Client Challenge

A Colorado-based construction manufacturer produces roof trusses and wall panels for residential and commercial projects, working in both wood and steel. The company priced and estimated contracts using general industry applications and spreadsheets to track material costs for plates, hardware, and raw materials, combined with its team’s experience in labor and build cost. This approach had served the company well for years.

As the company won larger and increasingly complex projects, in part because CAD tools made increasingly complex designs possible, small details introduced outsized cost implications. A skewed hip condition, for example, with a non-standard angle between roof sections in plan view, could cascade through every stage of a build. Capturing that kind of complexity called for a level of precision beyond what spreadsheets and experience could provide.

EIS Visual’s Approach

EIS Visual began with a time and motion study, focused on identifying where meaningful data could be collected non-invasively without changing current production processes. This led to a new timeclock system, built to capture additional data points beyond basic clock-in / clock-out, and to give the company full control over the structure and access of that data. The shop’s automated equipment generated significant operational data including time stamps. This data could show where manufacturing costs were concentrated, both for automated tasks as well as manual steps between automation. No change was initially required to how workers did their jobs.

Next, EIS Visual helped the company connect this shop floor data to its inventory, accounts receivable and payable, accruals, and tax and regulatory systems, along with tools to parse bills of materials and track job costs through each stage of production. The system expanded to include order, quote, and job tracking, billing and payments, and integration with video feeds from the factory floor, connecting the design complexity of each job to its actual cost to produce.

Outcome

The new system enabled the company to exactly correlate design complexity and its actual cost to produce, a powerful differentiator driving profits and growth. They later licensed the platform to other truss manufacturers of similar size and scale, providing a new source of revenue. Eventually the company was acquired with a strong return on investment.

Capabilities Demonstrated

• Time and motion studies to identify high-value, non-invasive data opportunities
• Custom data capture systems built for full structural control
• Integration of shop floor, financial, and inventory systems
• Development of job costing and estimation platforms for manufacturing
• Platform licensing and commercialization strategy

Client Challenge

A Fortune 100 organization managing hundreds of millions of orders and billions of customer service records faced a compounding data quality problem. Discrepancies between orders, service delivery, and provisioning and inventory records were common, and their downstream effects touched nearly every part of the business. Customer service teams spent significant time resolving issues rooted in bad data rather than serving customers. Incorrect service orders led to poor delivery outcomes and customer frustration. And the same underlying data quality issues that created these operational problems also limited the organization’s ability to train reliable AI models on its own data.

The billing engineering team recognized the scale of the problem but faced a familiar challenge: building a system capable of auditing and reconciling data at this scale required substantial investment, and the case for that investment needed to be made clearly to secure funding.

EIS Visual’s Approach

EIS Visual worked with the billing engineering team to identify the opportunity and build the case for funding a large-scale audit and reconciliation engine. Beyond the initial advisory work, EIS Visual remained engaged through the engineering of the system, the deployment of its underlying infrastructure, and its ongoing development and operation, continuing until the system reached a steady operational state.

The resulting architecture was built to ingest and curate data from diverse sources, including multiple relational database systems and enterprise data lakes, into a unified, high-performance data platform. On top of this curated foundation, authorized teams were enabled to build their own analysis, inferencing, and interfaces, supporting a wide range of audit and reconciliation use cases.

Central to the system’s design was a rules engine capable of inferring new rules for complex reconciliation scenarios, rather than relying solely on rules defined manually in advance. To ensure this capability could be trusted at scale, the system was built so that the impact of newly discovered rules could be fully understood through audit before any change was made to live data. Once a rule was approved, changes were rolled out in a deliberate “crawl, walk, run” progression, ensuring that automated reconciliation actions remained safe, predictable, and reversible. Every reconciliation action taken by the system was itself logged with its own audit trail, so that the corrections made to the organization’s data could always be traced and understood.

Outcome

The audit and reconciliation engine gave the organization a scalable, trustworthy way to identify and correct discrepancies across an enormous and complex data landscape, without sacrificing the explainability that operating at this scale demands. By pairing adaptive rule discovery with a deterministic, auditable execution model, the system was able to earn the trust required to act on the organization’s most critical data. The initiative is projected to deliver millions of dollars in savings and new revenue.

Capabilities Demonstrated

• Identification and validation of large-scale data quality opportunities
• Strategic case-building and funding advisory for infrastructure investment
• Architecture and engineering of large-scale data platforms
• Design of deterministic, auditable AI-driven rules engines
• End-to-end delivery from concept through infrastructure deployment to steady-state operation

Client Challenge

An organization had built a critical platform around a flexible data schema based on key-object pairs rather than indexed and normalized data structures. While the approach initially allowed rapid development and adaptability, the architecture became increasingly difficult to manage as the platform grew.

Over time, the lack of clear structure introduced significant operational challenges. The engineering team was unable to geographically distribute data using standard techniques, limiting the system’s ability to scale horizontally. Queries became progressively slower as the dataset expanded, and routine database maintenance required increasing amounts of engineering time.

The architecture also complicated the organization’s operational resilience. Disaster recovery and high availability mechanisms became more difficult to implement and maintain, increasing operational risk.

At the same time, the organization was exploring new initiatives involving AI and advanced analytics. However, the inconsistent structure and semantics of the underlying data limited the reliability of analytical models and made it difficult to generate accurate and predictable outputs.

Engineering leadership recognized that the underlying data architecture had become a strategic constraint. Addressing the issue would require a substantial effort to restructure the data schema and refactor the applications and services that depended on it.

EIS Visual worked with engineering leadership to analyze the architecture of the existing data platform and map the growing network of dependencies across databases, services, and applications.

We developed clear architectural models that illustrated how the original schema design had evolved and how the accumulation of technical complexity was affecting system scalability, operational resilience, and development velocity. Visual representations of the system made it possible to show how limitations in the data model prevented geographic distribution, slowed query performance, and complicated disaster recovery and high availability strategies.

We also developed comparative architectural models that demonstrated how a redesigned data structure, based on indexed and normalized data relationships, would support improved scalability, operational reliability, and the effective use of analytics and AI.

These models enabled engineering leaders to clearly communicate the technical realities of the system and the strategic importance of a comprehensive modernization effort.

Outcome

With a clear understanding of the architectural constraints and long-term implications, leadership aligned around a multi-phase initiative to modernize the platform’s data architecture. The effort included restructuring the schema, refactoring dependent services, and establishing a more scalable and maintainable data foundation.

By making the strategic implications of the underlying technical design visible, EIS Visual helped the organization move forward with confidence on an initiative that improved scalability, strengthened operational resilience, and enabled future innovation.

Capabilities Demonstrated

• Strategic analysis of platform architecture
• Data model and system dependency visualization
• Executive communication of complex technical initiatives
• Platform modernization strategy
• Alignment of engineering priorities with corporate strategy

Client Challenge

Following a merger between two communications operators, the combined organization faced the challenge of integrating multiple Autonomous Networks that had evolved independently.

Each network maintained its own IPv4 and IPv6 addressing structures, with extensive use of private address space including RFC1918 ranges and legacy allocations derived from Department of Defense spaces. These addressing schemes were deeply embedded throughout the infrastructure, assigned to router interfaces, switch ports, firewalls, load balancers, application services, and operational platforms.

The result was a complex and overlapping address architecture that could not easily be unified. Migrating IP blocks and restructuring address allocations would require coordinated changes across thousands of network elements while maintaining uninterrupted service for customers and integration partners.

Engineering teams clearly understood the scale of the problem. However, outside the engineering organization the issue was difficult to grasp. Executives and product leadership saw the initiative primarily as a costly infrastructure project with little visible customer impact. As a result, funding and organizational alignment were difficult to secure.

Meanwhile, the technical debt continued to accumulate. The network became increasingly fragile as routing rule sets grew more complex, maintenance operations required greater effort, and operational changes carried higher risk. Security controls became harder to manage, and the architecture constrained the organization’s ability to integrate systems and innovate across the merged platform.

Without intervention, the underlying technical debt threatened to slow the organization in multiple ways—reducing operational efficiency, increasing risk, and limiting the strategic benefits of the merger.

EIS Visual worked with network engineering and architecture teams to analyze the addressing structures across the merged networks and map the operational dependencies embedded throughout the infrastructure.

We developed architectural models that illustrated how IP address allocations were distributed across the network and how overlapping private address ranges created conflicts between the two organizations. The models also showed how address assignments were tied to routing policies, security controls, service platforms, and integration interfaces.

These visual frameworks revealed how the addressing architecture influenced routing complexity, operational risk, and long-term scalability. By translating the technical realities of the system into clear visual narratives, we enabled engineering leaders to communicate the strategic implications of the problem to executive leadership.

The models also demonstrated how a structured migration and address consolidation strategy could gradually resolve conflicts while maintaining service continuity across the network.

Outcome

With a clearer understanding of the architectural constraints and operational risks, leadership recognized the importance of addressing the issue as part of the broader integration strategy. The organization aligned around a phased approach to rationalize address space across the merged networks while maintaining operational continuity.

The effort reduced routing complexity, strengthened security controls, and created a more stable foundation for future network development. Most importantly, the initiative helped ensure that the combined organization could realize the full operational and strategic benefits of the merger.

Capabilities Demonstrated

• Strategic analysis of network architecture during mergers and integrations
• Visualization of complex addressing and infrastructure dependencies
• Communication of technical debt and infrastructure risk to executive leadership
• Coordination of large-scale network architecture transformation
• Alignment of engineering priorities with corporate strategy

Client Challenge

A technology organization was exploring a major initiative to introduce advanced automation and AI-driven control systems into its network operations environment. The proposed architecture involved collecting large volumes of operational telemetry, building real-time data pipelines, applying machine learning models to detect anomalies and predict system behavior, and implementing automated control loops capable of adjusting network configurations dynamically.

Engineering teams understood the technical opportunity and believed the system could significantly improve operational efficiency, service reliability, and scalability. However, the initiative required substantial investment in new infrastructure, software development, and cross-team integration.

For executive leadership, the challenge was clear: the proposal was technically compelling but difficult to evaluate. The system architecture was complex, the operational changes were far-reaching, and the expected returns were not easily visible without a deeper understanding of how the system would function.

Without a clear representation of the initiative’s mechanics and impact, it was difficult for leadership to confidently prioritize the investment among competing strategic initiatives.

EIS Visual worked with engineering and product teams to analyze the proposed architecture and the operational improvements it could enable. We developed structured visual models illustrating how telemetry data would flow through the system, how AI models would interact with operational platforms, and how automated control loops would influence system behavior.

These models made it possible to clearly explain how the proposed system would reduce incident response times, improve operational efficiency, and support future product capabilities built on more adaptive infrastructure.

By translating complex engineering architecture into accessible visual narratives, EIS Visual enabled leadership teams to see how the initiative would function in practice and how it could create measurable value over time.

Outcome

With a clearer understanding of the architecture and its potential impact, leadership was able to evaluate the initiative within the broader context of the company’s strategic objectives. The organization moved forward with a phased investment strategy that allowed it to build the platform incrementally while validating its benefits at each stage.

The result was a more confident and precise investment decision—one that positioned the organization to strengthen operational performance and support long-term innovation.

Capabilities Demonstrated

• Strategic evaluation of complex technical initiatives
• Visualization of AI-enabled operational architectures
• Executive communication of innovation investment opportunities
• Alignment of engineering proposals with strategic leadership priorities
• Structured frameworks for evaluating large-scale technology investments