[ AI personalization engine ]
Show Each Visitor the Products and Content They Came For
We build recommendation engines that learn from browsing, purchases and context, rank what each person sees in real time and prove every change with controlled experiments.
Shopper A, trail running
Trail shoes, wide fit
Seen 3 times
Running vest, 5 L
Often bought together
Merino socks
Fits your size
Rain shell
Rain forecast this week
Shopper B, city commute
Waterproof backpack
Matches recent search
Bike lights
Back in stock
Commuter jacket
Popular in your city
Coffee flask
Under your usual price
Example rankings for two shoppers on the same store.
Why One-Size-Fits-All Experiences Underperform
Most catalogs show the same bestsellers to everyone, whatever they came looking for.
- 01
Choice overload
Large catalogs bury the items a visitor would actually buy.
- 02
Rules do not scale
Hand-built merchandising rules cover a few segments and go stale fast.
- 03
New users and new items
Without history, basic recommenders show nothing useful.
- 04
No proof of impact
Teams ship personalization without a clean experiment, so nobody knows if it helped.
How We Build Your Personalization Engine
We start with one high-traffic surface and an experiment plan, then expand.
- 01
Surface and goal
We pick where recommendations appear and what success means: clicks, orders, watch time or retention.
Output: Experiment plan
- 02
Event pipeline
Views, clicks, carts and purchases are captured in real time with product and user features.
Output: Event and feature pipeline
- 03
Model build
Candidate generation and ranking models are trained and checked offline against your current logic.
Output: Offline evaluation
- 04
Experiment and rollout
A controlled test against your current experience, then gradual rollout to more surfaces.
Output: Live engine with test results
What the Engine Does
Built for product, merchandising and growth teams together.
Collaborative filtering
People who behave alike see items similar users engaged with.
Real-time context
Device, location, time and the current session reshape the ranking instantly.
Cold start handling
Content features and popularity priors for new users and new items.
Business rules
Margin, stock and brand rules blended into the ranking where you need them.
Cross-channel
The same profile powers web, app, email and push recommendations.
Experiment framework
Built-in A/B tests with guardrail metrics and clear readouts.
What We Measure With You
Targets are agreed per surface; results come from controlled experiments on your traffic.
- 01
- Click-through
- Engagement on recommended slots
- 02
- Conversion
- Orders or sign-ups from personalized sessions
- 03
- Revenue per visitor
- Measured against the control group
- 04
- Catalog coverage
- Share of items that get recommended
Where It Fits
- 01eCommerce and retailProduct rails, bundles and search ranking.
- 02Media and streamingWatch next, playlists and homepage rows.
- 03SaaS productsFeature suggestions, templates and onboarding paths.
- 04EdTechNext lesson and course recommendations.
- 05Travel and hospitalityDestinations, rooms and add-ons per traveler.
Personalization Stack
Low-latency ranking with an experiment layer on top.
- TensorFlow Recommenders
- PyTorch
- LightFM
- Two-tower retrieval
Every Ranking Change Measured Against a Control
We pick one high-traffic surface, run a clean A/B test against your current logic and only expand what wins.
Book a Personalization Call
Personalization Engine Cost
Indicative starting prices. Cloud usage is billed to your account.
Get a Fixed QuoteRecommendation pilot
One surface with offline evaluation
3 to 4 weeks
from$4,000
Production engine
Real-time ranking, rules and A/B testing
8 to 12 weeks
from$15,000
Cross-channel platform
Web, app, email and push from one profile
4 to 6 months
from$40,000
What Product Teams Say
5.0Clutch
5.0GoodFirms- 5.0Google
- 4.9Upwork
I had the pleasure of working with Sajal Tech on a travel website, and it was an incredible experience from start to finish. They demonstrated professionalism, clear understanding of requirements, and excellent communication throughout.
Knowledgeable, efficient, and ahead of schedule. Will hire again.
They completed our project even when the scope changed slightly. They were cooperative when we wanted to add or modify features.
Personalization Questions
Enough to run a meaningful A/B test on the chosen surface, usually thousands of sessions a week. Smaller sites start with content-based recommendations that need less data.
Yes. Business rules for margin, stock, brands and exclusions sit on top of the model ranking.
We use product attributes, descriptions and images so new items can be recommended from day one.
Rankings are served from a low-latency API and cache, with a fallback list if a call fails.
We respect consent settings, keep data in your cloud account and can work with first-party data only.
Not necessarily. We can personalize your current search results or upgrade search as part of the work.
Let's build your next product together
Book a free strategy call and leave with a clear plan and estimate. No commitment.
- 01Pick a time that suits you
- 0230 minutes on scope, stack, timeline and budget
- 03Fixed-price proposal, NDA on request