Emumba learned to build for someone else.

Before Emumba was building software for enterprise customers, it was building a place for cricket fans to talk. Cricout offered live match discussion, personalized feeds and the possibility that anyone with an opinion could become someone worth following. For a software company in Pakistan, cricket supplied an obvious starting point: a subject its team understood and an audience far beyond its hometown.

A good app, a difficult business

Founder Owais Anjum had worked in Silicon Valley before returning to Pakistan. Emumba, founded in 2011, pursued two rather different products: Cricout and PatientsWithPower, a platform connecting cancer patients and treatment providers. One organized sporting conversation; the other addressed consequential medical communication. Both required the team to turn complicated needs into usable software.

Building the software was only part of the problem. Historical reporting described an $85,000 investment in January 2013 and Anjum’s extended period without a salary. Cricout needed an audience large enough to support advertising. Anjum identified acquiring users as a difficulty. The ability to make a product did not automatically supply the ability to distribute it.

Historical company descriptions date the shift to services to 2015, after the products failed commercially. That distinction matters. A team could have useful engineering skills even when the business built around its own ideas struggled. Emumba began selling those skills to customers who brought their own problems, markets and reasons to pay.

The customer brings the problem

Today, Emumba sells engineering services to enterprises, startups and smaller businesses. Its public customer relationships include Microsoft, Extreme Networks and Salesforce. The work spans applications, data systems, generative AI, cloud infrastructure, testing and user experience. A buyer can bring a product to develop, an existing platform to modernize or an operational bottleneck to remove.

Look at the testing work. An unnamed Fortune 500 customer wanted a video platform for broadcasting technology events. Manual regression slowed releases, while the application faltered under load. Emumba describes building automated functional tests and performance tooling that could run 10,000 browsers simultaneously. The important question was whether the product could withstand the audience it was supposed to attract.

For Safe Kids AI, the problem involved sensitive monitoring data, reliable availability and repeatable deployment. Emumba’s account describes access controls, multiple AWS Availability Zones, message queues and infrastructure automation. These projects explain its place in the market: an engineering partner for systems that must keep working after launch. An internal team or another specialist consultancy remains an alternative; the choice depends on capability and delivery needs.

“Emumba is the best partner experience I have had, ever!”Maxim Lukiyanov · Principal PM Manager, Microsoft
Customer testimonial published by Emumba

The model bill changes the conversation

InterWiz makes the operating problem especially clear. The AI recruiting platform had launched, but its model usage was squeezing margins. Emumba’s migration account describes moving its language-model backend from Azure OpenAI to Amazon Bedrock, using Claude 3.5 Sonnet for interview questions, evaluation and feedback. Cost pressure and alignment with AWS supplied reasons to reconsider the original architecture.

The change required more than replacing an API address. Engineers separated provider differences from application logic, adapted prompts, redesigned authentication and retained fallback mechanisms. They provisioned parallel environments and compared responses, scoring behavior and latency before gradually shifting traffic. That sequence is worth copying: establish what acceptable behavior looks like before letting a cheaper system handle everyone.

Emumba reports a 90% reduction in AI processing costs and, separately, a two-thirds reduction in interview LLM interaction costs. Those are different measures. Neither should become a promise for another application. Savings depend on traffic, model selection and the quality the product requires. A migration that changes interview scoring or slows conversations needs further work, however attractive its projected bill.

The cloud bill, minus a third

Another customer was questioning whether to continue using AWS because spending had become difficult to manage. Emumba describes a practical response: resize instances to fit workloads, evaluate purchasing options, stop noncritical environments when unnecessary and introduce budget alerts. Resource monitoring made the waste easier to see. The reported result was approximately 33% lower monthly infrastructure expenditure.

The lesson is accessible even without hiring the company. Measure what is running. Match resources to actual use. Give someone a warning before the budget is exhausted. Scheduled shutdowns suit genuinely noncritical environments; services needed around the clock require other measures. For buyers, the useful comparison is the expected operating improvement against the engineering effort required to achieve it.

A laboratory with customers

Emumba still experiments on itself. Its 2025 North Star project explored using AI agents across company functions and produced a working objectives-and-key-results application. The founder’s year-end account also described a dedicated generative AI research group. The company’s advice from North Star was refreshingly practical: treat AI as a teammate, deliver something usable and iterate.

That experimental habit sits alongside mentorship, technical talks and a Toastmasters club established in 2019. Communication belongs in this story because customers must explain requirements, question results and trust people working across borders. For someone considering Emumba, the sensible starting point is a bounded problem with an observable result: a deployment delay, a costly workflow, a failing load test. Its own history offers a reason to insist on that discipline. Engineering becomes a business when somebody has a problem worth solving.