Ved Nikolic

AI Product Leader, from research to products people adopt, retain, and engage with

Montreal, Canada

Proven at consumer scale. Drove capture into the leading purchase driver on Meta's AI glasses by turning AI research models into shipped features, with greater than 10x growth in capture usage. Delivered $50M+ incremental revenue and a 400% attach rate lift at Best Buy by rebuilding recommendations on an ML foundation. Scaled a startup to 100% annual growth across six international markets. I work at the intersection of consumer product, AI, wearable hardware, and commerce. The capabilities change; the question doesn't: should this actually ship, and would users come back for it? I sharpen my craft through open source by building evaluation methodology for AI products, a knowledge graph, automated eval loops, and adversarial analysis frameworks.

Meta AI GlassesCamera Systems + UX$50M+ at Best BuyConsumer ScaleConsumer AIRecommendation Systems0-to-1 BuilderMultimodal AIAI EvaluationOpen SourcePython
Get in TouchLinkedInGitHub

Work Experience

I build high impact products at scale, and the question that keeps showing up is simple: will people actually use this again? That has followed me through AI glasses and wearables at Meta, ecommerce at Best Buy, and founder led product work for clients building from zero.

Meta wearable capture work
Ray-Ban Meta Gen 2
Meta
Senior Product Manager
2022 to May 2026

Led media capture across Meta's AI wearable devices, from sensing to sharing across camera systems, media quality, computational photography, media processing, power, thermal, systems health, AI model quality, and companion app software. That work spanned Meta Ray-Ban Display, Ray-Ban Meta Gen 1 and 2, Oakley Meta HSTN, and Oakley Meta Vanguard.

Turned AI research models into shipped capture features and drove capture into the leading purchase driver on Meta's AI glasses, with majority user awareness, retention above program targets, and greater than 10x growth in capture usage. Defined camera media quality strategy, influenced sensor and lens product decisions, drove thermal performance improvements with engineering partners, and built an automated media quality measurement prototype. Partnered with AI teams on ML models from ideation through production handoff, defining the performance requirements and quality metrics each model had to meet. Led the largest feature expansion in capture history ahead of schedule. Moved parts of media processing in house, eliminating an external licensing dependency and improving quality across 7+ device SKUs. Separate capture pipeline work drove sub second latency at >99% success rate.

Most RecentLeading Purchase DriverAI GlassesCamera SystemsSub Second Latency
Best Buy
Senior Product Manager
2020 to 2022

Led product across the online shopping journey, including recommendations, in home services, and in home experience products for millions of customers.

Delivered $50M+ in combined incremental annual revenue across recommendation systems and in home service products. Rebuilt the recommendation architecture on a machine learning foundation, driving a 400% attach rate lift, and expanded in home services online to $30M+ expected annual value.

$50M+ Revenue400% Attach LiftRecommendation SystemsCommerce
IS Inc.
Owner and Product Management Consultant
2018 to 2020

Founded and led a product management consultancy delivering 0 to 1 solutions across fintech and ecommerce for clients in four countries.

Led discovery through launch across 6+ concurrent client engagements as the sole product person, including business case creation, customer journey design, backlog prioritization, operational readiness, and product systems mentoring for teams across three time zones.

Founder0 to 1 Products4 Countries6+ Client Engagements

How I Work

1

Find the real constraint

The product problem is rarely what it looks like. At Meta, the constraint was not AI quality, it was capture latency: sub-second response and >99% success rate before any AI feature mattered. At Best Buy, the constraint was not traffic, it was attach: rebuilding recommendations on ML drove a 400% increase.

2

Define why before how

A prototype shows what is possible. The spec defines what good looks like: which tradeoffs you accept, which you do not, and why. That clarity turned a dozen features into the largest expansion in Meta capture history, shipped ahead of schedule. Extreme clarity on the why and what is what makes the how high quality.

3

Stress-test before building

A/B tested every feature at Meta across wearable hardware, AI models, and companion app software. Ran recommendation experiments driving $9M+ incremental revenue at Best Buy. Built evaluation methodology proven across hundreds of LLM evals. Red-team from 12 disciplinary lenses. The gates that catch bad tradeoffs have to exist before the work starts.

4

Ship across boundaries

$50M+ revenue across Best Buy product lines. 3x scale across 4+ organizations at Meta. 0-to-1 products across 4 countries. 100% annual growth across 6 international markets. The hardest product problems cross team, domain, and geography lines.

Open Source Tools

Tools that solve real problems I hit as a PM. All open source, all LLM-agnostic.

Cortex

PythonSQLite

The memory layer AI coding tools ship without. Routes session context to structured destinations and surfaces cross-project patterns via a knowledge graph.

cortex save "chose connection pooling over per-request"
cortex reflect  # surfaces patterns across all projects

Evalgate

PythonWork in Progress

Evaluation methodology and tooling for AI products. Schema normalization, constraint gates, variance-aware regression detection, and cost/quality tradeoff measurement across models.

Principles from hundreds of LLM evals:
- Atomic evals (one assertion per check)
- Constraint gates (one failure = zero score)
- LLM-as-judge variance: 5-7.5%

PM AutoResearch

PythonLLM-agnostic

Automated eval loop for product documents. Define binary pass/fail criteria, iterate, keep only improvements. Git tracks every round.

Score: 17% --> 94% (4 rounds)
Evals: 19 binary criteria, locked harness

Red-Team

ShellLLM-agnostic

Adversarial analysis from 12 disciplinary lenses. Point it at any product artifact and get severity-ranked findings with grounding and worst-case scenarios.

Agents: Engineering, UXR, PMM, Privacy,
Legal, Ethics, Security, Finance, Data,
Design, Ops, Localization

Steelman

ShellLLM-agnostic

The blue-team to Red-Team's red-team. Takes weaknesses and converts them into positioning advantages through 6 analytical lenses.

Lenses: Strengthen weakest argument,
Reframe positioning, Evidence,
Expand moat, Simplify, Second-order

Stakeholder Radar

ShellLLM-agnostic

Evidence-based stakeholder profiles from real artifacts. Simulates document reviews before they happen.

Input: meeting notes, Slack threads,
email chains, doc comments
Output: per-reviewer predicted feedback

Career

Meta
Montreal, QC
Senior Product Manager2022 to May 2026
  • Turned AI research models into shipped capture features and drove capture into the leading purchase driver on Meta's AI glasses, with majority user awareness, retention above program targets, and greater than 10x growth in capture usage
  • Defined camera media quality strategy, influenced sensor and lens product decisions, drove thermal performance improvements with engineering partners, and built an automated media quality measurement prototype
  • Led the largest feature expansion in capture history ahead of schedule, shipping Ultra HD (3K) video, hyperlapse, slow motion, 60fps, and adjustable stabilization among others
  • Partnered with AI team from model ideation through production handoff, defining performance requirements and quality metrics for ML models
  • Moved selected media processing in house to eliminate an external licensing dependency and improve quality across 7+ device SKUs; separate capture pipeline work drove sub second latency at over 99% success rate
  • Owned privacy and compliance reviews for AI driven features, documenting user journeys and data flows for cross functional sign off
  • Scaled product support 3x (4 engineering managers, 40+ engineers) while shipping largest feature expansion in product history ahead of schedule
Best Buy
Burnaby, BC
Senior Product Manager2020 to 2022
  • Delivered $50M+ combined incremental annual revenue across recommendation systems and in home service products
  • Rebuilt recommendations architecture: 400% increase in attach rates, $2M+ annual revenue
  • Expanded in home services online: $30M+ expected annual value
IS Inc.
Vancouver, BC
Owner and Product Management Consultant2018 to 2020
  • Founded consultancy delivering 0-to-1 solutions across fintech and ecommerce for clients in 4 countries
  • Led discovery through launch across 6+ concurrent client engagements as sole product person
  • Mentored teams across 3 time zones on product systems, data analysis, and market research
Allysian Sciences
Richmond, BC
Director of Product2015 to 2018
  • Drove 100% annual growth across 6 global markets over 3 years
  • Built 3 platforms from scratch: consumer ecommerce, gamified mobile app, custom ERP/CRM
  • Increased subscription retention 60% through loyalty and gamification features
USANA Health Sciences
Toronto, ON
Director of Marketing and Communications2012 to 2015
  • Drove 20% year over year growth in mature markets through integrated marketing strategies
  • Delivered product training and keynotes to audiences of 500+ nationwide
  • Managed public relations, crisis communications, and brand visibility campaigns

Education and Certifications

BCIT
Bachelor of Business Administration (BBA)
Business Administration, Management and Operations
BCIT
Diploma of Technology
International Business / Trade / Commerce
Certifications
PMP (Project Management Institute)CPM (AIPMM)CSPO (Scrum Alliance)