You build products that run on data. Your own data infrastructure deserves the same standard.

What you often don’t have is capacity — dedicated data engineering and analytics resources that aren’t constantly pulled into product delivery or firefighting.
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Train your team

We help you with

Data platform engineering

Data quality & governance

AI & ML data foundations

Application testing & QA

Team enablement

The data problems technology companies know too well

Your data tools have multiplied. Your data hasn't become easier to use.
The average tech company uses 5–7 data tools, but only 29% of applications are integrated — a fragmented stack where nothing fully agrees.
85% of data projects fail — usually because of data quality, not the technology
Gartner: 60% of AI projects will be abandoned through 2026 due to poor data quality — the model isn't the problem, the data feeding it is.
Your engineers are building product. Data debt is quietly accumulating.
Pipelines built to solve immediate problems with no governance or quality checks — at some point every team is fighting over whose data is correct.
Data Platform Engineering
Platforms built to last — not quick solutions that become tomorrow's technical debt. Snowflake, Databricks, BigQuery, or whatever you're already on.
Data Quality & Governance
Quality controls, ownership models, and monitoring that prevent data debt accumulating — and clean up what's already there.
AI & ML Data Foundations
Clean, governed data that makes ML projects deliver — training data integrity, feature engineering, production pipeline alignment.
Application Testing & QA
For data-intensive applications — functional, regression, performance, and data testing. A pipeline that looks correct can produce subtly wrong outputs.

How we work with technology companies

We work like engineers, not consultants — in your stack, your tools, your codebase. We build for handover from day one so you're not dependent on us permanently. Not a remote advisory layer.

What this looks like for technology companies

Centralised analytics platform replacing four disconnected systems
B2B SaaS company
Snowflake DWH connecting Salesforce, ERP, support, and product data. Certified datasets. Self-service BI — no tickets required.
Recovering a failing AI project through data quality remediation
Technology company
30% of training data corrupted by upstream ID mismatch. Fixed pipeline — model performance improved within one retraining cycle.
Embedded data team during product scaling phase
Fast-growing tech company
4 engineers for 8 months — new platform built, pipelines migrated, full ownership transferred with documentation and 3-month transition.

GenAI Enablement on a Governed Data Foundation

Client: A mid-size B2B software company
IDS built a governed semantic layer on Delta Lake that normalized customer data models into a consistent structure the GenAI assistant could query reliably, then supported the internal team in standing up its own data science capability to maintain and extend it.
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Multi-Cloud Strategy & Cost Optimisation

Client: A growing SaaS / IT services company
IDS ran a cost and architecture review across both AWS and Azure environments, then designed a rationalized multi-cloud strategy — keeping each provider for the workloads it did best, eliminating duplicated pipelines, and applying rightsizing and reserved-capacity planning where waste was highest.
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A Full-Stack Commerce Data Platform, Built and Tested End-to-End

Client: A Full-Stack Commerce Data Platform, Built and Tested End-to-End
IDS delivered full-stack development (Java backend, React front end) with a dedicated QA workstream running in parallel from the start — functional, regression, and performance testing built into every sprint — under a single programme manager accountable for scope, timeline, and quality together.
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Full-Stack Data Platform with Embedded Testing & Knowledge Transfer

Client: A mid-size IT services company
IDS delivered the platform end-to-end (Python and .NET services, automated test coverage from day one) while running a structured knowledge transfer track in parallel — pairing IDS engineers with the client’s internal team throughout, not just in a handover week at the end.
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Regulatory landscape

  • GDPR
  • ISO 27001
  • ISO 27701
  • SOC 2
  • DataOps / MLOps
  • NIS2

Data Platform Cloud Partners

Snowflake
Select Services Partner

As a Snowflake Select Partner, we deliver E2E Snowflake services—from strategy and architecture to migration, optimization, and managed support—helping organizations build modern, scalable data platforms.

Databricks
Registered Partner

We turn scattered data into a governed, AI-ready lakehouse. Our differentiator: Unity Catalog governance and Delta Lake architecture, set up right the first time across all major clouds.

AWS
Partner

We help you build and migrate data platforms without the trial and error. Our differentiator: hands-on depth with Redshift and Glue, so your lakehouse architecture scales without re-work six months in.

Google
Partner

We help you accelerate your cloud journey with technical expertise you can rely on. Our differentiator: serverless BigQuery analytics paired with Looker, so insight moves at the speed of the business, not the infrastructure.

Microsoft
Partner

As a Microsoft Cloud Solution Provider, we bring flexibility and personalized support to every engagement. Our differentiator: Synapse and Fabric expertise, integrated with the Microsoft stack your teams already run on.

Let's talk about your data infrastructure
Building from scratch, cleaning up data debt, or getting an AI initiative back on track — we’ll give you an honest assessment.
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