Product Design Workshops
Hands-on sessions to accelerate product development, validate concepts, and build design-driven cultures within growing organizations.
Top of Mind Labs
Top of Mind Labs helps the world's most ambitious enterprises move from AI ideas to real operational impact—fast. We design and deploy AI-native systems that drive measurable business outcomes within months, not years.
Services
Building AI Use Cases
Strategic identification and implementation of AI applications that drive measurable business value and competitive advantage.
Data Engineering Agents
Automated data pipelines and intelligent systems that scale data infrastructure and deliver real-time insights at enterprise scale.
Agentic SRE Agents
AI-powered reliability engineering that automates incident response, optimizes system performance, and reduces operational toil.
Focus
We partner with portfolio companies of private equity firms to unlock value through AI-driven transformation. We help growing companies leverage cutting-edge technology and intelligent systems to achieve measurable outcomes, increase market share, and build sustainable competitive advantages.
Our Process
💡 Step 1: Opportunity & Readiness Alignment
The Matchmaking — Find the right problem for the right team.
The company presents a portfolio of 3-5 high-value business pain points with detailed data readiness assessments. We present research strengths and capabilities. We match problems to solutions where data is available and clean, producing a scoped project charter with clear success metrics.
🔬 Step 2: Collaborative Research & Solution Design
The Prototype — Develop a Minimum Viable Model (MVM) that achieves target performance.
Researchers develop and test novel AI models while company domain experts provide ground truth data and validate assumptions. Together we create a validated model artifact and Model Card detailing performance, biases, and ethical risks.
🏗️ Step 3: Engineering Handoff & ML System Design
The Translation — Shift from research to production-ready systems.
MLEs design full-stack infrastructure including data pipelines, model serving, and monitoring. Researchers work side-by-side ensuring the production environment replicates the research environment. Compliance checks ensure regulatory requirements are met before deployment.
🧪 Step 4: Shadow Testing and A/B Evaluation
The Validation — Prove business value in low-risk environments.
Models run in shadow mode alongside existing logic, then undergo A/B testing with real users. Business owners confirm that defined success metrics have been met or exceeded before moving to full production.
🚀 Step 5: Production Deployment & MLOps
The Launch — Full rollout with comprehensive monitoring and reliability.
100% rollout with MLOps implementation including drift detection, performance monitoring, and automated feedback mechanisms. This ensures models stay accurate and deliver continuous business value.
🔄 Step 6: Knowledge Transfer & Iteration
The Future — Leverage shared learning for the next project.
Documentation, IP assignment, and lessons learned create a knowledge base for future projects. The process circles back to Step 1, using new operational data and insights to identify the next high-value opportunity, ensuring long-term strategic advantage.
Let's build something together
Reach out to discuss strategy, transformation, or an exploratory workshop.
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