About DAESAIX
DAESAIX was founded in June 2026 on a simple premise: most organizations have the data and ambition for transformation, but lack the engineering depth to build production-grade AI and data systems that actually work at scale.
We're building that capability now—combining deep technical expertise with practical business understanding to deliver systems that don't just demo well, but run reliably in production.
“Our mission is to give every organization access to the same intelligence infrastructure that powers the world's most sophisticated technology companies.”
What cloud infrastructure delivers
One source of truth across your business with sub-second queries on large datasets.
Move from on-prem to cloud with zero downtime while legacy systems stay integrated.
Automated clustering and cost optimization designed to reduce unnecessary compute spend.
Row-level access controls, audit trails, and SOC 2, HIPAA, and GDPR-ready patterns.
Zhandaulet Yespossynov
Founder & CEO
Founder's Vision
“I founded DAESAIX in June 2026 because most organizations have the data and the ambition—but lack the engineering depth to act on it intelligently at scale.
Our mission is to give every organization access to the same intelligence infrastructure that powers the world's most sophisticated technology companies.
What we are building is the next generation of operational technology: AI agents that handle front-line work, unified cloud data layers that power every decision, and platforms that migrate and scale without breaking the business.
We do not chase hype. We engineer systems that deliver value from day one and keep delivering for years. That is the future DAESAIX is building—and we invite you to build it with us.”
How We're Organized
A focused team structure built around engineering depth, reliable delivery, and long-term client partnerships.
Leadership
Founder & CEO
Company vision, strategy, and key client partnerships
Technical Leadership
AI & Automation
LLM pipelines, voice AI, receptionist systems, and multi-agent workflows
Data & Analytics
Data engineering, cloud warehousing, BI, and predictive models
Cloud & Platforms
AWS, Azure, GCP architecture, SaaS development, and legacy migration
Delivery Leadership
Architecture
Solution design, integration planning, and security blueprints
Implementation
Build, deploy, CI/CD, testing, and production monitoring
Partnership Lead
Engagement
Discovery, scoping, onboarding, and delivery roadmaps
Optimization
Performance tuning, model updates, and long-term scale
Our Story
Founded in June 2026—we're building DAESAIX from the ground up.
Zhandaulet Yespossynov founded DAESAIX with a clear focus: build production-grade AI, data, and cloud systems for businesses in the USA and Canada.
Established our core practices—AI automation, voice AI, data engineering, analytics, and cloud platforms—and set the engineering standards we build by.
Actively building the company: developing our platform, refining service offerings, launching this website, and opening conversations with our first clients.
What Drives Us
These principles shape every technical decision and every client interaction.
We don't ship prototypes. Every system is designed for production: resilient, observable, secure, and built to scale from the moment it's deployed.
Technology is a means, not an end. We tie every engagement to measurable business outcomes—revenue, cost, efficiency—and we track them relentlessly.
We build with explainability, human override controls, and comprehensive audit trails so you're always in control of your AI systems.
We move at startup speed with enterprise engineering practices—CI/CD, automated testing, infrastructure as code—so speed never compromises quality.
We operate as an extension of your team. Honest advice, transparent delivery, long-term thinking over short-term billing.
We design AI that's fair, explainable, and compliant. Security and privacy aren't afterthoughts—they're engineered in from line one.
Technology Stack
We're fluent across the full modern AI and data stack—no hand-offs, no gaps.
