About — ArcleIntelligence
Building the Future
of Private AI
Building fully offline, on-device, privacy-first AI — no cloud dependency, no data leaving the user's device. Intelligence that belongs entirely to you.
Fig. 1 — Founder Profile
The
Architect
Abhinav Anand
19 years old. Based in Katihar, Bihar, India. A self-taught developer and AI researcher building ArcleIntelligence from the ground up.
Architecting and training a custom 5.84 billion parameter native multimodal AI Omni model from the ground up. Built with an end-to-end omni-modal architecture capable of unified real-time reasoning across text, voice, and vision — featuring bespoke neural training pipelines, specialized tokenization, and highly optimized local inference kernels, all engineered without external funding.
Open benchmark results have been shared publicly on Reddit's r/indianstartups, demonstrating real performance metrics against established cloud-dependent models. The project has received media coverage for its ambitious scope and privacy-first approach.
Fig. 2 — Data Curation
The
Curator
Abhinav Kumar Singh
Responsible for the data Arcle V1 learned from. Sourcing, selecting and validating the training corpora behind every one of the model's seven capabilities.
The benchmark results are in large part his work. Curating the reasoning and mathematics data that produced 77.5% on GSM8K, stress-testing each dataset for quality, and filtering noise and duplication out before any of it reached the training run.
Also drives the evaluation side — assembling the sample sets behind Arcle's own harness, checking OCR and speech data against real documents and recordings, and shaping the Indian-language and India-knowledge corpora that make the model genuinely useful at home.
Fig. 3 — Data Matrix
Company
Parameters
| Parameter | Value |
|---|---|
| Founded | 2026 |
| Headquarters | Katihar, Bihar, India |
| Entity Type | Sole Proprietorship |
| Founder | Abhinav Anand |
| Team Size | 2 |
| Contact | lucifertkod2007aa@gmail.com |
| Core Product | Arcle V1 AI Omni Model |
| Technology Stack | Custom LLM · On-Device Inference · Custom Training Pipeline · AI Architecture Creator |
Fig. 4 — Development Timeline
Project
Milestones
Architecture Design
Core system architecture and model design finalized. Foundational decisions on inference engine, model topology, and on-device execution pipeline locked in.
Model Training
Custom LLM training pipeline built from scratch and initial models trained. End-to-end control over data curation, tokenization, and optimization for edge hardware.
Reddit Traction
Benchmark results shared on r/indianstartups, gaining significant community attention and validation from developers and AI enthusiasts across India.
The Training of the Model Actually Starts
Arcle V1 goes into training — 180 hours on a single GPU. Modality-grouped batching turns five tiny sub-batches per step into one full-width forward pass, length-bucketed sampling stops padding from wasting compute, and automatic checkpoint rotation lets the run survive interruption and resume on its own weights.
Evaluation & Release
Arcle V1 measured through its own evaluation harness at 400 samples per benchmark — 77.5% on GSM8K, 80.0% on ARC-Easy, 95% OCR content-word recall. LoRA rank and connector widths solved to land the parameter budget on 5,840,000,000 exactly, shipped as a single model file.