Building Intelligent Enterprise Systems with AI at the Core

DIATOZ designs, builds, and scales AI-powered digital products and enterprise platforms across financial services, telecom, manufacturing, healthcare, and logistics.

150+Engineers

30+Case Studies

2018Founded

1AI Patent

Four Pillars of Delivery

We combine deep engineering, domain expertise, and modern AI stacks to deliver outcomes, not just software output.

AI and GenAI Systems

On-prem LLM deployments, private copilots, document intelligence, and agentic workflows for enterprise teams.

On-Prem LLM AI Copilots RAG

Product and Platform Engineering

Cloud-native platforms, microservices, and resilient distributed systems designed for scale and security.

Cloud Native Microservices DevSecOps

Enterprise Integration

API-led integration across legacy and modern systems using MuleSoft, event-driven designs, and workflow automation.

MuleSoft API Gateway Automation

Data and Decision Intelligence

Data pipelines, real-time analytics, and operational intelligence that improves decision velocity across teams.

Kafka Spark Dashboards

Execution Framework

1

DiscoverBusiness mapping

2

ArchitectSolution blueprint

3

BuildAgile delivery

4

SecureDevSecOps

5

ScaleCloud rollout

6

OptimizeContinuous value

Products Built from Enterprise Pain Points

Our products emerge from real implementation contexts and are used to accelerate time-to-value in production environments.

FlowWork.ai

AI Work Platform

A unified AI workspace for leadership and teams to run strategy, execution tracking, and operational actions with AI support.

Valtren AI

Supervisory Intelligence

Connects ERP, MES, CMMS, SCADA, and IoT streams to power predictive and prescriptive operational decisions.

e2eHiring

Hiring and Recruitment

A full-cycle hiring platform for sourcing, assessment, and structured recruitment workflows across sectors.

FlowYard

Yard and Logistics AI

Brings visibility to vehicle movement, slot usage, and turnaround analytics with AI-driven operational controls.

Trusted by Leading Organizations

Engagement Model for New Clients

We usually begin with a focused discovery sprint, then transition into architecture, implementation, and measurable rollout with outcome checkpoints.

Phase 1: Discovery Sprint

2 to 3 weeks to align business goals, system landscape, data readiness, and a prioritized roadmap.

Phase 2: Architecture and Pilot

4 to 8 weeks to stand up foundational architecture and deliver one production-grade pilot initiative.

Phase 3: Scale and Integrate

Expand to enterprise systems, embed observability and governance, and accelerate multi-team adoption.

Phase 4: Continuous Optimization

Quarterly value reviews, AI model tuning, release optimization, and ongoing cost-performance management.

Ready to build your next intelligent platform?

Contact us to schedule a discovery session and receive a tailored solution blueprint for your enterprise use case.

diatoz.com | [email protected]