Enterprise AI Platform
Turning Market Data into Investment Intelligence
Transforms complex market data into clear signals and actionable insights, helping investors make faster, more informed decisions.
Industry
Enterprise Software
My Role
Lead Product Manager
Team Size
12+ cross-functional stakeholders
Duration
2026 - Present
At a Glance
Solution Type
Enterprise AI Platform
Platform / Environment
OpenText Aviator Model Services
Technologies
LLMs, LiteLLM, Kubernetes, OCP, Azure OpenAI, Gemini, REST APIs
User Impacted
Internal Product Teams and Enterprise Customers
1. Background / Context
Enterprise product teams wanted to rapidly adopt generative AI capabilities, but each team faced the same challenges independently. Integrating directly with multiple AI providers resulted in duplicated engineering effort, inconsistent governance, fragmented security practices, and increased operational overhead.
The objective was to establish a centralized platform that abstracted model providers behind a single enterprise service, allowing product teams to consume AI consistently while enabling the organization to evolve models without disrupting applications.
2. Challenge
Multiple engineering teams integrating separately with different AI providers
Lack of standardized model onboarding and governance
Frequent model releases and deprecations requiring application changes
Balancing cloud, private cloud, and on-premises deployment models
Supporting both internal teams and customer-facing applications through a common platform
Maintaining flexibility while enforcing enterprise security standards
3. Solution Design
• Converted complex analytical outputs into user-friendly investment signals.
• Organized intelligence into focused modules rather than one large analytical interface.
• Added context to help users understand what individual signals represented.
• Designed the experience to support informed decision-making rather than automated investment decisions.
• Used customer feedback and observed usage to continuously refine signal presentation and product usability.
Key Finding
The biggest obstacle to AI adoption was not model capability. It was the absence of a standardized, governed platform that simplified enterprise consumption.
Understand Customer Requirements
Standardize Model Planning
Design Secure Model Access
Enable Application Onboarding
Measure Adoption & Iterate
4. Product Decision
Introduced abstract endpoints to minimize application changes during future model upgrades.
Abstract Model Endpoints
Centralized Governance
Standardized model approval, onboarding, and lifecycle management instead of allowing individual product teams to create their own processes.
Deployment Flexibility
Designed the platform to support SaaS, private cloud, and on-premises deployment models from the outset.
Customer-Centric Adoption
Prioritized reducing integration complexity so application teams could focus on delivering AI features rather than managing provider-specific implementations.
5. Outcomes & Impact
1 Platform
Unified AI access layer
Multi-Provider
Standardized model access
Enterprise Ready
Governance-first architecture
Cloud + On-Prem
Flexible deployment support
6. Key Learnings
What Worked Well
✔ Early stakeholder alignment accelerated platform adoption.
✔ Standardization reduced duplicated engineering effort.
✔ Abstracting model providers simplified future migrations.
✔ Designing for governance from day one improved enterprise readiness.
What We Would Do Differently
✔ Introduce customer adoption metrics earlier in the program.
✔ Build self-service onboarding capabilities sooner.
✔ Invest in migration tooling before scaling customer onboarding.
Key Takeaway
Enterprise AI success depends as much on governance, standardization, and developer experience as it does on the underlying language models.
