How Redrob Is Rebuilding AI Economics for India’s Students

India’s AI story has often been framed around elite enterprise adoption and high-cost models trained far from its classrooms. Redrob is attempting to reverse that logic. With a fresh $10 million Series A round, the AI research startup is positioning students, not enterprises, as the entry point for India’s next AI wave. Its thesis is straightforward but ambitious: if AI becomes a default learning layer for students, enterprise adoption will follow organically from within the workforce.

The funding round, led by Korea Investment Partners with participation from KB Investment, Kiwoom Investment, Korea Development Bank Capital, Daekyo Investment, and DS & Partners, brings Redrob’s total capital raised to $14 million. The company plans to roll out free LLM access for Indian universities starting in Q1 2026, alongside multilingual AI support across all 22 constitutionally recognised Indian languages by the end of 2026.

At the centre of this strategy is an architectural departure from large, centralised AI models and a deliberate focus on cost, access, and resilience in low-bandwidth environments.

To understand how Redrob plans to execute this at a national scale, CiOL spoke with Kartikey Handa, Chief Operating Officer and Head of India Operations, Redrob.

Interview Excerpts

⁠You’re promising free LLM access for 300M students and a 50x reduction in operating cost, can you walk us through the concrete ML and systems innovations (model distillation, quantisation, retrieval augmentation, edge/cloud split, and caching) that enable this scale, and what trade-offs in latency, accuracy, or safety you are accepting to hit those cost targets?

Redrob's approach to cost-efficient AI infrastructure is built on a fundamental architectural choice: rather than competing on model size, we've built a system using multiple specialised small language models (SLMs) instead of one giant general-purpose AI.
Our stack includes a Redrob General LLM that acts as a "manager", routing tasks to specialised models. This specialisation allows each model to excel in its domain while remaining computationally efficient.

The key innovation is edge device deployment: these smaller, optimised models can run directly on phones and laptops rather than requiring expensive cloud server calls. This means faster responses (no data travel time), better privacy (data stays on-device), and dramatically lower per-query costs, enabling sustainable free usage at a massive scale.

We also employ retrieval-augmented generation, model distillation, and aggressive inference optimisation. We accept slightly higher latency in exchange for reliability during peak and low-bandwidth conditions, because a reliable answer is more valuable than a marginally faster one we can't afford to keep online.

⁠Offering nationwide, free LLM access implies large-scale user data collection. What privacy-first architectures, data minimisation, and consent mechanisms will you use for students, and how will you prevent commercialisation of student data while still monetising enterprise offerings?

Student privacy is fundamental to Redrob's architecture. Our approach operates on three principles:

• Data Minimisation: All student data remains confidential and is never shared without explicit consent.
• Structural Separation: Our monetisation creates natural separation between student data and commercial use. We monetise through APIs, institutional partnerships, and enterprise offerings, not through selling student data. Student usage data is not commercialised for targeting purposes.
• Regulatory Compliance: We're fully compliant with India's Digital Personal Data Protection Act. Universities maintain complete visibility into how student information is used, and students can opt out at any time.
• Edge-First Architecture: Where possible, AI processing happens on-device, meaning personal data never leaves the student's phone or laptop, eliminating exposure to server-side data breaches entirely.

⁠Redrob plans multi-language support across India’s 22 languages. What is your roadmap for data collection, annotation quality, evaluation metrics, and independent benchmarking per language, and how will you demonstrate parity (or acceptable degradation) versus English models to institutional partners?

Our localisation approach starts from how Indian students actually speak and learn and not from translation checklists.

• Training Methodology: We train and evaluate on code-mixed, informal, and exam-style language, so the model handles Hinglish, regional terms, and mid-conversation language switching naturally. This reflects how students actually communicate in Indian classrooms and WhatsApp groups.
• Context-Aware Calibration: We tune responses for grade level and board type; a 9th grader in a state-board school and a college student in a metro receive appropriately calibrated explanations with different abstraction levels and difficulty.
• Infrastructure Design: The entire stack is designed for low-bandwidth, mobile-first environments, ensuring the quality of explanation stays consistent even when network conditions don't.
• Benchmarking Commitment: We're building India-centric benchmarks and contributing research back to the ecosystem. Our goal is for Indian teams, including those building on Redrob, to set global benchmarks in Indian-language AI, and for those ideas to be adopted far beyond India.

The student-to-workforce pipeline is central to your B2C-to-B2B strategy. What evidence do you have that student familiarity converts to enterprise adoption at scale, and what customer acquisition, retention, and enterprise sales metrics should partners or investors track to validate this funnel?

Redrob has built substantial distribution with over 3 million users acquired through our skill-testing platform and 50+ strategic university partnerships across India. This gives us direct relationships with students throughout their academic journey.
Our thesis is that if we become the default AI layer for students and young professionals, that distribution and trust become significant long-term assets for enterprise adoption. Early professionals who relied on Redrob during their education become internal advocates when their employers evaluate AI solutions.

PeopleSearch aggregates profiles from 19+ sources to power intent signals and contact data. What legal and ethical guardrails exist around data provenance, consent, accuracy, and opt-out – and how do you prevent bias or harmful targeting when these insights are used by recruiters or sales teams?

To clarify an important distinction: PeopleSearch provides contact data aggregation, not intent signals; these are very different capabilities with different data requirements and privacy considerations.

• Data Sourcing: Redrob does not source contact data from our own user database. All contact data is obtained through third-party data vendors who are GDPR-compliant, and the underlying records are drawn exclusively from publicly available sources.
  
• Vendor Compliance: Our data vendors maintain their own compliance frameworks aligned with GDPR standards, which include established protocols for data accuracy, consent verification, and individual rights management.
  
• Separation from Student Data: This is structurally separate from our student-facing platform. Student usage data from our LLM and skill testing products is never fed into PeopleSearch or shared with enterprise customers for contact enrichment purposes.

⁠If you partner with public universities or government bodies, what resiliency, cost-sharing, liability, and audit controls are you willing to commit to, particularly around exam integrity, misinformation risks, and operational continuity during critical academic periods?

Redrob is designed for institutional deployment at scale, with governance appropriate for public sector partnerships.

• Safety Frameworks: We maintain hard lines on abuse, exploitation, self-harm, and hate, while allowing factual, multi-perspective discussion of sensitive topics. For education specifically, we bias toward explanation and scaffolding rather than auto-completing assignments.
• Academic Integrity: Our goal is that students feel safe asking anything they're genuinely curious about, but the system keeps nudging them back to understanding, not shortcuts.
• Infrastructure Reliability: We've built for low-bandwidth, mobile-first environments with reliability during peak conditions critical for academic periods. Our edge-first architecture reduces dependence on centralised servers, improving resilience.
• Partnership Readiness: We currently work with 50+ universities, including IITs, NITs, and leading engineering and business colleges. We're prepared to discuss formal SLA commitments, audit rights, and co-investment frameworks with government partners.
• Roadmap: For government partnerships (India AI, AICTE, YUVAi, Skill India/NSDC), we're building toward formal engagement with documented pilot outcomes and traction data to support these discussions.