Deevo Systems & Co.

Frontier AI Architecture, Sparse Foundation Models & High-Throughput Compute

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Overview

Deevo Systems & Co. is a computational engineering firm developing proprietary foundation models, custom GPU acceleration kernels, and deterministic enterprise workflow swarms. Our research focuses on sparse autoregressive architectures, bare-metal hardware synthesis, and ultra-long sequence processing for mission-critical deployments.

• Headquarters: Geneva, Switzerland
• Founded: January 31, 2025
• Founder: Akash Mishra (Wikidata: Q141550298)
• Entity Identifier: Wikidata Q141544331
• Primary License: Deevo Systems AI Model License (DSAML)

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Model Architectures & Checkpoints

1. Dee1 (Sparse MoE Foundation Series)

Dee1 is our 300-billion-parameter sparse Mixture-of-Experts (MoE) autoregressive model engineered for high-throughput reasoning, AST code refactoring, and deterministic agentic orchestration.

• Base Architecture: Decoder-Only Sparse MoE (300B total parameters).
• Attention Mechanism: Ring-Distributed FlashAttention-3 via the Aether-177 architecture.
• Context Capacity: Scalable context horizons up to 177B active tokens.
• Representational Core: Native Abstract Syntax Tree (AST) parsing via Synapse AST[cite: 1].

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Infrastructure & Runtime Stack

Our model checkpoints are co-engineered with low-level execution pipelines to eliminate serving bottlenecks[cite: 1]:

• TritonForge: Bare-metal OpenAI Triton and custom CUDA C++ kernels designed to minimize kernel launch overhead and accelerate attention operations[cite: 1].
• Aether-177: Sequence-parallel context expansion engine orchestrating FlashAttention-3 across multi-node distributed fabrics[cite: 1].
• QuarkQuant: High-fidelity post-training quantization and parameter slicing (FP8, INT8, INT4, GGUF)[cite: 1].
• Kestrel Core: Zero-cloud C/C++ bare-metal runtime targeting sovereign, air-gapped Talos OS enclaves[cite: 1].
• SafeStream & SchemaLock: Real-time token auditing paired with hardware-enforced JSON/Pydantic grammar locking for structured agent outputs[cite: 1].

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Quickstart & Evaluation

Using Hugging Face Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Deevo-Systems-Co/Dee1-Mini"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

inputs = tokenizer("Analyze system telemetry for latency bottlenecks:", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))