Api Development

LRB integrate effortlessly with internal and external systems a like.

Think of an API as a pipeline for data that connects two different systems together. Modern technology solutions utilize outside data and other tools on a regular basis, and the connectivity between solutions is made capable by these interfaces.
Our practice
The key practice when we say about REST/ API involve separating your API into logical resources. These resources are manipulated using HTTP requests where the method (GET, POST, PUT, PATCH, DELETE) has specific meaning. we understand the resource and the consumer where its one to one mapping is done.
Our Principal
Many of the API design opinions found on the web are academic discussions revolving around subjective interpretations of fuzzy standards as opposed to what makes sense in the real world. LRB goal to describe best practices for a pragmatic API designed for today's web applications. We make no attempt to satisfy a standard if it doesn't feel right.
Our Study

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We configure APIs for mobile, desktop, console and browser apps, as well as databases, search engines and intranet systems. We implement internal and external APIs while leveraging exposed third-party web services. Our industry-specific approach to API management preemptively solves issues related to the sharing of data, business logic, content, micro-services and communications.
Store-front Single Sign-On

To allow customers to sign in to their account in storefront automatically right after they sign in to merchant’s website or service, use Single Sign-On feature.

Store-front customization

If you want to customize how it looks or its logic - you can do that too: attach an external Javascript file or adapt a store to make it stylish.


he client wants to get the sorted list of companies, the GET /companies endpoint should accept multiple sort params in the query.


we can pass various options through query params.


searching the company name in companies list the API endpoint should be GET /companies.


the dataset is too large, we divide the data set into smaller chunks, which helps in improving the performance and is easier to handle the response.

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