# Turiyam builds cheaper AI inference infrastructure for enterprises > Source: > Published: 2026-06-24 03:19:55.796904+00:00 # Turiyam builds cheaper AI inference infrastructure for enterprises Bengaluru startup Turiyam, founded in December 2024 by Sanchayan Sinha, Parag Jain, and Praveen Jain, is building a full-stack AI inference platform that bypasses Nvidia hardware and bills customers by finished outputs rather than by tokens. The Ken reports token prices have fallen roughly 35% over two years while enterprise AI budgets have risen nearly 6x to about $7 million in 2026, a cost paradox Turiyam aims to exploit. Inc42 reported in March 2026 that the company raised $4 million in a pre-seed round led by Ankur Capital and Axilor's Micelio Fund, and is currently piloting with select enterprises. Its architecture pairs custom inference-focused silicon with a compiler-led software stack, targeting performance-per-watt and total cost of ownership rather than raw training throughput. ### What Turiyam is building The Ken reports that Turiyam, a Bengaluru deeptech startup, was founded in December 2024 by Sanchayan Sinha, Parag Jain, and Praveen Jain to build a full-stack AI inference solution. Per The Ken, the company designed a chip specifically for inference rather than training, and the stack omits Nvidia hardware. The Ken also reports Turiyam charges for finished outputs rather than by tokens - a pricing model that shifts cost risk from input compute to delivered results. ### Funding and backers Inc42 reported in March 2026 that Turiyam.ai raised $4 million in a pre-seed round led by Ankur Capital and Axilor's Micelio Fund. The company is using the capital to accelerate product development, expand its team, and support early enterprise and data-centre deployments. Ritu Verma, managing partner of Ankur Capital, said: "Putting the software stack in place from day one, rather than as an afterthought, is what makes the approach differentiated and relevant for where the market is headed." ### Market context The Ken reports token prices have declined about 35% over two years while enterprise AI budgets have climbed nearly 6x to about $7 million in 2026. That combination - cheaper tokens but far higher total spend - is the gap Turiyam is targeting. A former CTO quoted by The Ken said: "The cost of a single query has collapsed, but our total bill has exploded." ### Technical approach Turiyam's architecture pairs a hybrid memory design with a compiler-led optimization layer aimed at maximizing throughput for inference-heavy workloads while improving performance-per-watt and lowering total cost of ownership. Industry context: specialized inference accelerators from vendors such as Groq and Google have pursued similar tradeoffs by reducing memory and training-centric features in favor of smaller die area and lower energy per token. The startup is currently in pilot deployments with select enterprises. ### What to watch Adoption will hinge on published benchmarks showing total-cost-of-inference advantages at scale, interoperability with dominant model formats and runtimes, and traction with customers already running high-volume inference spend. Competing vendors typically need third-party benchmarks and real-world case studies to win enterprise procurement cycles, especially when incumbent GPU ecosystems already host existing workflows. ## Scoring Rationale Solid niche story on an early-stage Indian inference-silicon startup that has verified $4M pre-seed backing and active enterprise pilots. The output-based pricing model and Nvidia-free stack are a notable differentiator, but the company is pre-commercial and the story is regionally focused. Modest pull from 6.6 - sits in solid/niche territory rather than 'notable' until third-party benchmarks or larger customer announcements emerge. Practice interview problems based on real data 1,500+ SQL & Python problems across 15 industry datasets — the exact type of data you work with. [Try 250 free problems](/problems)