How FireAI Is Helping Turn Enterprise Turn Data Into Decisions

Trying to make sense of enterprise data can often feel like searching for a needle in a haystack.

For brands like Bata, the challenge was never the lack of data, but too much of it scattered across disconnected systems. Sales, inventory, pricing, footfall and seasonal demand all sat in different places, making it difficult for teams to quickly identify what was actually driving a slowdown.

FireAI’s platform brought those datasets together, traced the decline through a causal chain and surfaced the underlying factors behind the dip. What once took days of investigation could now be turned into a structured analysis in minutes.

A similar problem played out inside IRCTC’s network of kitchens. While AI-enabled cameras were already monitoring kitchens around the clock, every safety violation was still being logged as an isolated event. FireAI turned those alerts into a single intelligence layer, helping central teams spot recurring issues, compare compliance across locations and automatically route violations to kitchen managers for corrective action. 

The platform also helped D2C beauty brand Plum improve net revenue realisation by around 12% and cut reporting time from more than eight hours to under two minutes by automating data reconciliation across marketplaces. So, what does FireAI exactly do?

Founded in 2024 by Vipul Prakash, FireAI is building what it calls a decision intelligence platform. Rather than stopping at what happens inside a business, the startup is designed to explain why it happened and suggest what teams should do next.

During a live demonstration before Inc42, Prakash asked FireAI why a certain company’s sales were declining. Within seconds, the platform traced the drop to its underlying business drivers, pinpointed that nearly 40% of the decline stemmed from a specific customer’s account and generated a forecast of how the business could perform going forward.

The genesis behind FireAI comes from what the founder observed across industries, and the startup is now evolving that insight into a broader solution.

Learnings From The Past

Unlike many AI founders who entered the AI wave through foundation models, Prakash’s motivation came from years of running businesses across manufacturing, logistics, ecommerce, fintech and pharmaceuticals.

After graduating in electrical and electronics engineering in 2014, he worked at startups like Zomato and BharatPe before joining his family’s pharmaceutical business. Across each role, he noticed the same pattern: companies generated massive amounts of data, but turning that data into decisions still depended on spreadsheets, delayed reports and specialist analysts.

The issue became most obvious after he joined the family business. Products would run out in one market while inventory sat idle in another because sales, distributor and warehouse data lived in separate systems.

“We had data all around us, but nobody knew how to use it. A product would sell out in Odisha while another distributor still had inventory sitting there. We weren’t planning anything because we couldn’t analyse what the data was telling us,” Prakash recalled. 

This experience shaped FireAI’s first thesis. Before writing production code, the founding team spent nearly five months speaking to businesses across sectors, validating pain points and collecting enterprise datasets. Instead of building another chatbot, they focused on making enterprise data easier to access through conversational interfaces.

Commercial go-to-market began in November 2025 after multiple iterations with early customers. According to the company, nearly every major product feature came directly from customer feedback rather than an internal roadmap. But more than its customer-centric approach, it is the startup’s tech stack that does the heavy lifting.

From Dashboards To Decisions

Sitting on top of existing enterprise systems, FireAI helps businesses connect fragmented data and query these mounds of information through simple prompts. Here is all its tech stack does:

• Connects data across 700+ sources, including ERPs, accounting software, CRMs, cloud databases and marketing platforms
• Creates a unified semantic layer without moving customer data outside their infrastructure
• Reads only database schemas and table structures, instead of extracting complete datasets
• Generates SQL queries directly against the customer’s own databases. This enables real-time analysis while keeping data within the customer’s environment

The process begins when clients connect their internal databases to FireAI. Once linked, the AI platform activates its multi-agent architecture, which sits before the language model that handles prompts.

These specialised AI agents first do the groundwork: they identify relevant tables and fields, interpret the business ontology, map relationships between schemas, define the right formulas and assemble the business context.

Only after this preparation does the LLM generate SQL queries, which are then run against enterprise databases.

Under the hood, FireAI uses a fine-tuned version of Llama 3.3 for text-to-SQL generation, although the platform remains model agnostic and allows enterprises to integrate their own preferred models if required.

“The model is just the electricity for us. The real work happens before the model. Our agents understand the business context, the tables, the formulas and the relationships first. Only then does the LLM generate the SQL query,” Prakash told Inc42, arguing that the startup’s orchestration layer, rather than the model itself, forms its technical moat.

Climbing Up The Enterprise Ladder

FireAI claims that its multi-agent architecture significantly reduces token consumption because only structured context reaches the LLM. This, in turn, allows FireAI to price its platform through subscriptions instead of usage-based token billing while maintaining gross margins of around 80-85%, according to the founder.

Not just recurring subscriptions, the startup also monetises its offerings by charging enterprises an upfront integration fee to connect their existing data infrastructure. 

This approach has helped FireAI lap up 200 clients, including MSMEs and some of the biggest names in India such as Bata, IRCTC and Central Warehousing Corporation (CWC). The user mix is also skewed towards enterprises, which account for roughly 60% of its revenue. MSMEs contribute the remaining 40%. 

On the back of this, FireAI claims to be currently clocking ₹9 Cr in monthly recurring revenue (MRR).

While the early traction has been healthy, FireAI sits in an enterprise tech space that has its own set of hurdles. 

Firstly, enterprise data remains fragmented as many organisations still run on legacy ERPs, custom integrations and siloed databases. This makes zero-friction onboarding harder than the marketing promise and could take some critical time before the platform’s full value shows up.

Secondly, competition is intensifying. Global cloud providers are baking more AI into their analytics stacks, while traditional business intelligence (BI) vendors are adding conversational and LLM features on top of existing dashboards. As such, FireAI’s task is cut out: convincing customers that it is not just another tool, but a different way of doing analytics.

Thirdly, selling into enterprises means long cycles and management risks. Then, there are also issues related to control, accuracy and security. Finally, as new models emerge, FireAI will need to keep its orchestration layer evolving without disrupting existing customers. 

Nevertheless, as enterprises deploy AI across core business functions, the conversation is shifting from generating insights to enabling decisions. FireAI is betting that the real opportunity lies not in replacing business intelligence platforms, but in building a reasoning layer on top of them. 

While the jury is out on whether this becomes a new category, FireAI is sharpening its tools to capitalise on this opportunity.

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