AI risk intelligence startup Privue raises angel round to expand dealer intelligence application

VMPL
New Delhi [India], June 12: Privue, a startup focused on AI-powered risk intelligence for enterprises, has raised an angel round of funding. The round saw participation from owners of large manufacturing companies, along with senior leaders from real estate, wealth management, and insurance.
Gurugram-headquartered Privue, which operates under Nexvue Technologies Private Limited, will use the capital to deepen its AI capabilities and expand its go-to-market presence across sectors including pharma, MedTech, consumer electricals, agro, chemicals, and other manufacturing segments.
Founded by Saurabh Verma and Snehil Vijay, Privue is building a web-based AI risk analytics application that converts fragmented data--credit bureau records, GST signals, financial filings, and legal records--into predictive intelligence on dealers and distributors, helping CFOs, credit heads, and channel managers make faster, data-backed decisions.
Saurabh, Co-founder and CEO, is an alum of SRCC and ISB Hyderabad, and brings two decades of financial risk management experience, including seven years at Moody's, where he worked on data solutions and risk assessment for banks and financial institutions across the US and the UK. Snehil, Co-founder and CPO, is an IIT Roorkee alum who worked at HSBC and JP Morgan before starting Privue.
The startup says most mid-to-large Indian enterprises still depend on weeks of manual data collection and informal credit judgments to manage dealer and distributor networks, with collection problems often surfacing too late to act on.
At the core of the application is an analytics engine that ingests data across public and proprietary sources to score dealer risk and algorithmically generate credit terms. The startup applies this across three stages of the dealer relationship. For dealer discovery, companies can search from a database of over one million dealers, filtered by HSN code and location, with each dealer assigned a quality score derived from publicly available information using a heuristic model--enabling better dealer selection driven by data rather than field referrals alone. Before onboarding, the application runs assessments covering GST turnover-based capacity, compliance, and credit bureau profiles, and recommends credit terms--advance percentage, credit period, and credit limit--calibrated to the dealer's risk profile and the company's policy. It also sources geo-tagged facility photographs for physical verification, reducing the need for onsite visits.
Once a dealer is onboarded, the application applies analytics to invoicing and collections data to detect early signals of dealer stress before they turn into bad debt, and automates reminders for post-dated cheque and bank guarantee due dates.
Two of the application's agentic AI capabilities have found particular resonance with clients, according to the startup. A conversational workspace allows CFOs and Business Development heads to query their dealer portfolio in natural language and get specific answers in minutes, instead of waiting days for teams to compile reports. An AI calling agent handles follow-up calls for overdue collections, cutting the time finance teams spend chasing payments without adding headcount.
"Saurabh understood the problem from every angle. Snehil knew exactly how to build the solution," said one of the company's angel investors. "That combination is exactly what a platform like this needs."
The startup plans to target enterprises across pharma, MedTech, Consumer Electricals, Agro, chemicals, and other manufacturing sectors, where dealer and distributor networks form the backbone of distribution.
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