phased migration strategy with zero-downtime approach, validation checkpoints at every stage, and rollback plans if issues emerge
KMS Technology acquires Addepto. Read the Full Press Release
KMS Technology acquires Addepto. Read the Full Press Release
Turn Batch Delays into Real-Time Decisions: In just 4 weeks, build and validate a production-grade, real-time data pipeline through a four-week Proof of Concept.
Demonstrate technical feasibility, quantify latency improvements, and prove the business value of streaming data—before committing to full-scale implementation
Most enterprise data systems still rely on batch updates that run hourly or overnight. While this model worked when reporting was the primary objective, today’s operations require up-to-date data to drive immediate action—not just retrospective analysis. When data arrives late, decisions follow the same pattern, leading to missed revenue opportunities, slower responses to customer behavior, delayed fraud detection, and limited operational visibility—all while competitors act in real time.
Although moving to streaming appears to be the answer, traditional transformation programs are often long, costly, and uncertain—taking 18–24 months with significant upfront investment and no guaranteed business impact.
This streaming data pipeline solution changes that dynamic by enabling organizations to validate real-time capabilities, measure results, and prove business value in just weeks—before committing to full-scale transformation.
The Impact of Real-time Data:
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.
Achieve a production-ready real-time pipeline for a high-priority use case, with validated feasibility and measurable business impact. The result is clear, data-driven evidence of the value of real-time capabilities—proven in your own environment using live data, before committing to full-scale implementation. |
phased migration strategy with zero-downtime approach, validation checkpoints at every stage, and rollback plans if issues emerge
complete discovery of data volumes, workload patterns, dependencies, and transformation logic reveals exactly what you’re migrating
time-sensitive roadmap prioritizes workloads by business value and technical risk, ready to hand off to implementation teams
TCO modeling compares legacy costs to cloud economics across a realistic 3-year horizon, turning migration into an ROI conversation
Snowflake, Databricks, or BigQuery evaluated against your requirements (not vendor marketing) with technical rationale backing the recommendation
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.
Achieve a production-ready real-time pipeline for a high-priority use case, with validated feasibility and measurable business impact.
The results for the data engineering services are clear, data-driven evidence of the value of real-time capabilities—proven in your own environment using live data, before committing to full-scale implementation.
A production-ready system that processes live business data—ready to demonstrate value to stakeholders or serve as a foundation for full-scale rollout.
A clear technical design showing how the solution works, how it integrates with the current setup, and how it can scale over time.
Quantified outcomes based on the selected use case—such as improved fraud detection, higher conversion, better inventory accuracy, or operational savings.
A comparison of the new pipeline versus current batch processes, showing how much faster data can move through the system
Documentation of selected tools and practical guidance for connecting the pipeline to existing systems.
Live visibility into pipeline performance, reliability, and processing speed.
A plan for expanding from PoC to production, including priorities, timelines, and estimated effort.
Beyond deliverables, this engagement transforms how your organization approaches data infrastructure—replacing fear of migration with execution confidence and strategic clarity.
Validate streaming pipelines on your actual data, infrastructure, and scale—ensuring feasibility and performance in the conditions that matter most to your business.
Demonstrate the shift from batch to real time—reducing delays from hours or days to seconds—while quantifying outcomes like fraud prevention, conversion gains, and operational efficiency.
Build confidence through evidence-based ROI and a defined roadmap—enabling a smooth transition from Proof of Concept to a fully operational enterprise streaming platform.
Act on data as it happens—empowering faster decisions, improved customer experiences, and the ability to respond instantly to changing business conditions.
Gain continuous insight into business operations as events happen—enabling faster interventions, improved SLA adherence, and more agile decision-making across teams.
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.
covering infrastructure, data, AI/ML tooling, integration, and governance.
designed to align with industry requirements, scale expectations, and long-term AI ambitions
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.
focused on support for generative AI, real-time inference, vector databases, and feature stores
Platforms are designed to be usable and sustainable—supporting self-service, clear ownership, and efficient workflows for data, engineering, and analytics teams.
including right-sizing opportunities, license rationalization, and structural inefficiencies
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.
grounded in the existing technology stack, organizational capabilities, and strategic business objectives.
The streaming data pipeline PoC validates feasibility and quantifies business value—creating the foundation for broader real-time capabilities.
Organizations typically expand through:
Stop debating whether real-time pipeline is worth it.
Build it, measure it, prove it—before committing to full transformation.
