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Services

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Platforms

Success Stories

Insights

Contact Us

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We engineer pipelines built for scale and reliability — building the observability and engineering discipline to catch data drift early, before it reaches customers, while keeping data volumes, pipeline failures, and data quality in check.

15+YEARS CREATING SUCCESS WITH CLIENTS

DATA COMPANIES

Deliver more without adding complexity

Deliver more without adding complexity

For data companies, data quality is not only an internal concern; it is part of the product delivered to customers. Pipeline failures, drift and inconsistent outputs can directly affect client trust, service levels and commercial performance.

Ardent engineers and operates scalable data platforms with the observability, testing and operational discipline needed to identify issues early and keep customer-facing data products dependable.

10 TB

Data brought together in one usable lake

We unified different data streams in one environment, helping analysts work with large datasets instead of disconnected sources.

42%

Reduction in monitoring and reporting costs

We brought fragmented monitoring and reporting into one automated process, improving efficiency while lowering operating costs.

OUR APPROACH

From Data to Impact

A structured approach to help you move from data readiness to real business value, with governance and operational excellence built in.

01  Baseline the service

Map customer commitments, pipeline dependencies and current reliability risks.

02   Engineer the controls

Build testing, observability, lineage and scalable delivery into the platform.

03  Operate to service levels

Monitor customer-critical pipelines and respond through accountable processes.

04  Optimise for growth

Improve performance and cost as products, customers and volumes expand.

CAPABILITIES

Engineering Reliable Data Products at Scale

We strengthen the pipelines, quality controls and platform operations behind customer-facing data products, helping them remain dependable as volumes and complexity grow.

Data for AI

Turn high-value data products into reliable foundations for AI-driven features, enrichment and intelligent search that create greater customer value.

✓

Enterprise AI and ML data preparation

Improve the quality and readiness of data behind customer-facing intelligent features.

✓

Vector retrieval for data products

Enable faster semantic search, discovery and enrichment across large information estates.

✓

MLOps and LLMOps pipelines

Deploy, evaluate and improve AI-driven product features through controlled production processes.

✓

AI feature evaluation and monitoring

Measure quality, drift and reliability before customer trust is affected.

Data for AI

Turn high-value data products into reliable foundations for AI-driven features, enrichment and intelligent search that create greater customer value.

✓

Enterprise AI and ML data preparation

Improve the quality and readiness of data behind customer-facing intelligent features.

✓

Vector retrieval for data products

Enable faster semantic search, discovery and enrichment across large information estates.

✓

MLOps and LLMOps pipelines

Deploy, evaluate and improve AI-driven product features through controlled production processes.

✓

AI feature evaluation and monitoring

Measure quality, drift and reliability before customer trust is affected.

Data for Automation

Engineer scalable, tested pipelines that handle growing data volumes while preserving the reliability customers and service levels depend on.

✓

High-throughput streaming pipelines

rocess growing event volumes in real time without weakening reliability.

✓

Infrastructure as Code and CI/CD

Make pipeline delivery repeatable, testable and easier to scale.

✓

Automated pipeline testing

Detect defects before they reach customer-facing products or service levels.

✓

Scalable platform provisioning

Create consistent environments quickly as products, customers and data volumes grow.

Data for Automation

Engineer scalable, tested pipelines that handle growing data volumes while preserving the reliability customers and service levels depend on.

✓

High-throughput streaming pipelines

rocess growing event volumes in real time without weakening reliability.

✓

Infrastructure as Code and CI/CD

Make pipeline delivery repeatable, testable and easier to scale.

✓

Automated pipeline testing

Detect defects before they reach customer-facing products or service levels.

✓

Scalable platform provisioning

Create consistent environments quickly as products, customers and data volumes grow.

Data for Reporting

Make pipeline health, data quality and customer-facing performance visible, allowing teams to detect risk earlier and protect service quality.

✓

Unified operational reporting

bring product, platform and customer-service performance into one trusted view.

✓

Pipeline observability and SLA monitoring

dentify service risk early and demonstrate performance against customer commitments.

✓

Automated data-drift detection

catch changing patterns before they reduce product quality or customer trust.

✓

End-to-end data lineage

trace customer-facing information through every source, pipeline and transformation.

Data for Reporting

Make pipeline health, data quality and customer-facing performance visible, allowing teams to detect risk earlier and protect service quality.

✓

Unified operational reporting

bring product, platform and customer-service performance into one trusted view.

✓

Pipeline observability and SLA monitoring

dentify service risk early and demonstrate performance against customer commitments.

✓

Automated data-drift detection

catch changing patterns before they reduce product quality or customer trust.

✓

End-to-end data lineage

trace customer-facing information through every source, pipeline and transformation.

Managed Data Ops

Operate data platforms around the clock with clear service levels, faster incident response and continuous performance and cost improvement.

✓

24×7 managed pipeline operations

maintain availability and respond quickly when customer-critical data services are at risk.

✓

Continuous performance and cost optimisation

protect margins while data volumes and customer demand grow.

✓

SLA-led data incident management

investigate and resolve failures through a clear, accountable response process.

✓

Multi-cloud resilience and portability

reduce concentration risk and keep future platform options open.

Managed Data Ops

Operate data platforms around the clock with clear service levels, faster incident response and continuous performance and cost improvement.

✓

24×7 managed pipeline operations

maintain availability and respond quickly when customer-critical data services are at risk.

✓

Continuous performance and cost optimisation

protect margins while data volumes and customer demand grow.

✓

SLA-led data incident management

investigate and resolve failures through a clear, accountable response process.

✓

Multi-cloud resilience and portability

reduce concentration risk and keep future platform options open.

TECHNOLOGIES

The stack behind dependable delivery.

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

CLOUD INFRASTRUCTURE
Azure
GCP
AWS
DATA & AI PLATFORMS
Databricks
Snowflake
Medallion
DATABASES & VECTOR STORAGE
MongoDB
Redshift
PostgreSQL
Dynamo
INTEGRATION, PIPELINES & TRANSFORMATION
Airbyte
Fivetran
Kafka
BI & CUSTOM DASHBOARDS
Power BI
Tableau
OBSERVABILITY & QUALITY
Datadog
Monte Carlo
Pageduty
Prometheus
ENGINEERING AND DEVELOPMENT
GitHub
Python
View all technologies→

TRUSTED BY TEAMS WHO DEPEND ON THEIR DATA

❝

We work with Ardent as a cohesive unit. They understand our project goals and work hard towards our shared measure of success. Their ability to scale software resource in terms of numbers and expertise helps us successfully manage new opportunities.

Nick Church — Director of Engineering

FEATURED SUCCESS STORY

Optimising big data queries for large volumes of complex data

SUCCESS STORY

Optimising big data queries for large volumes of complex data

Giving research and sales teams faster, more reliable access to complex audience data through scalable AWS search and reporting.

Explore all success stories →

TRUSTED BY TEAMS WHO DEPEND ON THEIR DATA

❝

We work with Ardent as a cohesive unit. They understand our project goals and work hard towards our shared measure of success. Their ability to scale software resource in terms of numbers and expertise helps us successfully manage new opportunities.

Nick Church — Director of Engineering

FEATURED SUCCESS STORY

Optimising big data queries for large volumes of complex data

SUCCESS STORY

Optimising big data queries for large volumes of complex data

Giving research and sales teams faster, more reliable access to complex audience data through scalable AWS search and reporting.

Explore all success stories →

TRUSTED BY TEAMS WHO DEPEND ON THEIR DATA

❝

We work with Ardent as a cohesive unit. They understand our project goals and work hard towards our shared measure of success. Their ability to scale software resource in terms of numbers and expertise helps us successfully manage new opportunities.

Nick Church — Director of Engineering

FEATURED SUCCESS STORY

Optimising big data queries for large volumes of complex data

SUCCESS STORY

Optimising big data queries for large volumes of complex data

Giving research and sales teams faster, more reliable access to complex audience data through scalable AWS search and reporting.

Explore all success stories →

NEWS

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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

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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

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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.

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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 →