
At Menlo Consultants, we partner with teams in complex and regulated environments to design, build, harness, and operate AI systems. We prioritize clear business outcomes, rigorous evaluation, and robust governance with proven results.

Veer Bawa is an engineering lead & researcher with 10+ years in applied AI at Google, Cruise Automation, & VC-backed startups. His specialty is rapidly turning emerging AI capabilities into production-quality systems that drive business value. Career wins include working on novel NLP-powered search signals, & leading the launch of an autonomous vehicles object-tracking stack. He now consults with clients across industries like finance, defense, and healthcare.

Unlock the power of AI with tailored consulting services. Whether you’re starting your first AI project or scaling an existing one, we help you generate and integrate intelligent solutions that propel your business forward.

We design, train, and deploy intelligent models that supercharge your products and processes.

Tech due diligence, AI roadmaps, and everything you need to future-proof your enterprise.

Make sure you appear in agent chat searches with an optimized content strategy and cross-platform visibility tracking.
From ideation to deployment, we turn your concepts into market-ready products. With our end-to-end development services, we help companies design, build, and launch AI-powered solutions that drive results.


Gain the technical expertise your business needs on a flexible basis. Our fractional CTO services provide you with strategic guidance, technical expertise, and hands-on support to navigate complex and ever-evolving technological landscapes.
End-to-end review of your business operations and technology stack designed to help you prioritize where and when to integrate AI into your business.

Challenge: An AV company’s heuristic-based object tracking system produced too many false positives, slowing rollout.Approach: Led a team of 10+ engineers to replace legacy tracking with a deep-learning-based system, including the company’s first graph neural network in production.Result: Delivered a more accurate and reliable perception stack that powered the first-ever fully driverless deployment in a major US city.Takeaway: Moving from rules-based to learning-based perception systems unlocks real-world autonomy at scale.
Challenge: A financial research team struggled with manual equity analysis that took hours per company.Approach: Designed and deployed an LLM-driven system for parsing financial filings and generating structured analysis.Result: Cut research time from hours to seconds, enabling analysts to cover more ground, reduce risk exposure, and deliver higher-frequency insights.Takeaway: LLMs, when paired with eval frameworks, can safely augment high-stakes workflows in finance.
Challenge: A startup needed to prove technical feasibility of a sensor-fusion-based geolocation solution to win funding.Approach: Served as embedded ML lead, architecting and delivering the first sensor-fusion prototype.Result: Prototype secured both investor and government funding and unlocked roadmap to production.Takeaway: The right prototype at the right fidelity can make or break early-stage companies seeking external capital.
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