// PLATFORM

One stack,
co-designed end to end.

Most AI companies optimize one layer of the stack. We co-designed all five — so every byte, every cycle, every watt is accounted for. Krsna silicon. EdgeMatrix runtime. Shakti models. LingoForge orchestration. Production applications.

Stack Layers
5
SoC Variants
5
Models Released
6
Apps Supported
193
// THE STACK, VISUALIZED

Five layers, stacked.

From the silicon at L01 to the applications at L05 — each layer co-designed with the one above and below.

DATA FLOWL05EnterpriseAPPLICATIONS / APIS21+ENTERPRISES ONBOARDEDL04Foundation ModelsSHAKTI LLM & LEXICONS3×SMALLER THAN PEERSL03PlatformLINGOFORGE — LLM STUDIO193MODELS SUPPORTEDL02EdgeMatrixCOMPILER + INFERENCE ENGINE+73%TOKENS / SEC VS VLLML01Krsna SoCSILICON / EXSLERATE IP5SOC VARIANTS
Each layer ships independently but is co-designed with its neighbors. The metrics on the right are the headline performance signal at that layer.
L05
EnterpriseAPPLICATIONS / APIs
Lingo · IRA · HaluMon · LingoForge · TXTR
Production-grade applications embedded into enterprise workflows via API or full integration.
21+
ENTERPRISES ONBOARDED
L04
Foundation ModelsSHAKTI LLM & LEXICONS
Shakti family · Lexicons · open-weight on HuggingFace
Compact, capable, deployable models from 100M to 4B parameters — with 8B and 30B in flight.
3×
SMALLER THAN PEERS
L03
PlatformLINGOFORGE — LLM STUDIO
Data · model · workflow orchestration
Centralized fine-tuning, deployment, and lifecycle management across edge environments.
193
MODELS SUPPORTED
L02
EdgeMatrixCOMPILER + INFERENCE ENGINE
CORE (compiler + runtime engine) · EdgeFlow (inference engine)
Two layers: CORE handles compilation + runtime orchestration. EdgeFlow is the inference engine where models execute — across NVIDIA, AMD, Intel, ARM, NPU, FPGA.
+73%
TOKENS / SEC vs vLLM
L01
Krsna SoCSILICON / ExSLerate IP
Apex · Surge · Pulse · Flux · Lite
Custom AI accelerator IP — five variants covering wearables to data center inference.
5
SoC VARIANTS
// THE CO-DESIGN PRINCIPLE

Layer-by-layer optimization is a cost center.
Stack-level co-design is a moat.

When the compiler knows the chip, the runtime knows the model, and the model knows the use case, you get compounding gains — not 5% improvements, but 3× and 4× ones. Every product decision below is a co-design decision.

Stitched stackFIVE VENDORS. FIVE CONTRACTS. FIVE INTEGRATION POINTS.AI chip vendoropaque ISA, slow kernel updatesInference frameworkgeneric, not chip-tunedLLM APIrented tokens, no fine-tuneOrchestration platformseparate vendor, separate billApplication layerglued via APIs, latency stacksCo-designed stackONE TEAM. ONE BILL. ONE RUNTIME.Krsna SoCL01 — silicon designed for the modelEdgeMatrixL02 — runtime designed for the siliconShakti / LexiconsL03 — models tuned for the runtimeLingoForgeL04 — orchestration in-houseLingo / IRA / etc.L05 — applications on shared infra
Co-design compounds. Layer-by-layer optimization gets 5% gains; stack-level co-design gets 3–4× ones.
PRINCIPLE / 01

Co-design over over-build

Every layer is engineered with awareness of the layers above and below. The compiler knows the chip. The runtime knows the model. The model knows the use case.

PRINCIPLE / 02

Sovereignty by default

On-prem, air-gapped, edge — these are not deployment options bolted on later. They are the design starting point for every product we ship.

PRINCIPLE / 03

Compounding knowledge

We've kept the core team together for 5+ years. Every project teaches the next one. Eight years of compound learning is our moat against single-layer giants.

PRINCIPLE / 04

Cost-per-token is the only KPI

The market does not care about flops or parameters. It cares about predictable inference economics. Every layer is optimized for fewer cycles, lower watts, smaller footprints.

// WHAT THE STACK UNLOCKS

Two operational outcomes. Both measurable.

// OUTCOME 01 · COVERAGE

Any AI · Any silicon →

8+ model architecture families (Transformers, Mamba, RWKV, LFMs, CNNs, MoE, VLMs, Diffusion) running on 6 hardware platforms — through one unified runtime, the Infinite Series Engine.

See the compatibility matrix →

// OUTCOME 02 · ECONOMICS

Token economics →

Cost-per-token is the only KPI that matters in production AI. Stack co-design unlocks +73% throughput vs vLLM on L40s — and 40% cost reduction on inference workloads.

See the token economy →

// LET'S BUILD

Build on the whole stack.

Schedule architecture reviewBrowse documentation