Data and Analytics firm LUMIQ has raised USD 5.5 million in a Series A round led by existing investor Info Edge Ventures on Monday. The company stated in a press release that the fund raised is the first round of investment witnessed this year. LUMIQ mainly builds datastack products and solutions with a key focus on Financial Services and Insurance (FSI).

Info Edge Ventures led the round with INR 23.41 crore, with Season Two Ventures and RedStart Labs investing INR 19.51 crore and INR 1.2 crore, respectively. The funds will be further used to develop LUMIQ’s presence in global markets—regions such as US and SEA—thereby strengthening the company’s FSI-focused product line.

The firm has raised a whopping USD 7.5 million to date, which includes USD 2 million seed round led by Info Edge Ventures in August 2021. According to Fintrackr, the company has been valued at around USD 40 million or INR 320 crore (post allotment).

Read: Digital Transformation Is Not A Sprint, But A Fast Marathon: Shoaib Mohammad, Founder & CEO, Lumiq

LUMIQ was founded by data technologist Shoaib Mohammad, who has developed cutting-edge AI/ML solutions and data stack products—E2E Observability, DataOps, ML Risk Governance, Self-service analytics—for the FSI sector. 

Addressing the announcement, LUMIQ’s CEO Shoaib Mohammad spoke to AIM exclusively: “Key digital transformation initiatives get affected if the data isn’t reliable or business-ready. Data manageability is a big challenge for financial services and insurance enterprises. A weak data backbone can have a major impact in terms of data security, quality, auditability, and compliance. Profitability takes a big hit. We operate across the entire data value chain to plug existing gaps and make sure that enterprise data is business-ready—for analytics, reporting, operations, compliance audits, AI/ML operationalisation and more. I’m truly excited about the next leap that LUMIQ is making. We are building data products to enable global enterprises with best-in-class data engineering practices and data stacks built-for-scale. “