AI & Data Product Innovation Portfolio
High-impact, real-world builds that turned fragmented data and manual workflows into revenue, speed, and market advantage.
Transformative AI, data, and SaaS products that moved KPIs—not just code.
From AI-written press releases to sub-second ad-targeting engines, these products share a single through-line: measurable P&L impact. Each engagement combined modern LLMs, streaming data, and repeatable MLOps to slash cycle times, raise conversion, and unlock new ARR—proof that disciplined product strategy plus applied AI equals outsized returns. Explore the case studies below for architectures, KPIs, and play-by-play execution notes.
This page curates the flagship products delivered across multiple companies—each linked to a full case-study child page. Browse the index to jump directly to:
- Evertise AI PR — AI-driven content press-release automation (Read More)
- Intercom AI Triage — NLP based chat intent routing (Read More)
- Gibiru Relevance Engine — privacy search overhaul (Read More)
- 8×8 Revenue Intelligence — cross-stack data science (Read More)
- LavaPM Real-Time DMP — China-compliant audience sync (Read More)
- Sumo Logic Conversion AI — freemium monetization (Read More)
- LinkedIn ML Products — lead/account prioritzation & experimentation (Read More)
Every story details the problem, solution architecture, and quantified impact so you can see how disciplined product strategy plus applied AI drives outsized returns
Evertise AI PR — Accelerating Personalized Media at Machine Speed
A full-stack, AI-driven content automation platform designed to generate personalized, brand-consistent press release (PR) content grounded in company documents — delivered in under 48 hours.
Problem / Opportunity
The global press release and media distribution industry is valued at over $3 billion annually, yet workflows remain dominated by slow, manual agency services. For high-growth startups and enterprises, this friction stifles visibility and slows market response.
Solution
Evertise developed a scalable platform integrating LLAMA 3.1, LangChain orchestration, Vertex AI Pipelines, and Retrieval-Augmented Generation (RAG). The system allowed marketing and comms teams to auto-generate PRs grounded in brand-approved assets, with sub-7s latency.
Impact
- Cut delivery time from 5–7 business days to under 48 hours
- Reduced editorial overhead by 35%
- Onboarded 100+ clients in the first year
- Contributed to an estimated $10M valuation uplift via new recurring revenue line
Strategic Outcome Highlights
- Business Outcome: $10M valuation lift
- Efficiency: 60% improvement in turnaround time
- Architecture: LLAMA 3.1, LangChain, RAG, Vertex AI
Adoption: >10,000 PRs generated, >95% approval without edit
- Cut delivery time from 5–7 business days to under 48 hours
Intercom: AI-Driven Customer Support Triage & Lead Qualification Platform — “Turning millions of chat messages into instant answers and qualified leads”
An enterprise-grade natural-language understanding layer that classifies every inbound Intercom chat in real time and flags hidden sales intent—cutting support response times, lifting CSAT, and surfacing thousands of high-value leads per month.
Problem / Opportunity
Legacy human-only workflows could not keep pace with millions of monthly messages. Critical requests were mis-routed, while promising buyers slipped through unnoticed.
Solution
A full-stack machine-learning platform built around fine-tuned Bidirectional Encoder Representations from Transformers (BERT). The system performs sentiment analysis, entity extraction and intent classification, then routes messages to self-help, bots or humans and scores sales potential—all orchestrated on Google Cloud.
Impact
- +20 % customer-satisfaction (faster, accurate replies)
- +20 % year-over-year revenue in the sales & marketing suite
- 10 % support-team cost reduction via automated triage
- Millions of chats triaged and 200 k+ leads scored every month
Strategic Outcome Highlights
- Business: Material revenue uplift and improved sales-pipeline velocity
- Efficiency: MVP in 3 months; full dual-workflow system in 6 months
- Architecture: Google Cloud Dataflow → TensorFlow-fine-tuned BERT → Vertex AI Prediction; continuous feedback loop for re-training
- Market: Differentiated Intercom as an AI-first engagement platform, boosting enterprise adoption
- +20 % customer-satisfaction (faster, accurate replies)
Gibiru Relevance Engine — Re-architecting private search for precision at scale
A ten-week, ground-up overhaul replaced Gibiru’s brittle, monolithic search stack with a containerised BM25 + BERT relevance engine and real-time feedback loop—driving a 50–60 % lift in top-N precision and a 40 % surge in meaningful engagement.
Problem / Opportunity
Legacy ranking logic, hard-coupled code, and unmanaged infrastructure blocked relevance gains and experimentation, eroding user satisfaction as query volume grew.
Solution
A modular micro-services platform on Google Kubernetes Engine: BM25-powered Elasticsearch for lexical recall, BERT-based semantic re-ranking, and a clickstream-driven feedback pipeline enabling weekly model refresh.
Impact
- Top-N precision ↑ 50–60 %
- Engagement metrics ↑ 40 %
- Experiment cycle time cut from ~30 days to < 7 days
Strategic Outcome Highlights
- Business: Engagement growth positions Gibiru for premium search tiers
- Efficiency: 3× faster iteration with containerised services
- Architecture: Shift to GKE, BM25 + BERT re-ranking, Pub/Sub feedback loop
Market: Demonstrated that privacy-focused search can deliver mainstream-grade relevance without intrusive profiling
- Top-N precision ↑ 50–60 %
8x8: AI-Driven Revenue Optimization & Centralized Data Intelligence — Turning siloed customer data into real-time growth signals
A cross-functional data-science program that united 8×8’s fragmented customer data into a single Redshift warehouse, deployed three predictive revenue models, and streamed insights to ten live dashboards—unlocking a +3 % net-new-revenue uplift and cutting reporting errors by 80 % within nine months.
Problem / Opportunity
Disconnected marketing, sales, support, and finance systems obscured customer health and revenue risk, forcing reactive, quarterly decision-making.
Solution
A centralized data-science function built on Redshift, Airflow, and Python/scikit-learn models for acquisition, churn, and upsell. Outputs flowed to Tableau / Looker dashboards consumed across go-to-market, product, and executive teams.
Impact
- +3 % incremental revenue from proactive upsell & win-back motions
- -80 % reporting errors via automated data-quality checks
- Real-time decision cadence adopted by five major functions
Strategic Outcome Highlights
- Business: Material revenue lift and unified GTM metrics
- Efficiency: Nightly automated ETL replaced quarterly manual reporting
- Architecture Shift: Spreadsheets → Redshift + Airflow + CI/CD ML stack
Market Response: Faster pricing and retention tactics strengthened competitiveness in the crowded UCaaS arena
LavaPM: Real-Time DMP for Programmatic Ad Targeting in China — Localised audience intelligence at sub-second speed
A China-hosted Data-Management Platform on Alibaba Cloud that ingests 250 million device IDs, clusters them into 1 000+ behavioural segments in real time, and syncs audiences to four DSPs in under two seconds—boosting click-through rates by 35 percent while remaining fully compliant with China’s Cybersecurity Law.
Problem / Opportunity
Batch-based DMPs and offshore processing left Chinese advertisers without rapid, residency-compliant audience activation, throttling campaign performance and market share.
Solution
A Spark-powered, Aliyun-native DMP: Hadoop + Impala ETL feeds AnalyticDB; Spark MLlib drives K-Means and ALS models; Apache NiFi provides drag-and-drop campaign design; hashed IDs are pushed to iPinYou via sub-second REST APIs.
Impact
- +35 % CTR across personalised segments
- < 2 s audience-sync latency to four DSPs
- 40 % faster project delivery via bilingual, cross-border sprints
- 100 % compliance with data-residency regulations
Strategic Outcome Highlights
- Business: Higher media ROI attracted tier-1 advertisers
- Efficiency: Elastic autoscaling met Singles’ Day traffic without downtime
- Architecture Shift: Batch ETL → real-time Kafka + Spark streaming stack
Market Response: First-mover edge in compliant, real-time Chinese DMPs
- +35 % CTR across personalised segments
Sumo Logic: AI-Driven Conversion Optimization Platform — Turning freemium exhaust into predictable revenue
A machine-learning layer that scores every Sumo Logic freemium account daily for conversion, churn risk and upsell readiness—raising free-to-paid conversion from 2 % to 5 % and adding AI-backed monetisation credibility ahead of IPO.
Problem / Opportunity
Freemium users generated millions of events but only 2 % converted to paid. GTM teams lacked signals to prioritise upgrades, counter churn, or time expansion offers.
Solution
Spark-based pipelines produce three predictive scores—Conversion Propensity, Churn Risk, Upsell Readiness—using Random Forest and Logistic Regression. Scores refresh nightly and surface in Tableau dashboards for Sales, CS and Marketing.
Impact
- +150 % conversion rate (2 % → 5 %)
- +25 % upsell precision on time-to-upgrade offers
- AI scores adopted in IPO roadshow as monetisation proof-point
Strategic Outcome Highlights
- Business: Direct ARR lift and stronger IPO narrative
- Efficiency: Daily automated scoring replaced manual cohort guesses
- Architecture Shift: Ad-hoc SQL → Spark pipelines + predictive models
Market Response: GTM teams embraced score-driven playbooks
LinkedIn: AI Framework for Sales & Marketing Enablement — “Turning millions of LinkedIn signals into revenue-driving experiments”
A production-grade AI framework that enriches, scores, and experiments on millions of leads weekly—boosting LinkedIn sales revenue by 10 % and cutting product-test insight cycles from weeks to days.
Problem / Opportunity
Soaring user growth left Sales & Marketing teams with more prospects than capacity and no reliable way to test ML ideas. Manual targeting blunted revenue and slowed feature iteration.
Solution
A unified enrichment-and-scoring pipeline (Random-Forest + logistic regression) plus a custom A/B & multivariate experimentation engine. Scores refresh nightly via Hadoop/Hive and surface inside GTM tools; the test layer automates deployment, tracking, and analysis of every ML variant.
Impact
- +10 % sales revenue from precise account prioritisation
- 70 % faster time-to-insight for product decisions
- > 80 % of customer-facing changes shipped behind experiments
Strategic Outcome Highlights
- Business: Revenue lift informed IPO-era GTM strategy
- Efficiency: Nights-to-minutes model refresh; days-to-insight testing
- Architecture Shift: Siloed models → central AI + experimentation framework
Market Response: Sales Navigator adoption accelerated by trusted lead scores
- +10 % sales revenue from precise account prioritisation