covering infrastructure, data, AI/ML tooling, integration, and governance.
KMS Technology acquires Addepto. Read the Full Press Release
KMS Technology acquires Addepto. Read the Full Press Release
Build an AI-Ready Foundation to Scale: A focused, three-week engagement that evaluates your data platform, integration ecosystem, and overall architecture—identifying cost inefficiencies, complexity gaps, and AI readiness blockers—then delivering a clear, actionable modernization roadmap.
Most data platforms grow without a clear plan. Over time, short-term fixes pile up into complex systems built on aging pipelines. When new demands such as real-time analytics or generative AI emerge, they are layered onto infrastructure that was never designed to support them—widening the gap between business ambition and platform capability.
As complexity grows, data teams are forced to focus on maintaining and troubleshooting systems rather than delivering innovation. Meanwhile, AI initiatives stall because the underlying data is not fast, reliable, or well-governed enough for production use.
The Hidden Cost of Platform Drift:
In a focused three-week engagement, KMS Technology and Addepto work alongside domain experts to evaluate the full data and AI platform landscape.
The assessment spans cloud infrastructure, data warehouses and lakehouses such as Snowflake, Databricks, and BigQuery, as well as integration layers, governance frameworks, and ML infrastructure.
The objective is to identify cost inefficiencies, architectural constraints, and capability gaps that limit AI adoption—then translate those findings into a clear, actionable modernization strategy.
Within three weeks, you will get:
The outcome is a prioritized modernization roadmap grounded in the existing technology stack, organizational capabilities, and strategic business objectives.
In a focused three-week engagement, KMS Technology and Addepto work alongside domain experts to evaluate the full data and AI platform landscape.
The assessment spans cloud infrastructure, data warehouses and lakehouses such as Snowflake, Databricks, and BigQuery, as well as integration layers, governance frameworks, and ML infrastructure.
The objective is to identify cost inefficiencies, architectural constraints, and capability gaps that limit AI adoption—then translate those findings into a clear, actionable modernization strategy.
Within three weeks, you will get:
covering infrastructure, data, AI/ML tooling, integration, and governance.
designed to align with industry requirements, scale expectations, and long-term AI ambitions
focused on support for generative AI, real-time inference, vector databases, and feature stores
including right-sizing opportunities, license rationalization, and structural inefficiencies
grounded in the existing technology stack, organizational capabilities, and strategic business objectives.
The work focuses on concrete questions: where money is being wasted, where architecture limits scale or reliability, and whether the platform can support production-grade AI use cases. The outcome is not a theoretical framework, but a set of clear, defensible recommendations tied to cost, risk, and execution capacity.
The structure is consistent, but the conclusions are shaped by the platform’s real constraints, workloads, and business goals.
The result is a clear view of what the current platform enables, what it blocks, and what must change to support sustainable growth and AI execution.
By the end of the engagement, you won’t just have a concept or a prototype—you’ll have a working AI agent in production, plus everything needed to prove value and prepare for scale.
A fact-based view of the current data and AI platform that explains what exists and why—including overlapping capabilities, structural gaps, and components that increase cost or complexity without clear business return. This output supports informed build-vs-buy and consolidation decisions.
A clear visual and narrative explanation of how the platform operates in practice, showing data flows, system dependencies, ownership boundaries, and failure points that affect reliability, scalability, and delivery speed.
A quantified summary of where platform spend is concentrated and where it is inefficient. Highlights the architectural and operational choices driving excess cost.
A direct answer to whether the current platform can support production AI workloads. Identifies the specific data, infrastructure, and operational constraints that prevent AI initiatives from moving beyond experimentation.
A concise view of governance maturity and exposure, covering access control, data visibility, lineage, and compliance risks that could slow execution or increase operational and regulatory impact.
A clear description of the intended future platform state—what capabilities are required, what should be simplified or removed, and how the platform must evolve to support analytics and AI at scale. This defines direction, not execution detail.
A sequenced plan that translates the target platform direction into concrete actions. Initiatives are prioritized by impact, effort, cost, and risk, distinguishing immediate remediation from foundational and strategic changes.
We combine deep enterprise software delivery experience with hands-on data and AI expertise. The result is a consulting approach focused on turning platform assessments into real, executable modernization outcomes.
KMS Technology and Addepto combine deep enterprise software delivery experience with hands-on data and AI expertise. The result is a consulting approach focused on turning platform assessments into real, executable modernization outcomes.
Platform guidance accounts for AI requirements from the start—data availability, operational stability, and readiness for production inference—rather than treating AI as a future add-on.
Platforms are designed to be usable and sustainable—supporting self-service, clear ownership, and efficient workflows for data, engineering, and analytics teams.
KMS contributes proven software engineering and cloud delivery at enterprise scale. Addepto brings deep specialization in data platforms, analytics, and AI. Together, platforms are evaluated not just for architectural soundness, but for their ability to reliably support production workloads and AI initiatives.
The same teams that assess the platform are capable of modernizing it. This removes handoff risk, shortens time to value, and ensures recommendations are grounded in what can actually be built, operated, and scaled.
Experience across regulated and high-growth industries ensures that compliance, data governance, and performance are addressed without slowing execution or innovation.
Every recommendation is tied to measurable outcomes: lower operating costs, improved reliability, faster delivery of analytics and AI use cases, and reduced operational risk.
The work is led by senior engineers and architects who have designed, optimized, and run data and AI platforms in production. Recommendations reflect real-world constraints—cost, reliability, security, and team capacity—not theoretical best practices.
The Data Platform Assessment establishes a clear baseline for cost control, AI readiness, and long-term modernization. From this foundation, organizations typically move forward in one or more focused directions—based on business priorities, platform maturity, and delivery urgency.
Identify where costs and complexity originate, uncover practical optimization opportunities, and define clear next steps toward a more efficient, AI-ready platform.
