Satchit Edge · Vishwakarma silicon · Sudarshana Cloud
We pair the hallucination-free Sudarshana ML framework with our own Vishwakarma silicon — shipped as Satchit Edge servers, phones and Industrial 5.0 devices, every unit connected to Sudarshana Cloud.
01 The stack
Vishwakarma silicon, the Sudarshana framework and the Satchit Edge device line are engineered together, so models lower straight onto the silicon — no glue, no abstraction tax.
Layer 01 · Silicon
Four classes of AI silicon — phones, wearables, edge and servers — all designed around the same 4-topology NPU.
Layer 02 · ML framework
One compiler, one runtime, one instruction set. Models are lowered straight onto Vishwakarma silicon, and every inference is verified before it is returned.
Layer 03 · Devices & cloud
The device line. Each unit ships as a server, a phone or an Industrial 5.0 controller — and every one connects to the common Sudarshana Cloud.
02 Sudarshana ML
Every inference is verified against a formal bound before it is returned. What cannot be proven is refused.
Step 01
Your model is lowered by vw-graph into a closed set of verified Vishwakarma kernels. No interpreter, no escape hatches.
Step 02
The sparse runtime places work across the 4-topology NPU with static, deterministic scheduling — latency is a property of the graph, not the queue.
Step 03
Every inference is checked against a formal bound computed at compile time. Precision drift and out-of-distribution inputs are caught before the answer leaves the chip.
Step 04
If the bound holds, the answer is returned with its proof attached. If it does not, the system says so — it never guesses.
03 Vishwakarma silicon
Every Vishwakarma part is designed around the same 4-topology NPU and the same Sudarshana runtime.
A 3nm phone SoC fusing a 4-topology NPU with always-on vision. Runs Sudarshana on-device, no cloud round-trip.
Ships in Satchit Edge Phone
A sub-1W microchip for rings and watches. Streams sensor inference with continuous on-body learning.
A rack-scale edge node for factories, retail and towers. Deterministic latency under heavy real-time load.
Ships in Satchit Edge Industrial 5.0
A full training-and-serving accelerator for sovereign data centres. 256GB HBM, in-fab interconnect.
Ships in Satchit Edge Server
04 Deployments
Reference topologies built on Satchit Edge and Vishwakarma silicon — on your hardware or ours.
Mode 01 · Your silicon
Own the Vishwakarma hardware. We co-design the edge node with you, and the full Sudarshana stack — compiler, runtime, quantizer — runs entirely inside your walls. Your data never leaves.
Buy the hardware · deterministic latency
Mode 02 · Our silicon
Skip the hardware. Send your workload to Sudarshana Cloud and pay only for the inferences you run — the same hallucination-free framework, billed as you use it.
Per-inference billing · zero capex
Reference topologies
05 Team
Hardware and software, designed in-house in Bengaluru.
CEO
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06 Contact
From a single board to a sovereign data centre, our team engineers the silicon and the software together. Tell us what you are trying to run at the edge.