7/8/25
Scaling Industrial AI Without Losing Platform Control
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A fast-growing industrial AI company engaged [up]start.13 at a critical scaling moment to regain control of a fragmented data platform. By unifying development, restoring governance, and stabilizing delivery, the company reduced costs by 40% and built a foundation designed for secure, scalable growth.
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When Growth Starts to Feel Fragile
From the outside, the company looked exactly like it should.
A fast growing industrial AI firm with active deployments, a modern data platform built on Microsoft Fabric, and growing interest from investors and enterprise customers.
Inside the organization, the picture was less certain.
“On paper the platform looked healthy, but we couldn’t articulate a single, end-to-end system model. Architecture decisions lived in silos, and there was no shared understanding of how changes in one area impacted the rest of the stack.”
VP of Engineering
The platform wasn’t failing. But it wasn’t settling either. Each new feature felt heavier to ship than the last. Timelines slipped quietly. Costs climbed without a clean explanation.
Leadership could feel that growth was starting to stress the system beneath it.
Challenge
As the company prepared to scale, the leadership team faced three compounding risks:
A fragmented, vendor-built codebase with limited end-to-end observability, making it difficult to understand system behavior, diagnose issues, or assess platform health
An infrastructure and deployment model not designed for reliable concurrent usage, creating risk as customer volume and data workloads increased
No unified governance model for security, data privacy, or architectural decision-making, limiting the platform’s readiness for enterprise scale
Shipping faster would have increased fragility. Pausing to refactor everything risked losing market momentum.
The team needed a path that balanced delivery speed, platform stability, and external credibility, without disrupting active deployments.
Approach
Upstart13 led a focused technical takeover designed to stabilize the platform without slowing growth. The engagement prioritized restoring ownership, reducing structural risk, and creating visible momentum for leadership, customers, and investors.
The work was intentionally sequenced to balance stability, speed, and credibility.
What We Built
Sprint Zero: Surface the Hidden Risks
Before making architectural changes, we ran a structured Sprint Zero to establish a shared understanding of the system:
Mapped the existing Microsoft Fabric–based architecture, including data flows, dependencies, and failure points
Identified fragmentation across vendor-owned codebases and delivery pipelines
Assessed scalability, concurrency, and platform readiness for enterprise usage
Evaluated governance gaps across security, privacy, and architectural decision-making
Aligned leadership on a sequenced roadmap to stabilize, scale, and extend the platform
Stabilize the foundations (Phase 1)
We focused first on making the platform reliable under load.
Unified Engineering Model
Consolidated development under a single operating structure with clear ownership, standards, and accountability across teams and vendors.Delivery and Infrastructure Stabilization
Introduced modern DevOps practices to improve deployment reliability, reduce release risk, and increase delivery predictability as usage scaled.Scalability and Compliance Readiness
Re-architected core components to support concurrent usage, multi-tenant scale, and enterprise compliance requirements.
This phase reduced fragility without interrupting active deployments.
Build Observability and Governance (Phase 2)
With the foundation stabilized, the focus shifted to operational discipline.
System-Level Visibility
Improved observability across workflows and environments, enabling leadership to understand platform health, risk, and delivery status.Consolidated Backlog and Roadmap
Unified feature requests, technical debt, and operational work into a single, prioritized backlog aligned to business impact.Governance Frameworks
Established architectural decision rights, delivery standards, and escalation paths so the platform could scale without reintroducing fragmentation.
Deliver Platform Momentum
Stability alone isn’t enough. Progress has to be visible.
Alongside foundational work, we supported targeted platform improvements that reinforced momentum:
Improved reliability and performance across core workflows
Reduced friction in user-facing processes
Enabled advanced analytics capabilities on a trusted data foundation
The result was a platform leadership could confidently stand behind—stable, owned, and still moving forward.
The 90-Day Outcome
Within 90 days, the platform shifted from a source of structural risk to a controlled, scalable foundation.
Before the engagement, development costs were rising without a clear link to progress. Delivery timelines depended on manual coordination across vendors, and leadership had limited visibility into platform health, ownership, and risk. Scaling the platform meant compounding uncertainty.
After consolidating ownership and stabilizing delivery, the platform became predictable, measurable, and defensible.
Key outcomes included:
40%+ reduction in development and operational costs through vendor consolidation, removal of duplicated work, and right sized infrastructure
Predictable delivery timelines enabled by unified ownership and stabilized deployment practices
Clear architectural authority and governance, replacing fragmented decision making across teams
Improved scalability and reliability, supporting concurrent usage and enterprise workloads
A credible roadmap for advanced capabilities, including natural language querying, AI copilots for industrial operations, and trusted analytics
Before, architectural decisions were made locally and optimized for speed. Small changes introduced outsized delivery risk, and advanced capabilities were deferred due to platform fragility.
After, decisions were made at the system level with clear accountability.The platform became an asset leadership could stand behind; stable, owned, and ready to scale.
Why This Worked
This engagement worked because it prioritized platform ownership before optimization.
The underlying technology was viable. The failure point was structural—fragmented decision-making, unclear accountability, and delivery models optimized for short-term speed. Rather than accelerating feature delivery or introducing new tools, the focus was on restoring system-level control.
Timing mattered. The platform was already in production, but scale had not yet magnified its weaknesses beyond recovery. That made it possible to stabilize without interrupting delivery or sacrificing momentum.
Ownership is a scalability prerequisite
Platforms fail when architectural authority is diffuse, not when tools are inadequate.
Stability enables speed
Delivery velocity increased only after deployment, governance, and observability were stabilized.
Visibility reduces risk
System-level observability replaced assumption-driven decision making.
Sequence beats acceleration
Addressing foundational risk early prevents technical debt from compounding under scale.
Governance is not overhead
Clear decision rights and standards are what allow platforms to scale without fragility.
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