Company

Services

Sectors

Platforms

Success Stories

Insights

Contact Us

Company

Services

Sectors

Platforms

Success Stories

Insights

Contact Us

Menu

We resolve data quality, consistency and pipeline issues at the root giving your models and agents a foundation they can completely rely on.

15+YEARS CREATING SUCCESS WITH CLIENTS

DATA FOR AI

Turning AI ambition into results

Successful AI depends on more than choosing the right model. It depends on the quality, consistency and accessibility of the data that models, agents and applications use every day.

Ardent prepares and strengthens those foundations, resolving hidden data issues before they weaken outputs, limit adoption or prevent AI initiatives from moving reliably into production.

3,000+

3,000+

Users supported on one AI platform

We brought separate AI tools into one connected agent platform, giving thousands of users a simpler way to access them.

60%

Reduction in avoidable processing costs

Predictive monitoring reduced costly pipeline reruns and excess compute, showing how better data operations can remove unnecessary spend.

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 Discover and prioritise

Identify the highest-value AI use cases, data sources and readiness gaps.

02 Prepare and govern

Clean, structure and control the data, content and definitions AI will use.

03 Build and connect

Engineer retrieval, feature and deployment pipelines around approved enterprise knowledge.

04 Operate and improve

Monitor quality, evaluate outputs and refine models as conditions change.

CAPABILITIES

Building the Data Foundations for Production AI

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

From Raw Data to Production Ready AI

We turn fragmented enterprise data into a trusted, governed foundation that helps AI initiatives move faster and perform reliably at scale.

✓

AI-ready lakehouse architecture

Unite fragmented data in one scalable foundation, helping AI projects move into production faster.

✓

Enterprise knowledge platforms

Make organisational knowledge easier to find, trust and use across teams, models and AI agents.

✓

Enterprise semantic layers

Give teams and AI systems consistent business definitions, reducing conflicting answers and decisions.

✓

AI data preparation and governance

Deliver cleaner, richer inputs with clear controls for access, quality, lineage and permitted use.

From Raw Data to Production Ready AI

We turn fragmented enterprise data into a trusted, governed foundation that helps AI initiatives move faster and perform reliably at scale.

✓

AI-ready lakehouse architecture

Unite fragmented data in one scalable foundation, helping AI projects move into production faster.

✓

Enterprise knowledge platforms

Make organisational knowledge easier to find, trust and use across teams, models and AI agents.

✓

Enterprise semantic layers

Give teams and AI systems consistent business definitions, reducing conflicting answers and decisions.

✓

AI data preparation and governance

Deliver cleaner, richer inputs with clear controls for access, quality, lineage and permitted use.

GenAI Data Foundations

We connect generative AI to trusted enterprise knowledge so responses become more accurate, relevant and useful to the business.

✓

Retrieval architecture

Help AI applications find and rank the right enterprise information quickly and consistently.

✓

RAG pipelines

Ground AI responses in approved enterprise content, improving relevance, accuracy and trust.

✓

Vector databases and stores

Help AI find relevant information by meaning across large volumes of unstructured content.

✓

AI-ready content preparation

Turn raw documents into clean, searchable knowledge that generative AI can use effectively.

GenAI Data Foundations

We connect generative AI to trusted enterprise knowledge so responses become more accurate, relevant and useful to the business.

✓

Retrieval architecture

Help AI applications find and rank the right enterprise information quickly and consistently.

✓

RAG pipelines

Ground AI responses in approved enterprise content, improving relevance, accuracy and trust.

✓

Vector databases and stores

Help AI find relevant information by meaning across large volumes of unstructured content.

✓

AI-ready content preparation

Turn raw documents into clean, searchable knowledge that generative AI can use effectively.

MLOps / LLMOps Engineering

We help AI move into production with the controls, monitoring and improvement processes needed for dependable long-term performance.

✓

Model deployment pipelines

Move approved models into production faster through controlled, repeatable release processes.

✓

Feature stores

Reuse governed model inputs consistently across training and production, reducing errors and duplicated work.

✓

Prompt operations and evaluation

Manage versions, testing and releases while measuring quality against clear business criteria.

✓

AI monitoring and continuous retraining

Detect drift and keep models relevant as data, behaviour and operating conditions change.

MLOps / LLMOps Engineering

We help AI move into production with the controls, monitoring and improvement processes needed for dependable long-term performance.

✓

Model deployment pipelines

Move approved models into production faster through controlled, repeatable release processes.

✓

Feature stores

Reuse governed model inputs consistently across training and production, reducing errors and duplicated work.

✓

Prompt operations and evaluation

Manage versions, testing and releases while measuring quality against clear business criteria.

✓

AI monitoring and continuous retraining

Detect drift and keep models relevant as data, behaviour and operating conditions change.

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

❝

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

Improving data turnaround by 80% with Databricks

SUCCESS STORY

Improving data turnaround by 80% with Databricks

Improving data reporting turnaround by 80% for a Fortune 500 media company using parallel Databricks processing and ongoing operational support.

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

Improving data turnaround by 80% with Databricks

SUCCESS STORY

Improving data turnaround by 80% with Databricks

Improving data reporting turnaround by 80% for a Fortune 500 media company using parallel Databricks processing and ongoing operational support.

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

Improving data turnaround by 80% with Databricks

SUCCESS STORY

Improving data turnaround by 80% with Databricks

Improving data reporting turnaround by 80% for a Fortune 500 media company using parallel Databricks processing and ongoing operational support.

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

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

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

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

Why Targeted Advertising Depends on Better Data Pipelines

Why better advertising intelligence depends on the pipelines behind retail, viewing and campaign data, and how reliable preparation, governance and responsiveness turn fragmented signals into usable insight.

Jack Bailey

Read insight

AI Readiness

Overcoming Data Administration Challenges, and Strategies for Effective Data Management

A practical look at common data administration challenges, including growing datasets, outdated infrastructure, resource constraints and security, and the management practices that help organisations keep data reliable and usable.

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

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

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

Why Targeted Advertising Depends on Better Data Pipelines

Why better advertising intelligence depends on the pipelines behind retail, viewing and campaign data, and how reliable preparation, governance and responsiveness turn fragmented signals into usable insight.

Jack Bailey

Read insight

AI Readiness

Overcoming Data Administration Challenges, and Strategies for Effective Data Management

A practical look at common data administration challenges, including growing datasets, outdated infrastructure, resource constraints and security, and the management practices that help organisations keep data reliable and usable.

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

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

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

Why Targeted Advertising Depends on Better Data Pipelines

Why better advertising intelligence depends on the pipelines behind retail, viewing and campaign data, and how reliable preparation, governance and responsiveness turn fragmented signals into usable insight.

Jack Bailey

Read insight

AI Readiness

Overcoming Data Administration Challenges, and Strategies for Effective Data Management

A practical look at common data administration challenges, including growing datasets, outdated infrastructure, resource constraints and security, and the management practices that help organisations keep data reliable and usable.

Ardent

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 →