---
title: "Recommendation System Development | India-Based AI Team"
description: "Personalized recommendation and ranking systems built by an India team for US product, media, and commerce companies. Measured on real conversion, A/B tested."
image: "https://foundrysoft.co/api/og?type=page&title=Recommendation+System+Development+%7C+India-Based+AI+Team&st=Personalized+recommendation+and+ranking+systems+built+by+an+India+team+for+US+product%2C+media%2C+and+commerce+companies.+Measured+on+real+conv%E2%80%A6"
url: "https://foundrysoft.co/services/ai-recommendation-system-development-india"
---

Recommendations · Built in India for US companies

# Recommendation system development

We build recommendation and ranking systems for US companies: personalized product, content, and feed recommendations grounded in real behavior and shipped behind A/B tests, so you see conversion impact instead of a data-science science project.

Book a 30-min scoping call [See our work](https://foundrysoft.co/work)

No sales script. You talk to the engineers who'd build it.

9+ hrs

Timezone overlap

Our team works a shifted day so you get real-time standups and same-day turnarounds in your time zone, not next-morning replies.

100%

You own the IP

Every line of code, model weight, and prompt is yours from day one. NDAs and clean IP assignment are standard, not an upsell.

Senior

No juniors hidden on the bill

You work directly with the engineers building your system. No account managers sitting between you and the people writing code.

Weeks

To first deployment

We move from scoping to a working system in production in weeks. Most engagements ship something usable inside the first month.

## What we build

Concrete systems we ship, tuned to your data and your stack.

### Personalized recs

Product, content, and feed recommendations tuned to each user's real behavior.

### Ranking & search

Rank results and listings by relevance and business goals, not just recency.

### Cold-start handling

Sensible recommendations for new users and items, not an empty shelf.

### Measured lift

Every change ships behind an A/B test so impact is proven, not assumed.

## How we work

01

### Scope & evals

We pin down what success means and build the evaluation set before writing the feature, so quality is measured, not guessed.

02

### Build in the open

Weekly demos against real data. You see progress every week and can change direction before it gets expensive.

03

### Ship & instrument

We deploy with logging, cost tracking, and guardrails in place, then tune against production traffic.

04

### Hand off or stay

Take the keys with full docs, or keep us on for iteration. Either way you're never locked in.

## Questions, answered

### How soon can we see conversion lift?

+

Quickly, because we ship changes behind A/B tests and measure against your real metrics. You get numbers within weeks, not a model that sits in a notebook.

### Do we need a huge dataset to start?

+

No. We start with what you have, handle cold-start explicitly, and add sophistication as behavior data accumulates rather than waiting for scale.

### Can it balance relevance with business goals?

+

Yes. We can weight for margin, inventory, or strategic content alongside relevance, with controls so you decide how aggressive it gets.

### Will it feel creepy to users?

+

We recommend on behavior you're permitted to use and give you controls over how personal it gets. Good recommendations feel helpful, not like surveillance.

## Let's scope your build.

Tell us what you're trying to ship. We'll tell you honestly whether AI is the right tool and what it would take.

Start the conversation

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