Whatever the deployment — readiness assessment, integration, or Build-Operate-Transfer — documented outcomes from Day 1. Not another advisory engagement.
Pilot to production-deployed, on your stack. EZ builds to your technical specifications, operates against documented benchmarks, and transfers with full handover — no CapEx commitment, data governance built in from architecture.
AI in production for enterprise clients since 2018.
Pick a real situation your team faces — see how EZ turns it around, and which of our 85+ services combine under one brief to deliver it.
The board wants to know if we can act. Strategy-firm assessments consume the pilot budget.
We learned whether our data could support AI before spending the pilot budget on a long assessment.
Chief Digital Officer · Insurance · Dubai
A pilot proved feasible months ago, then stopped. The board wants something running.
The stalled pilot is now in production, and the board has something running that it can see.
Head of AI · Banking · Riyadh
Its provider is paid by headcount, so efficiency has not happened in five years of asking.
Our BPO process now works to an efficiency target instead of a headcount count, after years of asking.
COO · Financial services · Abu Dhabi
The workflow is ready and the case is clear. The machine-learning role has been open for months.
The claims-triage agent got built while the machine-learning role was still open, and the workflow went live.
VP Transformation · Insurance · Doha
Paper records, scans and inconsistent systems block every AI project before it starts.
Our paper records and scans are usable data now, so AI projects can finally start.
Chief Data Officer · Healthcare · Jeddah
The board wants a credible account of controls. The documents that exist are written for engineers.
The AI governance pack explains the controls in words a board can follow, not only engineers.
Chief Risk Officer · Banking · Dubai
The rollout is failing on adoption. Staff do not know if it is coming for their role.
A short video explained the AI rollout in plain terms, and staff finally knew what it means for their roles.
Head of Change · Utilities · Riyadh
The model works; the interface does not. Adoption is blamed on change resistance.
The model was always fine. Once the interface was redesigned, people started using the tool.
Head of Innovation · Logistics · Dubai
The capability is deployed; the workforce is not trained. No programme, curriculum or owner.
Our teams were trained on the new AI capability, with a curriculum and an owner behind it.
Learning Director · Banking · Abu Dhabi
The deployment shipped undocumented. Two engineers hold the knowledge and have moved on.
The deployment is documented now, so users no longer depend on two engineers who have moved on.
Head of Digital Operations · Telecoms · Dubai
Outputs the model is unsure of need a human. Neither review nor user support was scoped.
A human review layer sits behind our AI now, and users have someone to ask when the output is unsure.
Director of Operations · Insurance · Riyadh
GCC · Enterprise · Anonymised
A GCC-based enterprise with a failed generative AI pilot engaged EZ for an AI Readiness Assessment. The assessment identified 11 data gaps, a priority deployment sequence for four AI applications, and a BOT engagement structure. Two applications were in production within six months; documented OpEx reduction in the first deployed process: 29%.
We'd been piloting for 18 months with no production deployment. EZ's readiness assessment identified the three data governance gaps blocking us. We were in production within four months of that assessment.
Chief Digital Officer · Financial Services Group · UAE
EZ does not produce AI strategies. EZ executes them. The AI Readiness Assessment produces a deployment plan, not a report. The BOT engagement deploys production AI, not a pilot. The CTO gets something to show the board, not a presentation about what could be deployed.
EZ builds the AI capability, operates it at the client's standard, and transfers it to the internal team on the client's timeline. The client shifts existing OpEx budget — no large CapEx required. EZ commits to documented year-on-year efficiency gains from the first deployed process.
EZ operates across jurisdictions where AI regulation is evolving fastest — EU AI Act, Saudi SDAIA guidelines, UAE AI regulation, and PDPL data sovereignty requirements are understood and built into every deployment. No learning curve on local regulatory context, wherever the deployment sits.
Enterprise AI deployments handle sensitive business data, customer records, and operational process data. EZ's certified information security framework meets the data governance requirements that enterprise AI deployments require.
The urgency premium, the co-ordination overhead, and the morning-after fix never appear on an invoice. Until you're paying them.
EZ
EZ builds the AI capability — integration, data pipeline, model deployment, and workflow redesign. EZ operates it at the agreed performance standard for a defined period. At the agreed transfer point, the capability and institutional knowledge moves to the client's internal team. The client shifts existing process budget to EZ; EZ commits to efficiency gains from Day 1.
The assessment scope determines the timeline. A focused assessment of a specific process or data domain takes 2–4 weeks. A full enterprise AI readiness assessment covering data quality, governance, infrastructure, and regulatory compliance takes 4–8 weeks. Both produce a board-presentable output with a prioritised deployment sequence.
Yes. EZ's deployment model works with the enterprise AI tools the client has already licensed — OpenAI, Microsoft Azure AI, Google Cloud Vertex, AWS Bedrock, or an existing on-premise deployment. EZ does not require platform displacement.
EZ operates under ISO 27001:2022 certification with need-to-know data access controls. For Saudi deployments, PDPL requirements are built into the data handling model. For clients requiring data residency, in-kingdom or in-country deployment options are available.
Before each deployment, EZ and the client agree on a baseline process metric — time per transaction, error rate, cost per output, or throughput. The efficiency gain is measured against that baseline in production. Documented ranges across prior implementations: 26–31% OpEx reduction for supply chain, finance, and operations processes.
A structured 60-minute call to assess where your AI deployments are stalling and what the fastest path to production looks like. No commitment.
No commitment. No minimum. ISO 27001:2022 certified.
