One-to-one experiences at one-to-million scale.
Recommendation systems, personalised content generation, behaviour insights and dynamic messaging that lift conversion, order value and retention.
What is a personalization engine?
A personalization engine uses behavioural data to decide what each user should see next: products, content, offers or messages. Eazyware builds recommendation and ranking systems, personalised content generation and next-best-action triggers on your event data, and ships every deployment with an A/B experiment so the lift in conversion and retention is measured, not assumed.
| Service line | AI-Powered Product Engineering |
|---|---|
| Engagement | Scoped build with milestones |
| Duration | Quoted after scoping; typically 8–16 weeks |
| Starting price | $21,000 |
| Typical range | $21,000 – $70,000 |
| Deliverables | 4 listed below |
| Delivered from | Bengaluru, India (IST, UK and US East hours) |
| Code ownership | Client owns code, infrastructure, prompts and documentation |
What problem does it solve?
Every user sees the same homepage, the same email, the same upsell. The data to do better is already in your events. Nobody has turned it into a model.
How do we approach it?
Personalisation starts with identity and events: we consolidate what your storefront, app, email and messaging tools know about a customer into one event pipeline with a stable identity, because without that nothing else works. Models are hybrid from the start, collaborative signals where history exists, content and popularity fallbacks where it does not. A real-time ranking service and a feature store make the experience respond within a session. Every change ships as an A/B test with a held-out control, one module at a time, so the lift is measured on your numbers and can be believed.
What do clients use it for?
- Product and content recommendations
- Personalised email and push messaging
- Search and feed ranking
- Next-best-offer for fintech and telecom
- Onboarding paths in SaaS
Is it the right fit?
Good fit when
- E-commerce, media, education and SaaS with event data
- Businesses with a catalogue and repeat users
- Teams ready to run experiments
Probably not when
- Sites without event tracking
- Catalogues too small to personalise
What do we build?
- Recommendation systems: collaborative, content-based, hybrid, session-based
- Real-time ranking for feeds, search and product listings
- Personalised content and messaging per segment or individual
- Next-best-action and churn-intervention triggers
- Customer behaviour insights and segmentation
- Experimentation framework to prove lift
What you get
- Recommendation and ranking service
- Event pipeline
- Messaging integration
- Experimentation dashboard
How does the engagement work?
- 01
Event and catalogue audit
- 02
Baseline model
- 03
Offline eval
- 04
Online A/B
- 05
Scale
- 06
Iterate
What does good look like?
Each user seeing a homepage, feed, message or offer chosen for them, with the effect on conversion, order value and retention shown as test-versus-control on your dashboard. Coverage that includes new users and new products through fallbacks. An experimentation framework your marketing team runs without engineers. And a system that keeps its control group long enough to confirm the effect holds through a sale season.
How does it compare?
| Eazyware | Typical agency | In-house hire | |
|---|---|---|---|
| Time to first result | Sprint Zero in 10 days, then a fixed-scope build | 6–12 weeks of discovery before a proposal | 3–6 months to hire, then ramp |
| Pricing model | Fixed scope, milestone billing, INR or USD | Time and materials, open-ended | Salaries, tooling, management overhead |
| AI depth | Multi-model, evals, cost routing, observability as standard | Often a single vendor API and a prompt | Depends entirely on who you can hire |
| Ownership | Client owns code, infra, prompts and docs | Sometimes retained or licensed back | Owned, but concentrated in one or two people |
| After launch | Care Plans with SLA and AI add-on | Change requests at hourly rates | Ongoing headcount whether or not there is work |
Which pitfalls do we design around?
Personalisation fails without clean events and identity, when cold start is ignored and new users see an empty experience, when lift is claimed from a before-and-after comparison rather than a controlled test, and when the model is deployed once and never retrained. We fix identity first, design fallbacks, insist on controls and schedule retraining with drift alerts.
What do we measure?
Every engagement is instrumented. These are the numbers you see in the dashboard and the monthly report, not claims on a website.
- Conversion and order value lift in A/B tests
- Retention and engagement change
- Coverage and cold-start performance
Which technologies do we use?
- Event pipeline
- Python ML
- Redis feature store
- BigQuery / Postgres
- LLMs for content
- Your CRM / ESP
Who does the work?
A data engineer for the event pipeline, an ML engineer for recommendation and ranking, and a full-stack engineer for the storefront modules and experimentation dashboard.
What do you need to bring?
Access to your storefront, app and messaging platform events, the product catalogue, an owner for the marketing calendar, and agreement to hold out a control group. Where events are missing, we add tracking in the first sprint.
Frequently asked questions
E-commerce only?
No. Media, education, SaaS onboarding and fintech offers too.
Cold start?
Content-based and popularity fallbacks, then learn.
Proof?
Every deployment ships with an A/B test. You see the lift.
Where does this fit?
Personalization Engines is part of our AI-Powered Product Engineering line. Not sure yet? Start with Sprint Zero, a ten-day discovery whose fee is credited to this build. See all pricing or talk to an engineer.