Bridging 26 years of Engineering with Agentic AI.
\n \n\n Serving as a Technical Force Multiplier for organizations requiring high-stakes AI implementation—from medical diagnostics to autonomous agricultural logic.\n
\n\n Most AI today is built as a \"wrapper\" around third-party APIs. At Sofact, we architect Ground-Up Intelligence. We focus on Private LLMs and Computer Vision systems that protect data sovereignty.\n
\n\n By providing End-to-End Technical Leadership, I enable mid-market firms to launch complex AI products without the massive overhead of a multi-person engineering department.\n
\nConverting massive, expensive LLMs into lean, high-speed Small Language Models (SLMs) optimized for specific institutional tasks and on-premise deployment.
\nFine-tuning encoders and embedding models to ensure 99%+ accuracy in retrieval-augmented generation for medical, legal, and engineering datasets.
\nArchitecting autonomous agents that reason, execute workflows, and manage IoT sensors entirely within your air-gapped secure network.
\n\n Transitioning from probabilistic chat to deterministic institutional intelligence. Our workflow orchestrates the entire lifecycle of a sovereign AI agent.\n
\nThe entry gate for all telemetry. We sanitize inputs through a multi-layer validation stack before logic execution.
\nUtilizing Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Graph-of-Thought (GoT) for recursive problem solving.
\nKnowledge Distillation: extracting weights from LLMs to fine-tune high-speed, local Small Language Models (SLM).
\nMaintaining workflow state via Hybrid Vector Stores and local KV-caching for near-zero latency.
\nRetrieval is optimized via RAPTOR (Recursive Abstraction) and CRAG (Corrective RAG) for fact-checking. Self-RAG protocols ensure the agent critiques its own source quality before responding.
\nThe agent interacts with the physical and digital world. From SQL generation to Private API calls and secure Sandboxed Code Execution.
\nContinuous monitoring via Langfuse/LangSmith feeds a sovereign feedback loop. The system evolves through automated DPO (Direct Preference Optimization) and QLoRA fine-tuning cycles.
\nA repository of production-ready AI concepts across 25+ specialized domains. From Agriculture drone logic to CyberSecurity anomaly detection.
\n \nWeed/Pest Vision
\nTactical Object Detection
\nUrban Flow Optimization
\nNeural Threat Defense
\nCivic Infrastructure Audit
\nDocument Intelligence RAG
\nVisual Search & Trends
\nReal-time Anomaly Alerts
\n\n \"The Hundred Physicians\" — A proprietary multimodal Medical Intelligence engine.\n
\n\n Shatabhisha-M automates clinical pathology analysis using SSD-based object detection and Vision Transformers. Optimized for NVIDIA Jetson Edge, it enables real-time diagnostic assistance for oncology, radiology, and retinal health in secure environments.\n
\nEvaluating feasibility and designing the technical roadmap for AI integration into legacy business logic with a focus on institutional cost-reduction.
\nBuilding the full production stack—Backend, Custom Models, and Frontends—in high-velocity 6-8 week engineering cycles.
\nHandling high-stakes algorithmic due diligence and cross-border vendor negotiations for global expansion and regulatory compliance.
\n\n We maintain an independent R&D lab for Data Sovereignty. Equipped with NVIDIA RTX 4090 clusters and Jetson Edge nodes, we develop and test private AI models completely offline before client deployment.\n
\nInitiate institutional orchestration.
\n \n Request Technical Briefing\n \nGlobal Delivery Framework // Established 1999
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