AI Startups To Watch: 5 Indian AI Startups That Caught Our Eye In April

India’s AI ecosystem faced its own set of challenges last month. While OpenAI dominated headlines with its staggering $122 Bn fundraise to build an AI superapp, the Indian ecosystem saw a cautious investor mood. 

As per Inc42’s recent Indian Investor Ranking & Sentiment Survey Q1 2026, about 92% of the 70 institutional investors surveyed are taking a rather selective approach to their AI bets. 

As the Indian AI ecosystem matures, more investors are shifting their focus away from startups providing AI wrappers to entities building the broader infrastructure to power India’s AI stack. 

This shift was clearly visible in investment trends this April. While Gnani.ai, a startup building a speech-to-speech LLM, secured $10 Mn, Sarvam AI has initiated talks with investors to raise $250 Mn at a unicorn valuation.

Meanwhile, Indian AI startups primarily restricted to wrapper-based offerings are seeing increased pressure from their more advanced foreign counterparts. Ripples are being felt. 

A case in point is Flipkart-backed Neuropixel.ai. The provider of GenAI solutions to the fashion ecommerce sector wound down its operations earlier this month as competition from large tech players like Google made it difficult for it to sustain operations.

The developments point to a broader shift within the ecosystem, where demand for more infrastructure, workflow automation, and India-specific use cases for AI is beginning to outgrow the hype of generic GenAI solutions. 

As a result, founders are increasingly turning their attention to emerging infrastructure gaps across discovery, engineering, monetisation and customer engagement.

Against this backdrop, we are back with our monthly edition of the “AI Startups To Watch” series. 

The latest edition spotlights five emerging AI players, building products across infrastructure, production debugging, revenue stack modernisation, and sales automation.

So, without further ado, here is the seventh edition of Inc42’s Five AI Startups To Watch.

Editor’s Note: This is not a ranking. The startups featured here are a curated selection by the Inc42 editorial team and are listed alphabetically.

Flexprice | Monetisation Infra For AI Native 

As AI products shift towards usage-based and outcome-driven pricing, traditional SaaS billing infrastructure is struggling to keep up. Founded in 2024 by Manish Choudhary, Nikhil Mishra and Koshima Satija, Flexprice is building a developer-first billing and monetisation platform designed for AI-native companies.

The founding insight came from Choudhary’s experience managing pricing changes, which could take up to a month. This highlighted the rigidity of legacy systems built for subscription-based models.

Built For Modern Pricing Models: Flexprice enables companies to implement hybrid pricing structures combining subscriptions, usage, and outcome-based billing. It also offers real-time tracking, granular control over credit allocation, and developer-centric workflows. Its open-source approach has already attracted over 3,500 GitHub stars, reflecting early traction within developer communities.

Expanding Into Revenue Automation: Beyond billing, the startup is now looking to build a broader revenue automation stack, targeting workflows across finance teams such as revenue recognition, payroll, and reporting. This startup operates in the subscription billing market, which is projected to reach $16.5 Bn by 2030. 

Framewise | Automating Sales Conversations

In B2B sales, product demos remain one of the most resource-intensive and time-bound processes, often leading to delays in conversion. Founded in 2025 by Kowshik Chilamkurthy and Alok Hegde, Framewise is building an AI-powered system that enables companies to deliver instant, personalised product demonstrations.

Reimagining Sales Engine: The platform uses voice AI combined with real-time product interaction to simulate live demos, eliminating the need for scheduled calls. Unlike static or video-based demos, the system can join live meetings, navigate the product interface, and personalise the walkthrough based on user context. This allows companies to engage prospects in real time.

Always-On Engagement Layer: Framewise’s system operates 24/7 and supports multiple languages, making it particularly relevant for global SaaS companies with distributed customer bases. Beyond demos, the same system extends into onboarding, customer support, and product adoption workflows. The platform is already being used across enterprises and training organisations. 

As inbound sales becomes self-serve and AI-assisted, the startup is betting on a future where the first layer of sales interaction is fully automated, reducing dependency on human-led demos. Framewise is potentially targeting the conversational systems market, which is projected to grow from $23.11 Bn in 2025 to $55.84 Bn by 2031. 

LLMLab | Visibility Layer For AI Search

As user discovery increasingly shifts from traditional search engines to AI assistants, brands are beginning to lose visibility in ways that existing marketing tools cannot measure or optimise. Founded in 2025 by Chetan Parmar, Ankur Phani and Sayak Sen, LLMLab is building a Generative Engine Optimisation (GEO) platform that helps B2B companies get cited and recommended across AI systems such as ChatGPT, Claude, and Gemini.

A Full-Stack AI Visibility Engine: The idea for LLMLab emerged from the founders’ own experience while scaling a previous consumer product. They learned that AI-led discovery was expected to reshape how brands compete for attention.  

LLMLab operates across four layers — tracking brand visibility across LLMs, generating actionable content insights, executing content through AI agents, and providing human-led strategic guidance.

Unlike traditional SaaS tools, the startup takes ownership of execution, positioning itself as an outcome-driven managed service rather than a dashboard product.

Market Opportunity: With a primary focus on US-based B2B SaaS companies, LLMLab is targeting the SEO software market projected to become an $8.1 Bn market opportunity by 2030, growing at a CARG of 18.2% CAGR. 

Scoutflo | Personal AI Site Reliability Engineer

As software systems grow increasingly complex, the cost of downtime and production failures continues to rise. Founded in 2024 by Kalpesh Bhalekar and Vedant Vyawahare, Scoutflo is building an AI SRE platform designed to automatically investigate and resolve production incidents in real time.

From Observability To Action: The idea emerged from the founders’ experience building a DevOps automation tool, where troubleshooting remained slow and manual. Teams were increasingly turning to tools like ChatGPT to debug incidents, signalling the absence of a dedicated, context-aware troubleshooting system.

Unifying DevOps: Scoutflo integrates across the entire DevOps stack — including observability tools, cloud infrastructure, incident management systems, and code repositories — to build a unified context layer. Acting like an experienced site reliability engineer (SRE), the platform correlates signals, identifies root causes, and surfaces clear resolution steps. What typically takes teams hours of investigation is reduced to minutes.

The platform is built around three layers — instant root cause analysis, a topology layer mapping microservices and dependencies, and a memory layer that learns from past incidents and runbooks to improve over time.

Enterprise Urgency And Opportunity: Scoutflo is addressing a high-stakes problem for cloud-native teams. The platform already counts 20+ large organisations using its stack in production. The startup is tackling the AIOps market, which is projected to grow from $18.95 Bn in 2026 to $37.79 Bn by 2031. 

Spleen AI | Automating Recruitment Flow

As hiring volumes increase and roles become more specialised, recruiting teams are struggling to balance speed with quality. Founded in 2025 by Akshat Chhapolia and Harish R T, Bengaluru-based Spleen AI is building an intelligent hiring platform that combines sourcing, screening, and structured evaluation into a single, AI-native system.

An Active Hiring Engine: Unlike conventional ATS platforms that primarily store candidate data, Spleen actively participates in the hiring process. It enables recruiters to source candidates based on skills and intent. Once candidates enter the pipeline, Spleen automates resume parsing, skill extraction, and rubric-based scoring. Each candidate is evaluated against predefined criteria, allowing teams to compare profiles objectively.

AI-Led Interviewing Layer: The platform’s AI interviewer, Mira, conducts role-specific interviews with adaptive follow-up questions. It generates transcripts, evaluates responses against defined rubrics, and produces structured reports, enabling asynchronous review by hiring teams. Through this process, Spleen attempts to bring both scale and rigour to candidate evaluation, reducing bias.

Spleen claims to improve recruiter productivity and reduce time-to-hire. The startup operates in the AI hiring software market, which is set to become a $1.1 Bn opportunity by 2030.

With inputs from Venu Rathore
Edited by Shishir Parasher 

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