Recommend What Matters. Convert More, Retain Longer.

Use behavior, context, and real‑time signals to serve the right product, content, or offer to each user, increasing click‑through, basket size, and lifetime value across channels

Overview

GullyAI's Recommendation Systems turn browsing, purchase, and engagement data into highly relevant suggestions that feel personal, not pushy. We blend collaborative filtering, content and graph models, deep learning, and business rules to optimise for measurable outcomes like click‑through rate, add‑to‑cart, watch time, and repeat purchase. With guardrails, explainability, and strong MLOps, we deliver recommendations you can trust at scale for e‑commerce, OTT, EdTech, SaaS, and retail.

Overview

Higher Conversions

Serve relevant items in search, PDPs, carousels, and email so users discover value faster, improving click‑through and add‑to‑cart while reducing friction in critical decision moments

Bigger Baskets & AOV

Use complementary and cross‑sell logic to suggest bundles, accessories, or upgrades that fit user context, increasing average order value without resorting to steep discounting strategies

Better Retention & LTV

Keep users engaged with timely, taste‑aware picks that adapt as interests evolve, driving repeat visits, subscription endurance, and higher lifetime value across segments and cohorts

Personalised Journeys

Orchestrate one‑to‑one paths across web, app, email, and push, aligning recommendations with intent, inventory, and margin rules so every touchpoint feels smart, useful, and consistent

Faster Merchandising Decisions

Give teams explainable signals on what resonates by audience and channel, guiding promotions, catalog curation, and content placement with confidence backed by live performance data

Features

Hybrid Recommendation Engines

Combine collaborative filtering, content‑based models, graph relationships, and deep learning to capture taste, context, and novelty while avoiding filter bubbles and cold‑start pitfalls

Context & Real‑Time Signals

Ingest session behavior, location, seasonality, price sensitivity, and inventory to update suggestions instantly, keeping recommendations aligned with user intent and commercial goals

Cold‑Start & New‑Item Handling

Use item metadata, embeddings, and popularity priors to recommend new products or content immediately, limiting dead ends and accelerating discovery for fresh catalog additions

Business Rules & Guardrails

Enforce constraints for margins, stock, compliance, and brand safety, ensuring recommendations are profitable, permissible, and aligned to merchandising and policy requirements

Explainability & Controls

Provide interpretable reasons, similarity insights, and override tools so marketers fine‑tune carousels, troubleshoot edge cases, and maintain trust with stakeholders and end users

MLOps & A/B Testing

Ship with CI/CD, feature stores, monitoring, and experiment frameworks to compare models, detect drift, and continuously lift KPIs with evidence rather than assumptions or guesswork.

Use Cases

E‑commerce

“Frequently bought together,” “You may also like,” and onsite search personalisation that increases discovery, improves conversion, and raises average order values at scale reliably

OTT / Streaming

Taste‑aware rows and continuation picks that reduce choice paralysis, increase session starts, and extend watch time by sequencing content users are statistically likely to finish and enjoy

EdTech

Adaptive lesson, quis, and resource suggestions that respect learner pace and gaps, improving completion rates while guiding instructors to content that drives measurable learning outcomes

SaaS

In‑app guidance, template picks, and feature surfacing based on role and behavior, accelerating time‑to‑value, boosting activation, and nudging expansion into higher‑impact workflows predictably

Retail (Omnichannel)

Store and digital signals unify to power clienteling and localised offers, ensuring associates and apps present relevant items that convert without eroding margin or inventory balance

Our Process

Discover & Align

Define commercial objectives, guardrails, and success metrics; map key placements and journeys so recommendations optimise the moments that matter for growth and profitability

Data & Features

Integrate clickstream, catalog, pricing, and inventory; engineer cross‑channel features and embeddings that capture similarity, co‑occurrence, and seasonality with high signal quality

Modeling & Validation

Train hybrid models and tune for precision, recall, diversity, and coverage; back‑test against baselines and run offline metrics correlated with real business outcomes credibly

Pilot & A/B Test

Launch controlled experiments in selected placements; measure uplift on CTR, add‑to‑cart, watch time, or activation while monitoring margin impact and operational constraints carefully

Deploy & Monitor

Productionise with MLOps, feature stores, and observability; track drift, latency, and bias, and automate retraining so performance remains steady as behavior and catalogs change

Scale & Govern

Extend to new channels, audiences, and geographies; codify playbooks, override policies, and compliance checks to ensure sustainable results with transparent accountability.

Why Choose GullyAI

Outcome‑First Design

We anchor models to revenue, retention, and margin goals, ensuring lifts are real, repeatable, and visible in dashboards executives trust for ongoing investment decisions

Domain Depth

Experience across e‑commerce, OTT, EdTech, SaaS, and retail helps us preempt pitfalls, select placements wisely, and tune incentives so algorithms complement brand and category strategy

Responsible Personalisation

Built‑in guardrails, diversity constraints, and bias checks protect users and brands, preventing echo chambers while honoring policy, safety, and regulatory requirements

Operational Fit

APIs, batch jobs, and plug‑ins integrate with your CMS, search, CDP, ESP, and apps, enabling teams to launch and iterate without disruptive replatforming or lengthy migrations

Continuous Improvement

A/B testing, multi‑armed bandits, and monitoring pipelines let us adapt quickly, retire under‑performers, and compound gains with disciplined experimentation at sensible cadence.

Frequently Asked Questions

With clean data and defined placements, pilots often launch in a few sprints; we target quick wins while setting up MLOps and tests that support safe, confident scaling afterward.

No; we encode margin, stock, and price rules so suggestions balance conversion and profitability, avoiding discount dependence and protecting gross contribution reliably.

We use metadata, embeddings, and cohort priors to recommend from first sessions, then adapt rapidly as we collect behavior, minimising drop-off and dead ends effectively.

Yes, marketers get explainability, pinning, exclusions, and inventory-aware boosts, so they can align results with campaigns, compliance, and brand presentation standards.

We track CTR, add-to-cart, AOV, watch time, activation, and retention, plus diversity and coverage, correlating metrics with revenue and margin to confirm genuine impact.

Ready to personalise every touchpoint without guesswork?

Let's deploy recommendations that lift conversion, grow loyalty, and protect margin from day one.

Book a Free Consultation