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CASE STUDY · ENTERPRISE SEARCH + ML

Search & Recommendations

Enterprise search platform leadership across multiple brands. 73 projects shipped, 30 production releases, 50+ search optimizations, two major ML implementations — built on Coveo, instrumented with rigorous signal strategy, and operated under control plans that kept experimentation honest. The platform answered millions of queries; the program answered to the people who paid for the answers.

73
Projects delivered
30
Production releases
50+
Search optimizations
2
Major ML implementations
17
React components

How a search program stays trustworthy at scale

Two halves: signal strategy, then control plan. In that order — always.

Signal strategy — measure before you tune
Every roadmap began with measurable relevance goals. Inventory traffic channels, map searcher intent, and instrument before tuning models. Bad signal beats clever models every time.
  • Blend transaction, content, and behavioral signals per vertical
  • Pair qualitative research with log-level analysis
  • Fast route for surfacing zero-result and low-satisfaction patterns
Control plan — kill switches first
Every initiative shipped with explicit kill-switches, A/B coverage, and compliance guardrails. Experimentation is fine; experimentation that quietly damages trust is the failure mode.
  • All models registered with owners, KPIs, and rollback steps
  • Weekly review of bias, cold-start, and data-freshness issues
  • Shared dashboards across product, engineering, merchandising

Core expertise

Four areas that compound — pulling on any one threads the others.

Machine learning & AI
recommendation engines · personalization
Designed and shipped recommendation engines (collaborative filtering + content-based), personalization algorithms, and intelligent content delivery. Measured by conversion lift, not novelty.
Search platform architecture
Coveo · faceting · real-time index
Architected search platforms handling millions of queries — advanced faceting, real-time indexing, query-perf tuning. The platform was the contract; the rest of the org built on it.
Data optimization
pipelines · CTR analytics · monitoring
Pipeline optimization, click-through analytics, and performance monitoring. Insights only matter if they make it into a roadmap; built the loop end-to-end.
Technical leadership
cross-functional · dev + QA + business
Coordinated dev, QA, and business teams across multi-brand initiatives. Translated relevance metrics into dollars; translated dollars back into roadmap priorities.

Featured implementations

Strategic projects with shipped business impact — not lab demos.

Intelligent recommendation systems
ML-powered personalization at scale
Recommendation engines using collaborative filtering and content-based algorithms. Personalized experiences that moved engagement and conversion — measured, not asserted.
  • Frequently Bought Together (FBT) with attribute pre-selection
  • Location-aware recommendation with comprehensive geo data
  • Homepage carousel personalization with safety filtering
  • Cross-platform consistency between web and mobile
Advanced analytics & optimization
data-driven performance enhancement
Analytics frameworks capturing user behavior, search patterns, and conversion funnels. The optimization loop only closes when telemetry reaches the product roadmap.
  • Real-time click tracking + standardized event schema
  • Search-query analytics with perf-optimization insights
  • A/B testing framework for recommendation algorithms
  • Conversion-funnel analysis and bottleneck identification
Enterprise platform development
scalable search infrastructure
Search platform infrastructure for millions of daily queries — server-side rendering, security compliance, multi-environment deployment with automated testing.
  • React search UI with SSR for SEO
  • Multi-environment deploy pipeline with automated tests
  • API security management + key-rotation protocols
  • Enterprise monitoring and health checks
Performance & security optimization
enterprise-grade reliability + compliance
Performance + security improvements that scaled with demand and held the line on compliance. Latency budgets are real; treating them as theoretical was the previous regime's mistake.
  • Query response time reduced ~40%
  • Security vulnerability remediation + platform upgrades
  • Content-safety filtering for compliance requirements
  • Retry logic for data reliability under spike load

Stack & instruments

Where the work actually happened.

Coveo Platform React JavaScript Machine Learning A/B testing Analytics & SEO REST APIs Data pipelines SSR / SEO

What I'd do again — and what changed

Signal first, model second
Tuning a model on bad telemetry is theater. Every win on this program traced back to a clean instrumentation pass we did before touching the algorithm.
Kill switches are non-negotiable
"Roll it back in five minutes" is a real product feature. If you can't, you ship slower — because every change is a bigger bet than it should be.
Bias + cold-start as standing agenda
Weekly review, not quarterly. The minute you only look at relevance metrics, you stop noticing the populations the model fails for.
Now it's agents, then it was algorithms
Same instinct: instrument, gate, observe, audit. The rigor that made search programs trustworthy is the same rigor that makes the current AI-infra portfolio trustworthy.

Plan the next search leap

Need a partner who can combine ML rigor with retail instincts? Or who can carry that discipline into AI-agent infrastructure? Both stories are connected — one substrate, two eras.

Start a conversation → Current AI-infra portfolio React modernization case study