01 // Who We Help
We help mature teams adopt AI with the strategy, systems, and engineering discipline required to make it stick.
We work with Series A+ startups and large enterprises that already see the opportunity in AI but need a clearer path from ambition to production.
Most teams do not need another generic workshop. They need a partner who can help them choose where AI creates leverage, build the right systems, and raise the internal capability to own them.
This is usually where model choice, evals, agentic workflows, legacy architecture, and business adoption start colliding.
Team background: Google DeepMind, Meta FAIR, and AWS. We pair research depth (2,500+ citations) with operator experience as exited founders (acquired 2025).
02 // How We Help
Strategy
AI Adoption Roadmap
We help leadership teams decide where AI creates real leverage, what the operating model should look like, and how to move from scattered pilots to an AI-native roadmap.
Result: a prioritized plan tied to business value, operating constraints, and adoption risk.
Systems
Applied AI Delivery
We design and ship the hard parts: evals, model selection, fine-tuning decisions, workflow architecture, guardrails, monitoring, and production integration with legacy systems.
Result: AI systems that are reliable enough to trust and practical enough to scale.
Uplift
Capability Transfer
We work alongside internal teams so they can own the stack: experimentation loops, engineering standards, deployment patterns, and the judgment to know when not to fine-tune.
Result: stronger internal capability instead of permanent vendor dependence.
03 // Partners
04 // What The Experts Say
Suhas Pai
CTO, Hudson Labs / O'Reilly author
“The Bay Labs team has worked with me across a variety of projects, including at Hudson Labs. What sets the Bay Labs team apart is their holistic approach to problem solving: they combine strong business acumen with the technical depth needed to translate real-world needs into practical, effective solutions. They also stay closely attuned to state-of-the-art AI developments, which allows them to consistently bring fresh, relevant thinking to the work they do.”
05 // Track Record
06 // Research
Latest research pieces
Memory for Long-Running Agents: What Actually Works
We stay close to the frontier by doing the research ourselves. The research page collects our notes on fundamental machine learning and the practical systems questions that determine whether AI creates leverage in real organizations. We also study the current research landscape through our own experiments to understand where the field stands in pursuit of AGI, separate from marketing hyperbole and hype.
Latest: A practical guide to memory for long-running agents: what breaks, what architecture to use, and what research is converging towards.
Read latest pieceAI adoption breaks when strategy, systems, and team capability move at different speeds. We work across all three so organizations can compound progress instead of restarting every quarter.
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