A Global Chemicals Company
Industry
Chemicals
Year
2026
Stack
Databricks, Azure, Azure OpenAI
⸻ Business Impact
Faster Time-to-Production, Reduced Friction, and a Scalable Path from GenAI PoC to Enterprise-Grade Solutions
Our customer works in a global enterprise setting, where multiple teams were tasked with developing AI use cases, but delivery was slow.
Projects stalled because teams got stuck on technical depth. Specifically, this means teams had to spend time identifying which technologies to use, applying internal governance, meeting industry best-practice standards, and staying in line with security requirements within their specific environment. As a result, initiatives often took months to reach production, and even then, solutions were frequently not fully aligned with industry and organizational standards.
MobiLab addressed this by closing a critical gap: lack of relevant, organization-specific guidance for building AI. MobiLab built a practical foundation that helps teams move faster, grounded in real implementations, aligned with existing governance, and continuously updated with new technology.
This changed how AI projects were executed:
Up to 50% shorter timelines from idea to production
Faster and smoother compliance and approval processes
Reduced time spent evaluating technologies and approaches
Lowered development and operations costs, and increased reusability across teams
More consistent, production-ready AI delivery
⸻ STARTING POINT
Fragmented Knowledge, Slow Delivery, and High Uncertainty in AI Development
As AI adoption increased, teams across the organization began exploring use cases in parallel. While building proofs of concept was fast, delivering enterprise-grade solutions was slow and inconsistent.
The core issue was a lack of clarity and relevance when researching and building AI products, as well as keeping up with a fast-changing AI landscape:
- Teams struggled to determine which architectures, technologies, and models were appropriate
- Knowledge was fragmented across projects, individuals, and external sources
- Governance and compliance requirements were difficult to interpret in practice
- Each initiative required re-evaluating decisions that had already been made elsewhere
This resulted in long lead times, duplicated effort, and uncertainty around what the product should look like in the organization’s specific context.
⸻ SETTING THE FOUNDATION
From Scattered Knowledge to a Practical, Usable AI Engineering System
MobiLab worked closely with product, engineering, architecture, and governance stakeholders to understand how AI use cases were actually being built.
The core issue was that relevant knowledge was:
- scattered across teams
- buried in past projects
- disconnected from governance
- outdated due to rapid technology changes
To address this, MobiLab introduced a knowledge-driven approach.
Instead of optimizing individual use cases, the focus was on creating a shared, practical foundation that reflects how AI should be built in this specific environment in a rapidly changing AI landscape. This meant we set out to strike the right balance between standardization and flexibility.
We aimed to establish reusable patterns, governance-aligned components, and a consistent baseline, while preserving the ability to explore and adapt as technologies evolve. The foundation follows a modular architecture that applies established standards where they add value, while leaving room to adopt new technologies where needed.
At the center of this is an enterprise-specific AI knowledge hub, a structured, continuously evolving system that consolidates:
- architecture patterns that work in practice
- existing governance and compliance requirements
- reusable implementation approaches
- learnings from real pilots and product cases
- updates aligned with the latest AI technology trends
This ensures that teams are not navigating AI development blindly, but starting from relevant, validated, and up-to-date guidance.
⸻ SOLUTION
A Knowledge-Driven Foundation for Faster, Governed AI Delivery
MobiLab designed and implemented a foundation that enables teams to build AI solutions faster providing genuinely reusable assets: customer-specific guidance on how to apply tools in their specific environment, alongside reusable codebases that teams can actually build on.
We gathered the information not from isolated frameworks. We worked with internal teams on real use cases and extracted the reusable components, information, and know-how. From there, we continuously populated the knowledge hub — updating existing assets and generating new ones as new insights emerged and technology evolves.
This way, we ensure all the knowledge is highly relevant and tested.
Now, instead of each project independently solving the same problems, teams can now:
- start from proven architecture patterns
- work within pre-aligned governance frameworks
- leverage validated approaches from prior implementations
- reduce time spent on technical depth
Critically, governance is no longer a late-stage blocker. It is integrated into the foundation, making compliance part of the development process rather than an obstacle.
From Exploration to Execution
With this foundation in place:
1. Teams no longer start from zero when building AI use cases
2. Technology decisions are guided by prior experience, not trial and error
3. Governance requirements are easier to navigate and apply
4. Knowledge shifts from individuals and isolated projects into a shared system
5. AI delivery becomes faster, more predictable, and more scalable
This foundation was intentionally not designed as a full management static platform. While AI is evolving quickly for the foreseeable future, locking everything into fixed structures would reduce the ability to respond to new technologies and emerging needs. Instead, our approach was to create a modular, continuously evolving foundation that standardizes what should be standardized, while keeping enough flexibility to adapt where required.
It addresses one of the most important barriers to enterprise AI scale, the lack of relevant, reusable, and trusted knowledge that teams can act on.
Stop reinventing the wheel on every AI project.
Let’s build the foundation that gets your teams to production faster.
Let's go forward. Together.
Every business is unique. Let us show you how we can accelerate your AI journey. Get in touch with us today!