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Ardentisys works with fast-growing software product and service organisations to power scalable schema design, unifying duplicated pipelines and inconsistent metrics into one governed data foundation, so fast-moving engineering teams can scale without messy internal data blocking their initiatives.

15+YEARS CREATING SUCCESS WITH CLIENTS

TECHNOLOGY

Move faster without compromising quality

Move faster without compromising quality

Fast-growing technology companies often accumulate duplicated pipelines, inconsistent schemas and conflicting metrics as products and teams expand. Over time, fragmented data starts to slow engineering, product analysis and AI development.

Ardent creates governed, cloud-native data foundations that support real-time product intelligence, self-service access, AI applications and reliable ongoing operations.

4 PB

Data managed in one enterprise environment

We supported and optimised a multi-petabyte data estate, helping keep complex workloads manageable over the long term.

40%

Reduction in ongoing cloud costs

Continuous oversight and optimisation reduced monthly infrastructure spend across a managed data environment.

From Data to Impact

OUR APPROACH

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

01  Rationalise the foundation

Remove duplicated pipelines, conflicting schemas and avoidable platform friction.

02  Define trusted metrics

Align teams around governed entities, events and measures.

03  Enable the teams

Provide self-service data, live product signals and AI-ready knowledge.

04  Scale reliably

Manage performance, cost and incidents as products and usage grow.

CAPABILITIES

Building Data Foundations That Scale With Your Product

We create governed, scalable data foundations that give product and engineering teams faster access to trusted data, reliable insight and production-ready AI.

Data for AI

Give product teams reliable data, retrieval and evaluation foundations for AI features, agents and internal knowledge tools that can scale beyond experimentation.

✓

AI-ready product pipelines

give analytics, agents and intelligent features access to timely, consistent product data.

✓

RAG and vector retrieval

help employees and developer tools find accurate internal knowledge faster.

✓

Prompt operations and evaluation

improve the consistency, safety and measurable quality of AI product experiences.

✓

LLM fine-tuning data preparation

create clean, representative and controlled datasets for specialised model behaviour.

Data for AI

Give product teams reliable data, retrieval and evaluation foundations for AI features, agents and internal knowledge tools that can scale beyond experimentation.

✓

AI-ready product pipelines

give analytics, agents and intelligent features access to timely, consistent product data.

✓

RAG and vector retrieval

help employees and developer tools find accurate internal knowledge faster.

✓

Prompt operations and evaluation

improve the consistency, safety and measurable quality of AI product experiences.

✓

LLM fine-tuning data preparation

create clean, representative and controlled datasets for specialised model behaviour.

Data for Automation

Give engineering and product teams faster access to scalable environments, real-time product data and automated platform delivery. ons for AI features, agents and internal knowledge tools that can scale beyond experimentation.

✓

Self-service data environments

give engineering and product teams governed access without waiting for repeated manual setup.

✓

Real-time product-usage pipelines

turn live customer behaviour into faster product and commercial insight.

✓

Automated platform delivery

release data-platform changes faster through CI/CD and Infrastructure as Code.

✓

Cloud-native lakehouse architecture

eplace fragmented systems with one scalable foundation for product data and analytics.

Data for Automation

Give engineering and product teams faster access to scalable environments, real-time product data and automated platform delivery. ons for AI features, agents and internal knowledge tools that can scale beyond experimentation.

✓

Self-service data environments

give engineering and product teams governed access without waiting for repeated manual setup.

✓

Real-time product-usage pipelines

turn live customer behaviour into faster product and commercial insight.

✓

Automated platform delivery

release data-platform changes faster through CI/CD and Infrastructure as Code.

✓

Cloud-native lakehouse architecture

eplace fragmented systems with one scalable foundation for product data and analytics.

Data for Reporting

Establish one trusted metrics layer across engineering, product, commercial and leadership teams so everyone works from the same view of performance.

✓

Unified product metrics layer

give every team consistent definitions and one source of truth for performance.

✓

Internal data catalogue

help engineering teams find, understand and reuse trusted data assets.

✓

Reporting observability

detect broken dashboards, stale metrics and unexpected changes before decisions are affected.

✓

A/B testing data quality

protect experimentation decisions through consistent event capture, definitions and validation.

Data for Reporting

Establish one trusted metrics layer across engineering, product, commercial and leadership teams so everyone works from the same view of performance.

✓

Unified product metrics layer

give every team consistent definitions and one source of truth for performance.

✓

Internal data catalogue

help engineering teams find, understand and reuse trusted data assets.

✓

Reporting observability

detect broken dashboards, stale metrics and unexpected changes before decisions are affected.

✓

A/B testing data quality

protect experimentation decisions through consistent event capture, definitions and validation.

Managed Data Ops

Reduce operational burden on internal teams by managing platform reliability, cost, architecture and incident response as the business scales. levels, faster incident response and continuous performance and cost improvement.

✓

Managed platform operations

free internal teams to focus on product development rather than ongoing pipeline maintenance.

✓

Cloud and data-spend optimisation

improve visibility, control and value as usage scales.

✓

Hypergrowth data roadmap

plan the architecture, priorities and operating model needed for the next stage of scale.

✓

Platform monitoring and incident response

identify and resolve infrastructure issues before they disrupt product and engineering teams.

Managed Data Ops

Reduce operational burden on internal teams by managing platform reliability, cost, architecture and incident response as the business scales. levels, faster incident response and continuous performance and cost improvement.

✓

Managed platform operations

free internal teams to focus on product development rather than ongoing pipeline maintenance.

✓

Cloud and data-spend optimisation

improve visibility, control and value as usage scales.

✓

Hypergrowth data roadmap

plan the architecture, priorities and operating model needed for the next stage of scale.

✓

Platform monitoring and incident response

identify and resolve infrastructure issues before they disrupt product and engineering teams.

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
DATABASES & VECTOR STORAGE
MongoDB
Redshift
PostgreSQL
INTEGRATION, PIPELINES & TRANSFORMATION
Airbyte
Fivetran
Kafka
BI & CUSTOM DASHBOARDS
React
Angular
OBSERVABILITY & QUALITY
Datadog
Pageduty
Prometheus
ENGINEERING AND DEVELOPMENT
GitHub
Python
Microsoft.net
View all technologies→

TRUSTED BY TEAMS WHO DEPEND ON THEIR DATA

❝

Ardent were able to engage their developers as an extension of our own resource. At Ardent, we were able to access high-quality developers. After a project brief, they go off, they develop a solution and they rigorously test the software before it is being released.

Chris Elson — Systems Director

FEATURED SUCCESS STORY

10 TB data lake for survey and near-real-time social media data

SUCCESS STORY

10 TB data lake for survey and near-real-time social media data

Unifying a 10 TB estate of survey and social-media data for scalable, near-real-time analysis and reporting.

Explore all success stories →

TRUSTED BY TEAMS WHO DEPEND ON THEIR DATA

❝

Ardent were able to engage their developers as an extension of our own resource. At Ardent, we were able to access high-quality developers. After a project brief, they go off, they develop a solution and they rigorously test the software before it is being released.

Chris Elson — Systems Director

FEATURED SUCCESS STORY

10 TB data lake for survey and near-real-time social media data

SUCCESS STORY

10 TB data lake for survey and near-real-time social media data

Unifying a 10 TB estate of survey and social-media data for scalable, near-real-time analysis and reporting.

Explore all success stories →

TRUSTED BY TEAMS WHO DEPEND ON THEIR DATA

❝

Ardent were able to engage their developers as an extension of our own resource. At Ardent, we were able to access high-quality developers. After a project brief, they go off, they develop a solution and they rigorously test the software before it is being released.

Chris Elson — Systems Director

FEATURED SUCCESS STORY

10 TB data lake for survey and near-real-time social media data

SUCCESS STORY

10 TB data lake for survey and near-real-time social media data

Unifying a 10 TB estate of survey and social-media data for scalable, near-real-time analysis and reporting.

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

The 5 layers of software – what you need to know

A practical explanation of layered software architecture and the roles played by the presentation, application, business logic, data access and database layers in building maintainable applications.

Ardent

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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

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

The 5 layers of software – what you need to know

A practical explanation of layered software architecture and the roles played by the presentation, application, business logic, data access and database layers in building maintainable applications.

Ardent

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

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

The 5 layers of software – what you need to know

A practical explanation of layered software architecture and the roles played by the presentation, application, business logic, data access and database layers in building maintainable applications.

Ardent

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

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

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 →