What we do
From raw data to AI-driven insight
We connect your systems, build reliable data foundations, deliver Power BI reporting, and put AI and agents to work on your data.
Data & Application Integration
Ingest your data from any source into a single location. Sync master data between your cloud applications.
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Data Architecture & Engineering
Combine your data into a data lake or warehouse. Clean, harmonise, transform and aggregate. Define Semantic Models with core measures.
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Interactive Dashboards & Reporting
Provide a complete picture of your business performance. Turn data into insights with interactive Power BI content.
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AI-Driven Insight & Automation
Ask your data questions in plain English, with AI summaries and trends. Governed agents on Microsoft Foundry act in your tools.
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Accelerators
What our accelerators cover
Reusable accelerators for integration, data engineering, semantic models and AI agents give you a flying start.
- Integration
- Connectors, sync patterns, transformation and monitoring for the applications we see most, so a new integration is mostly assembly.
- Data engineering (ETL)
- Proven pipeline patterns for landing data from any source in your lake or warehouse, then refining it through bronze, silver and gold layers.
- Semantic models
- A standard approach to modelling, naming and measures, so reporting and AI work from one consistent version of the truth.
- Agent workflows
- The InsytesAgents pattern on Microsoft Foundry and the Agent Framework, with MCP, Copilot and Teams routes already built.
In practice
A complete picture of your business in near real time
Hear from the engineers who build it, and see how connected data turns into the view leadership acts on.
Sound familiar?
The problems we hear most
We’ve invested in all these systems but I still can’t see across the business
You’ve got a CRM, an ERP, a finance system, and a dozen other tools, each doing its job, none talking to each other.
We connect them into a single, governed data environment so you see performance across every function, not just the slice each system shows.
We know we should be doing more with AI, but we’re not ready
Most organisations aren’t. AI needs clean, connected, semantically modelled data, and most don’t have that yet.
We build those foundations, then deploy natural language querying, automated analysis and agents that deliver real value, not just demos.
We tried AI, but I can’t trust the answers it gives us
Pasting spreadsheets into a chatbot is quick, but two people can get two different answers to the same question.
We connect AI to a governed data layer tuned to your business, so every answer comes from the same trusted numbers as your dashboards.
I don’t trust the numbers I’m looking at
When every department produces its own version of revenue, headcount, or pipeline, confidence collapses and decisions stall.
We build a single source of truth: one set of agreed definitions, one trusted dataset, so everyone in the room is working from the same facts.
It takes us a week to answer a question that should take five minutes
If your team still pulls data from three systems and reconciles it in Excel first, that’s not reporting, it’s admin.
We replace that with automated, near real-time dashboards that answer questions in the moment.
My best people are wasting their time compiling reports
When skilled analysts spend their week assembling data instead of analysing it, you’re paying for insight and getting admin.
We automate the build so your team can focus on what the numbers mean, not on putting them together.
FAQ
Frequently asked questions
What business problems can data integration solve?
Data integration can solve problems caused by disconnected systems, duplicated information, manual data entry and incomplete reporting. When applications and databases do not communicate properly, teams often lose time reconciling data or working with outdated information. Connecting systems helps create a clearer view of operations, customers, finance, sales and performance across the organisation.
Why do businesses need data architecture before building dashboards or AI tools?
Good data architecture gives a business a reliable structure for storing, managing and using information. Without it, dashboards may be inaccurate, reports can become difficult to maintain and AI tools may produce poor results because the underlying data is incomplete or inconsistent. A strong data foundation makes analytics, automation and AI-powered insights more reliable and trustworthy.
How can business intelligence improve reporting?
Business intelligence improves reporting by connecting data sources, reducing spreadsheet-based processes and creating dashboards that update with reliable information. This helps teams spend less time gathering data and more time understanding performance. It also makes reporting more consistent, so departments can work from the same figures instead of relying on disconnected files or conflicting versions of the truth.
What is the difference between business intelligence and AI?
Business intelligence (BI) uses historical data to report on what has happened. AI goes further by identifying patterns, predicting outcomes, and giving you information that standard reporting can miss. The most effective setups use both: BI and artificial intelligence solutions.
How can natural language querying help businesses use their data?
Natural language querying allows users to ask questions about their data in plain English, rather than relying only on technical reports or manual analysis. This makes business intelligence more accessible to non-technical teams and helps users find answers faster across sales, finance, operations and other business areas.
How can AI agents support business decision-making?
AI agents can monitor business data, identify changes, send alerts and support faster decision-making. When connected to reliable data foundations, AI agents can help teams spot risks, trends or anomalies more proactively, reducing the need for constant manual checks and making analytics more actionable.
