On September 15 in Astana, executives from ForteBank, Qazsport, and Magnum took the stage to detail how they deploy artificial intelligence and AWS cloud infrastructure. RAEM examined which enterprise implementations are already backed by measurable business metrics—and which remain preliminary conference promises.
September 15, 2026 · 09:00 · Alem AI, Astana · Admission via mandatory pre-registration
Ahead of the conference, ForteBank’s implementation is by far the most rigorously documented with verifiable numbers. For Qazsport and Magnum, the most critical architectural answers must be delivered live on stage.
AWS Cloud & AI Day Astana convened at the Alem AI innovation hub. The agenda featured keynotes from AWS, Softprom, and GitLab, alongside government technology leaders and three prominent Kazakhstani enterprise case studies: ForteBank, Qazsport, and retail giant Magnum Cash & Carry.
Context is vital: according to Bureau of National Statistics data reported by Kapital.kz, only 3.4% of surveyed Kazakhstani enterprises (approximately 5,100 companies) utilized AI technologies in 2025. Consequently, examining three corporate deployments on a single stage represents a rare industry cross-section. However, the depth of empirical proof across the three varies drastically.
ForteBank: Comprehensive Metrics and Verifiable Impact
Among the three corporate cases, ForteBank presents by far the most transparent, thoroughly documented transformation. Chief Data & AI Officer Nurzhan Smailov frames the concept of an AI-native financial institution simply: rather than merely migrating legacy manual procedures online, the bank first asks whether the process itself is necessary if neural networks can handle the cognitive load autonomously. Smailov detailed this operating philosophy in an in-depth interview with Digital Business.
This architectural shift is backed by substantial metrics. The bank’s AI-Nur virtual conversational agent has processed over 2 million customer inquiries, autonomously resolving more than 60% of all digital channel requests without human routing. Customer satisfaction ratings (CSAT) hover at approximately 80%. Machine learning models assist in underwriting creditworthiness, shrinking preliminary loan reviews for SMEs down to 10 minutes. According to internal bank audits, automated AI systems generate operational savings exceeding 3,200 person-days per month.
Metrics cited from Nurzhan Smailov’s interview with Digital Business published July 2, 2026.
The operational scale extends deeper: AI models assist in preparing analytical dossiers for the credit committee and analyze nearly the entire omnichannel stream of customer service interactions. Crucially, final loan authorizations and statutory fiduciary compliance remain anchored by human officers.
One pivotal architectural question persists: prior to the event, public documentation confirming which specific Forte systems reside on AWS public cloud infrastructure remained undisclosed. This nuance is crucial to clarify on stage. Nevertheless, Forte’s broader AI roadmap is already validated by published numbers and third-party analysis.
Qazsport: Cloud Streaming Evaluated Against Past Outages
Qazsport’s keynote presents the opposite dynamic. The agenda highlights fault-tolerant sports video streaming on AWS designed to scale to 5 million concurrent viewers. Yet prior to the conference, verified production benchmarks of this cloud migration were nonexistent. Instead, the national sports broadcaster carries a well-publicized history of high-profile streaming failures.
On October 1, 2025, Qazsport’s digital streaming infrastructure crashed under peak traffic during the UEFA Champions League clash between Kairat and Real Madrid, despite the channel having tripled its provisioned server capacity beforehand. The broadcaster issued an official apology citing external network routing bottlenecks, as reported by Orda.kz and Informburo.kz.
In June 2026, football viewers voiced fresh outrage over 2-to-3-minute transmission latencies during World Cup matches. Qazsport Director Nagi Bakytbekov attributed the latency to OTT video processing architectures and CDN routing constraints, as detailed by Offside.kz.
Consequently, Qazsport’s presentation could be among the most valuable technical sessions. If the new AWS architecture has truly sustained massive real-world loads, the audience needs concrete data: peak concurrent stream counts, end-to-end latency reduction, zero-downtime failover proofs, and architectural changes implemented since legacy outages.
Magnum: Proven Retail Analytics vs. Announced AI Agents
Magnum announced a keynote exploring autonomous AI agents on AWS, presented by Chief ML Officer Yevgeniy Babenko. Prior to the event, verified production descriptions, pilot metrics, or operational KPIs regarding autonomous agents were nowhere to be found.
However, Magnum possesses a substantial, verified enterprise data track record—centered on descriptive and predictive analytics rather than autonomous agents. Between 2022 and 2024, the retail giant transitioned to the Qlik BI analytics platform. According to enterprise integrator Datanomix, decision-making velocity compressed from one month to a single day, daily active analytics users jumped by 30%, and on-shelf product availability surged from 50% to 91%. Earlier algorithmic pilots in assortment personalization delivered revenue growth exceeding 8% across select categories.
Furthermore, Magnum Digital Transformation Director Olga Novitskaya designated multimodal generative AI, warehouse robotics, and supply chain digital twins as strategic imperatives on a three-to-four-year horizon. While the retail network is moving in this direction, the announced case on autonomous agents should be evaluated as an architectural preview rather than an already verified production victory.
Why Enterprise Case Studies Matter for “Digital Qazaqstan”
On June 29, 2026, the President of Kazakhstan approved the national strategy Digital Qazaqstan through 2029. Core objectives encompass expanding sovereign high-performance computing clusters, mass generative AI literacy, and stimulating private technology investments.
Against this regulatory backdrop, enterprise case studies serve as an acid test for national policy. Government declarations set ambitious macro targets, but genuine market adoption begins where a financial institution proves quantifiable labor savings, a broadcaster guarantees crash-free high-concurrency streaming, and a national retailer links machine learning directly to inventory turnover.
Today, ForteBank provides the most convincing evidentiary case. Qazsport must demonstrate that its cloud rebuild permanently resolves the outages viewers experienced firsthand. Magnum must clarify where traditional business intelligence ends and truly autonomous AI agency begins.
Should You Attend?
The conference is primarily calibrated for Chief Technology Officers, enterprise IT architects, public sector digital transformation leaders, and AWS ecosystem partners. While developers and students can gain valuable context, the agenda is structured around enterprise deployment roadmaps and business ROI rather than deep code-level workshops.
For attendees in the audience, the defining question to put to every corporate speaker remains unchanged: what is currently running in live production, how many real users rely on it, and what measurable ROI has the company captured in revenue, operational speed, or service quality?
