// AI / DATA & ANALYTICS
Your clients' AI cannot run on a broken data foundation.
We have the data somewhere, we just cannot use it: every partner hears it. We build the lakehouse, migrate the legacy warehouse, wire in governance from the first day, and hand business users conversational analytics. The platform is AI-ready and it ships under your brand.
Start a data conversationThe AI delivery gap starts as a data delivery gap.
Enterprises sit on petabytes of siloed, inconsistent, ungoverned data spread across legacy warehouses, SaaS platforms and cloud buckets, and none of it is ready to feed an AI initiative. Standing up a data engineering practice from scratch takes time, certifications and headcount most partners do not have. Meanwhile the competitor who can answer do you have data platform delivery capability in an RFP is the one who wins the deal.
What we build into the platform.
Lakehouse Architecture
Unified platforms on Microsoft Fabric, Azure Synapse or BigQuery, migrating clients off legacy Oracle, SQL Server and Teradata warehouses onto AI-ready cloud foundations. AI-powered accelerators cut migration time and cost by up to 60 percent.
Legacy Warehouse Migration
A structured path from on-premises to cloud that protects the data and the business logic. Benchmarked deployments have reached 97 percent reductions in processing cost while standard 8-to-50-week timelines compress under the accelerators.
Governance From Day One
Microsoft Purview and Amazon DataZone wired in at the start, not bolted on later: data lineage, sensitivity classification, policy enforcement and audit trails. This is what GDPR and the EU AI Act actually require, ready before the auditor asks.
Generative BI
Power BI with Copilot and Amazon QuickSight Q put conversational analytics in front of business users, so they ask questions in plain language and get answers with anomaly detection, instead of waiting on a report from the data team.
Real-Time Streaming
Streaming pipelines on Azure Stream Analytics, AWS Kinesis and Apache Kafka (MSK) so clients act on operational data as it lands: fraud detection, IoT anomaly signals, live inventory and operational dashboards.
AI-Readiness as the Outcome
The whole platform is engineered so the client's AI initiatives have clean, governed, well-modelled data to run on. The AI delivery gap starts as a data delivery gap, and this is where we close it.
The numbers behind a modern lakehouse.
How the options compare.
What your client gets from an internal team, a single-cloud vendor, or our team under your brand.
Data and analytics, answered straight.
01 How long does a Microsoft Fabric migration take?
Timelines run from about eight weeks for a clean single-warehouse migration to fifty weeks for a complex multi-source environment with heavy transformation logic. AI-powered accelerators cut both time and cost by up to 60 percent. We give a detailed estimate during the architecture assessment, before any commitment.
02 What is the ROI of moving to a lakehouse?
Forrester's Total Economic Impact study found Microsoft Fabric delivers 379 percent ROI over three years when implemented with a partner. Benchmarked deployments show up to 97 percent reductions in processing cost, and partner-led builds reach around 156 percent higher ROI than going it alone internally. The data is consistent: this is not a build to attempt without delivery muscle.
03 We already have a small data team. Won't you compete with them?
No. We operate as your delivery extension, not a rival practice. Your data team designs and owns the architecture; we bring the pipeline engineers, data modellers and governance specialists they need to execute. Every deliverable carries your team's name, and the platform stays theirs to run.
04 How do you handle governance for GDPR?
Microsoft Purview or Amazon DataZone goes in from day one, never as an afterthought: data lineage, sensitivity classification, access controls, policy enforcement, and the cross-border transfer documentation GDPR requires. For clients under the EU AI Act, we add the Annex III data-provenance documentation high-risk systems need.
05 What is the difference between Azure Synapse and Microsoft Fabric?
Synapse is a dedicated analytics service focused on warehousing and big-data integration. Fabric is the unified platform that brings data engineering, data science, real-time analytics and BI into one SaaS experience, building on Synapse while adding OneLake, Copilot and simplified governance. Microsoft is guiding customers toward Fabric as the modern path, and we plan migrations with that in mind.
06 Can you handle real-time streaming analytics?
Yes. We design and deploy streaming pipelines on Azure Stream Analytics, AWS Kinesis and Apache Kafka (MSK), so clients act on operational data as it happens: fraud detection, IoT anomaly detection, real-time inventory signals and live operational dashboards.
Tell us what your clients need.
A tri-cloud migration. A 200-site SD-WAN rollout. A security architecture before NIS2 hits. An AI system your client is asking about next quarter. We scope it, staff it, and deliver it under your brand. One conversation tells us if we are the right team.