence, reducing delays and manual intervention.

✓

Pipeline observability

Monitor flow, freshness and failures so issues can be resolved before automation is disrupted.

Infrastructure illustration

Real-Time & Event-Driven Engineering

We help systems react as events happen, enabling faster operational, customer and machine-driven decisions.

✓

Streaming architecture

Make continuously changing data available quickly for time-sensitive decisions and automation.

✓

Event-driven pipelines

Trigger the right process automatically when important business or operational events occur.

✓

CDC (change data capture)

Keep systems synchronised efficiently by moving only the data that has changed.

✓

IoT data pipelines

Turn high-volume device and sensor data into usable information for monitoring and action.

Infrastructure illustration

DataOps & Platform Automation

We automate how data platforms are built, tested and released, improving speed, consistency and reliability. ble pipelines that keep data moving accurately between systems, teams and automated processes.

✓

Infrastructure as Code

Deploy repeatable, auditable infrastructure faster and with fewer manual configuration errors.

✓

CI/CD for data

Release pipeline and platform changes faster through automated testing and controlled deployment.

✓

Automated testing

Catch quality and pipeline issues before changes reach production and affect the business.

✓

Self-service platform provisioning

Create consistent, governed data environments quickly without repetitive manual setup.

Infrastructure illustration

Data Warehouses & Data Lakes

We create modern warehouses and data lakes that make trusted information easier to store, access and use across the business.

✓

Unified storage layers

Bring data together in one governed foundation, reducing silos and simplifying access

✓

Unified analytics layers

Give teams consistent metrics and trusted data for reporting, analytics and automation.

✓

Scalable workload management

Balance storage and compute as data volumes, users and processing demands change.

✓

Governed access and lifecycle controls

Manage permissions, retention and usage consistently across the data estate.

Infrastructure illustration

Data Warehouses & Data Lakes

We create modern warehouses and data lakes that make trusted information easier to store, access and use across the business.

✓

Unified storage layers

Bring data together in one governed foundation, reducing silos and simplifying access

✓

Unified analytics layers

Give teams consistent metrics and trusted data for reporting, analytics and automation.

✓

Scalable workload management

Balance storage and compute as data volumes, users and processing demands change.

✓

Governed access and lifecycle controls

Manage permissions, retention and usage consistently across the data estate.

Infrastructure illustration
TECHNOLOGIES

The stack behind dependable delivery.

Proven platforms, selected for the job—not forced into it.

CLOUD INFRASTRUCTURE
AWS
Azure
GCP
DATA & AI PLATFORMS
Databricks
Snowflake
DATABASES & VECTOR STORAGE
MongoDB
PostgreSQL
Redshift
Azure AI Search
Amazon OpenSearch
Pinecone
INTEGRATION, PIPELINES & TRANSFORMATION
Kafka
Airbyte
Fivetran
OBSERVABILITY & MONITORING
Monte Carlo
Datadog
Prometheus
ENGINEERING AND DEVELOPMENT
Python
GitHub
View all technologies→

TRUSTED BY TEAMS WHO DEPEND ON THEIR DATA

❝

With Ardent we were able to engage the developers as an extension of our own internal development resource.

Simon Maystre — Managing Director

FEATURED SUCCESS STORY

Monetizing broadcasting data

SUCCESS STORY

Monetizing broadcasting data

Processing 224 million broadcasting-data records every hour to improve reporting speed, reliability and data availability.

Explore all success stories →

TRUSTED BY TEAMS WHO DEPEND ON THEIR DATA

❝

With Ardent we were able to engage the developers as an extension of our own internal development resource.

Simon Maystre — Managing Director

FEATURED SUCCESS STORY

Monetizing broadcasting data

SUCCESS STORY

Monetizing broadcasting data

Processing 224 million broadcasting-data records every hour to improve reporting speed, reliability and data availability.

Explore all success stories →

TRUSTED BY TEAMS WHO DEPEND ON THEIR DATA

❝

With Ardent we were able to engage the developers as an extension of our own internal development resource.

Simon Maystre — Managing Director

FEATURED SUCCESS STORY

Monetizing broadcasting data

SUCCESS STORY

Monetizing broadcasting data

Processing 224 million broadcasting-data records every hour to improve reporting speed, reliability and data availability.

Explore all success stories →

NEWS

Latest Insights

Explore expert perspectives, practical advice and fresh thinking on data engineering, AI and technology. Drawing on real-world challenges and hands-on experience, our insights share lessons, emerging trends and ideas to help businesses make smarter technology decisions.

AI Readiness

Data Platform Modernisation: When to Retain, Modernise or Migrate

A practical framework for deciding whether to retain, modernise or migrate a data platform by identifying the true constraint, comparing total cost and risk, and changing only what the evidence justifies.

Jack Bailey

Read insight

AI Readiness

How to Data Proof Your Automation Before Scaling with AI

How to data-proof an automated workflow before scaling it with AI by tracing the evidence behind each action, testing exceptions and proving that incorrect outcomes can be stopped and recovered.

Jack Bailey

Read insight

AI Readiness

Migrating to Agentic AI; A Practical Roadmap for Data Teams Already Running ETL Pipelines

A practical roadmap for moving mature ETL operations towards agentic AI through operational contracts, shadow mode, bounded authority, visible review and a deliberate approach to cost and human accountability.

Jack Bailey

Read insight

AI Readiness

Can You Trust AI Reporting? Understanding Hallucinations in Data and Dashboards

How AI reporting can generate wrong business answers even when the underlying data is sound, and the controls needed to keep metrics, explanations, forecasts and recommendations traceable and trustworthy.

Jack Bailey

Read insight

AI Readiness

The Most Dangerous Dashboard Is the One That Looks Right

Why polished dashboards can still hide unreliable numbers, and how organisations can rebuild reporting trust through clear definitions, reconciliation, ownership, data-quality controls and traceable metrics.

Jack Bailey

Read insight

AI Readiness

How to Design a Data Checkpoint That Decides What an AI Agent Is Allowed to Run

A five-step guide to building a data checkpoint that decides whether an AI agent’s proposed query should run, be narrowed, be reviewed or be refused before it reaches a critical warehouse workload.

Jack Bailey

Read insight

AI Readiness

Data Platform Modernisation: When to Retain, Modernise or Migrate

A practical framework for deciding whether to retain, modernise or migrate a data platform by identifying the true constraint, comparing total cost and risk, and changing only what the evidence justifies.

Jack Bailey

Read insight

AI Readiness

How to Data Proof Your Automation Before Scaling with AI

How to data-proof an automated workflow before scaling it with AI by tracing the evidence behind each action, testing exceptions and proving that incorrect outcomes can be stopped and recovered.

Jack Bailey

Read insight

AI Readiness

Migrating to Agentic AI; A Practical Roadmap for Data Teams Already Running ETL Pipelines

A practical roadmap for moving mature ETL operations towards agentic AI through operational contracts, shadow mode, bounded authority, visible review and a deliberate approach to cost and human accountability.

Jack Bailey

Read insight

AI Readiness

Can You Trust AI Reporting? Understanding Hallucinations in Data and Dashboards

How AI reporting can generate wrong business answers even when the underlying data is sound, and the controls needed to keep metrics, explanations, forecasts and recommendations traceable and trustworthy.

Jack Bailey

Read insight

AI Readiness

The Most Dangerous Dashboard Is the One That Looks Right

Why polished dashboards can still hide unreliable numbers, and how organisations can rebuild reporting trust through clear definitions, reconciliation, ownership, data-quality controls and traceable metrics.

Jack Bailey

Read insight

AI Readiness

How to Design a Data Checkpoint That Decides What an AI Agent Is Allowed to Run

A five-step guide to building a data checkpoint that decides whether an AI agent’s proposed query should run, be narrowed, be reviewed or be refused before it reaches a critical warehouse workload.

Jack Bailey

Read insight

AI Readiness

Data Platform Modernisation: When to Retain, Modernise or Migrate

A practical framework for deciding whether to retain, modernise or migrate a data platform by identifying the true constraint, comparing total cost and risk, and changing only what the evidence justifies.

Jack Bailey

Read insight

AI Readiness

How to Data Proof Your Automation Before Scaling with AI

How to data-proof an automated workflow before scaling it with AI by tracing the evidence behind each action, testing exceptions and proving that incorrect outcomes can be stopped and recovered.

Jack Bailey

Read insight

AI Readiness

Migrating to Agentic AI; A Practical Roadmap for Data Teams Already Running ETL Pipelines

A practical roadmap for moving mature ETL operations towards agentic AI through operational contracts, shadow mode, bounded authority, visible review and a deliberate approach to cost and human accountability.

Jack Bailey

Read insight

AI Readiness

Can You Trust AI Reporting? Understanding Hallucinations in Data and Dashboards

How AI reporting can generate wrong business answers even when the underlying data is sound, and the controls needed to keep metrics, explanations, forecasts and recommendations traceable and trustworthy.

Jack Bailey

Read insight

AI Readiness

The Most Dangerous Dashboard Is the One That Looks Right

Why polished dashboards can still hide unreliable numbers, and how organisations can rebuild reporting trust through clear definitions, reconciliation, ownership, data-quality controls and traceable metrics.

Jack Bailey

Read insight

AI Readiness

How to Design a Data Checkpoint That Decides What an AI Agent Is Allowed to Run

A five-step guide to building a data checkpoint that decides whether an AI agent’s proposed query should run, be narrowed, be reviewed or be refused before it reaches a critical warehouse workload.

Jack Bailey

Read insight

Ready to make your data work harder?

We build and manage the data foundations that remove the quality, consistency and reliability issues holding back AI, automation and better decisions.

BOOK A CONVERSATION →