Founders’ Adda
Detailed Summary
- Moderator (Archana Jahagirdar) opened the session, explaining the “founder’s Adda” format: each founder should talk only about the product (no fundraising, no market‑size slides).
- The audience was asked to keep the language accessible – jargon is fine for AI‑savvy listeners but a brief, simplified version should be ready for non‑technical participants.
2. Founder Presentations
2.1 Ravinder Kumar – Technod8.AI (formerly “Technodate AI”)
| Key Points | Details |
|---|---|
| Mission | “Automate automation” – create an AI‑driven assistant that lets any company design, deploy and troubleshoot industrial robotics/automation without deep expertise. |
| Background | 15 years in robotics, previously with the world’s largest industrial‑robot manufacturer (achieved 100 % shop‑floor automation in 2010). Noted the paradox: high‑end robots exist, but most Indian factories cannot exploit them. |
| Product – Agent TKI | A three‑module suite: 1) Conceptualisation (AI helps design the robotic solution), 2) Deployment (auto‑generates robot programmes, wiring diagrams, commissioning steps), 3) Troubleshooting (real‑time fault diagnosis & re‑programming). |
| Technical Approach | Initially considered building a foundational model but pivoted to customer‑driven experiments. After pilot deployments (including Fortune‑500 customers and a proof‑of‑concept with the Indian Air Force), the team concluded a bespoke foundational model is still required. |
| Demo Highlights | Showed a UI where the AI produces full system architecture, robot code, and step‑by‑step assembly instructions. Demonstrated a CNC‑style generation for aerospace engine fault handling: user enters an error code → AI visualises the 3‑D component and outputs a repair workflow. |
| Strategic Partnerships | Collaboration with Dr Sumit Chopra (AI “godfather” Yan Lee Kun) and academic talent from IITs. |
| Current Status | Demonstrations at Hall 14 (conference expo). Early field trials, but no commercial rollout yet. |
| Q&A Highlights | • Foundational‑model question: Ravinder argued that generic chat‑LLMs cannot handle the “complex workflow” required for industrial automation; a custom application layer is essential. • Open‑source vs. proprietary models: Even if OpenAI builds a specialized model, it still needs a domain‑specific application stack and on‑premise deployment because industrial data is proprietary. |
2.2 Vaibhav Vats Shukla – Quansys AI
| Key Points | Details |
|---|---|
| Mission | Build India‑first end‑to‑end voice‑infrastructure that can run a call‑center without any human‑in‑the‑loop. |
| Market Context | Indian call‑center industry is a $55 bn sector (≈ 2 % of India’s GDP). Existing platforms rely on outdated “agent API” models. |
| Product Stack | A layered architecture: 1) Data Engine – generates massive multilingual speech datasets (including Indic languages). 2) Model Layer – custom LLM + speech‑to‑text / text‑to‑speech tuned for Indian accents. 3) Orchestration Layer – handles call routing, context management, API hooks. |
| Key Partnerships | Integrated with OpenAI for foundational model licensing; partnered with Paytm, CRED, PropBotX for early pilots. |
| Pricing Model | Per‑minute subscription – customers pay a usage‑based fee (e.g., ₹2 /minute). |
| Scalability | Current concurrency ≈ 50 simultaneous calls; roadmap to increase as model optimisation progresses. |
| Cost‑Savings Argument | For large BPOs (e.g., SBI Insurance), automation can cut 90 % of call‑center labour cost, reducing price per minute from ~₹30 to ₹2‑3. |
| Q&A Highlights | • Integration with telecom numbers: The system can be attached to a PSTN line via a websocket handshake, enabling a fully automated inbound agent. • Foundational‑model scaling: Quansys built its own data‑engine (award‑winning from the Prime Minister’s office) to avoid reliance on public datasets that cause “serving‑break” failures. • Regulatory compliance: Data is processed on bare‑metal European servers (Hetzner) rather than public clouds, ensuring tighter control over privacy. |
2.3 Pradyum Gupta – Papli Labs
| Key Points | Details |
|---|---|
| Problem Statement | Existing map‑navigation services lack real‑time situational awareness (e.g., gate closures, fog, accidents). |
| Core Technology | Vision‑first data pipeline: dash‑cams, CCTV and other roadside cameras feed raw video to a central platform; AI extracts semantic events (open/closed gates, traffic density, weather conditions). |
| Data Scale | Operates on ~100 PB of video from ~8,000 fleet units (e.g., Delhi Transport Corporation buses). |
| Use‑Cases Demonstrated | 1) Dynamic billboard pricing for JC Decox – shift from flat‑rate to impression‑based pricing, boosting revenue by ~45 %. 2) Autonomous vehicle edge‑case mapping for MG‑Motors – real‑time hazard detection (fog, broken dividers). 3) Transit optimisation for DTC – route‑rationalisation algorithm that matches capacity to demand, increasing passenger load and revenue. 4) Live news generation – converting raw city‑wide video feeds into searchable news bites. |
| Business Model | B2B SaaS: Tile‑based pricing (e.g., 1.5 L per 25 km² tile per day). |
| Challenges | • Data acquisition consent – convincing drivers and municipal agencies to share video streams. • Privacy compliance (DPDP) – front‑camera faces and number plates are blurred; no raw footage is ever released. |
| Q&A Highlights | • Privacy compliance: All video is stored on bare‑metal European servers; faces/plates are anonymised before processing. • Incentive model for data providers: Papli doesn’t pay drivers; instead, the operational efficiency gains (e.g., DTC saved ≈ ₹800 cr of lost revenue) act as the business incentive. • Scalability: The platform processes petabytes in near‑real time using custom bare‑metal GPU clusters (CDAC‑supplied supercomputers). |
2.4 Meenal Gupta – EGRIO Pi Solutions (Imagix AI)
| Key Points | Details |
|---|---|
| Mission | Provide AI‑driven precision imaging & treatment‑planning for cancer radiotherapy. |
| Regulatory & Compliance | HIPAA, ISO 13485, CEDESCO (India’s medical‑device certification), multiple patents pending. |
| Problem Landscape | ~20 million new cancer cases annually in India; shortage of oncology experts leads to delayed treatment planning and poorer outcomes. |
| Product Flow | 1) CT/MRI upload to secure cloud DICOM viewer. 2) AI segmentation (organ‑and‑tumor contouring). 3) Radiotherapy plan generation – reduces manual contouring from 60‑90 min to 5‑15 min (≈ 80 % time saving). |
| Data & Performance | Trained on 5 M images (≈ 30 % Indian data from remote North‑East regions). Accuracy 92‑99 % depending on case complexity. |
| Deployments & Impact | • Gujarat pilot: 1 M chest X‑rays, 4 000 TB‑positive detections, 6 early‑stage lung cancers identified. • Radiotherapy plans: > 1 000 completed; 50 k chest X‑rays screened with 2 700 TB flags. |
| Recognition | Invited to Microsoft to demo solution; praised by PM Narendra Modi. |
| Q&A Highlights | • Trust & Human‑in‑the‑Loop: AI assists but final approval stays with radiologists; no replacement of clinicians. • Data residency: All data processed on‑premise or secure cloud within India, complying with healthcare regulations. |
2.5 Vivek Gupta – Indus Labs AI
| Key Points | Details |
|---|---|
| Mission | Build a complete Indian‑language voice operating system (speech‑to‑text, text‑to‑speech, LLM, speech‑to‑speech) that can be used by any enterprise to create voice agents. |
| Why Indian‑centric? | Dialectal variation every ~20 km; global providers (Google, 11 Labs, Azure) deliver only a generic Hindi model, leading to poor user experience. |
| Platform Architecture | 1) Infrastructure layer – own GPU servers and on‑premise data centres (low latency, sub‑500 ms). 2) API layer – modular services (STT, TTS, emotion‑aware STT, voice cloning). 3) Workflow builder – no‑code UI to design call flows, integrate with CRMs, trigger webhooks. |
| Key Differentiators | • Dialect mastery across Indian languages (including regional accents). • Emotion‑aware STT (recognises happiness, anger, etc.). • Cost advantage: ₹2 /min vs. ~₹8 /min for 11 Labs (≈ 70 % cheaper). • Latency: 300‑400 ms end‑to‑end, enabling natural conversation. |
| Partnerships & Reach | Telecom partners Airtel, Jio for SIP integration; B2B customers (banks, NBFCs, FMCG). Global white‑label partners in Dubai, Germany; expanding to Arabic, German, French, Mandarin. |
| Scalability | Handles 1 000 concurrent requests; can spin up from 0‑1 000 in ~10 min. |
| Q&A Highlights | • Data residency: All training data resides in India, satisfying sovereignty requirements. • Foundational‑model strategy: Started with public datasets, then built proprietary data‑engine to avoid “serving‑break” failures. • Cost‑impact: For typical BPO call‑center workloads, total operating cost can be cut by up to 70 %. |
3. Cross‑Session Themes & Discussions
| Theme | Insights / Observations |
|---|---|
| Localization & Indian Context | Both Quansys AI and Indus Labs AI highlighted the necessity of building indigenous data pipelines and models to handle Indian dialects, regulatory rules, and data‑sovereignty concerns. |
| Infrastructure Choice (Bare‑Metal vs. Cloud) | Multiple founders (Quansys, Papli, Indus) deliberately used bare‑metal or private‑cloud (European data‑centres, CDAC super‑computers) to maintain low latency, privacy, and cost control. |
| Foundational Model vs. Application Layer | Ravinder (Technod8) and Indus Labs argued that generic LLMs are insufficient for domain‑specific workflows; a custom application layer and domain‑specific fine‑tuning are essential. |
| Business Model – Usage‑Based Pricing | Most startups adopted pay‑per‑minute or per‑tile pricing, aligning costs directly with value delivered (call‑center minutes, mapping tiles, radiotherapy plans). |
| Regulatory & Privacy Compliance | Papli Labs and Imagix AI both described privacy‑by‑design measures (blurring faces/plates, DPDP compliance, HIPAA/ISO certification). |
| Scalability & Real‑World Deployments | Demonstrations ranged from pilot projects with Indian Air Force / Fortune‑500 (Technod8) to large‑scale city‑wide video ingestion (Papli), showing early traction but also highlighting the need for robust data pipelines. |
| Human‑in‑the‑Loop | Imagix AI, Quansys AI, and Indus Labs emphasized that AI augments, not replaces human operators, preserving trust and regulatory compliance. |
4. Audience Q&A – Representative Questions & Answers
| Speaker | Question | Answer / Discussion |
|---|---|---|
| Ravinder (Technod8) | “If OpenAI builds a dedicated industrial model, will you still need your own application layer?” | Yes – even a bespoke model must be wrapped in an application framework that handles data privacy, on‑premise deployment, and complex workflow orchestration. |
| Vaibhav (Quansys) | “How do you handle scaling when the original public data caused service crashes?” | Built a proprietary data‑engine (award‑winning), now generating synthetic multilingual data in‑house; also migrated to bare‑metal servers, avoiding public cloud throttling. |
| Pradyum (Papli) | “How do you stay DPDP‑compliant with billions of video frames?” | All video is processed on European bare‑metal servers, front‑camera faces and license plates are automatically blurred; raw footage never leaves the secure pipeline. |
| Meenal (Imagix AI) | “How do clinicians trust the AI contours?” | AI assists radiologists; final acceptance is always manual. The system’s 90 %+ accuracy has been validated on‑site with multiple hospitals. |
| Vivek (Indus Labs) | “What is the latency for a real‑time voice conversation?” | 300‑400 ms end‑to‑end latency (sub‑500 ms), comparable to human turn‑taking, thanks to on‑premise GPU clusters. |
| General | “How do you price for enterprises versus SMBs?” | Most adopt usage‑based models: per‑minute for voice (₹2 /min), per‑tile for mapping (₹1.5 L per 25 km² per day), per‑plan for radiotherapy (fixed fee per treatment). |
Key Takeaways
- Product‑first mindset: Each founder focused on a concrete AI‑driven solution (automation, voice, mapping, oncology, voice OS) rather than fundraising or market sizing.
- Local Data & Infrastructure: All startups emphasized the need for India‑centric datasets, bare‑metal/private‑cloud deployment, and data‑sovereignty to meet latency, cost, and regulatory demands.
- Application Layer is Crucial: Even with powerful foundational models, domain‑specific orchestration, UI, and compliance layers are indispensable for industrial, healthcare, and telecom use‑cases.
- Usage‑Based Pricing Aligns Value: Pay‑per‑minute, per‑tile, or per‑plan models allow startups to scale with demand while offering measurable ROI to enterprise customers.
- Human‑in‑the‑Loop Remains Standard: Across sectors (radiotherapy, call‑center automation, industrial troubleshooting), AI is positioned as an assistive tool, preserving human oversight for trust and regulatory compliance.
- Regulatory Compliance Integrated Early: DPDP, HIPAA, ISO 13485, and CEDESCO certifications were built into the product pipelines, not added later.
- Scalable Architecture Demonstrated: From 100 PB video pipelines (Papli) to multi‑language voice APIs handling thousands of concurrent calls (Indus Labs), founders showcased real‑world scalability.
- Strategic Partnerships Accelerate Adoption: Collaborations with OpenAI, telecom giants (Airtel, Jio), government bodies (Indian Air Force, PM’s office), and large enterprises (Paytm, Fortune‑500) provide validation and market entry.
- Future Challenges: Building foundational models for niche domains, maintaining privacy at petabyte scale, and monetising large‑scale B2B deployments remain the next hurdles for these founders.
End of summary.
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