SNWN Tech Solution engineers production-grade Generative AI platforms, custom Large Language Models, scalable MLOps pipelines, and intelligent Robotic Process Automation for modern enterprises. We bridge the gap between experimental data science and mission-critical operations, embedding strict zero-trust security guardrails, real-time drift telemetry, and autonomous agent workflows to reduce costs and accelerate decision velocity.
Engineered for sub-second inference speed, factual groundedness, continuous 24/7 bot execution, and measurable enterprise cost reduction.
Accelerate token generation and predictive scoring using TensorRT-LLM, vLLM continuous batching, quantized weights, and semantic response caching architectures.
Eliminate model hallucinations through hybrid dense-sparse vector retrieval, cross-encoder reranking, knowledge graph grounding, and automated citation verification guardrails.
Combine unattended RPA bot fleets with intelligent document extraction and dynamic model routing to slash manual back-office labor.
Deploy isolated VPC and on-premises model enclaves with real-time PII redaction, customer-managed encryption keys, and zero data retention.
Fragile Jupyter notebooks and isolated chatbot demos fail under concurrent enterprise traffic, noisy production data, and strict latency requirements.
Production MLOps & LLMOps: Containerized microservices, automated Kubeflow/MLflow pipelines, and elastic GPU serving clusters engineered for 99.99% uptime.
Generic foundation models fabricate unverified facts and misinterpret domain terminology, eroding executive trust and creating severe compliance liabilities.
Grounded Agentic RAG: Hybrid semantic-keyword indexing, graph grounding, and deterministic NeMo Guardrails enforcing verifiable source citations on every response.
Sending proprietary source code, financial records, and customer PII to public APIs exposes enterprises to data leaks and jailbreaks.
Zero-Trust AI Enclaves: Private VPC hosting, automated PII masking, input/output firewall sanitization, and role-based access control across vector stores.
Legacy screen-scraping macros break repeatedly whenever target application interfaces change slightly or unstructured documents deviate from rigid templates.
Cognitive RPA & IDP: Self-healing computer vision bots paired with deep learning OCR and LLM reasoning to process unstructured exceptions.
Routing every routine query to frontier models and running unoptimized training clusters causes unpredictable, skyrocketing monthly cloud compute bills.
FinOps Model Cascading: LoRA fine-tuned Small Language Models, 4-bit quantization, spot-instance orchestration, and semantic caching cutting inference spend.
Architect enterprise RAG pipelines, multi-agent systems, and NeMo security guardrails connecting foundation models to proprietary business data securely.
Deploy time-series forecasting, real-time fraud detection, recommendation engines, and computer vision models backed by unified online feature stores.
Orchestrate attended and unattended enterprise bot fleets across UiPath, Automation Anywhere, and Power Automate with cognitive document processing.
Build multilingual voice and text virtual assistants, sentiment telemetry engines, and autonomous tool-calling agents integrated with CRM/ERP backends.
Execute distributed multi-GPU training, LoRA/QLoRA model fine-tuning, structural quantization, and automated drift monitoring across Kubeflow, Ray, and MLflow.
Eliminate manual operational toil through event-driven AIOps remediation, GitOps CI/CD delivery pipelines, and declarative Terraform cloud infrastructure automation.
Quality Management System
Information Security Management
Azure Solutions Architect
Solutions Architect & CloudOps
RHCE Linux Enterprise
Certified Systems Professional
Certified Administrator
Software Testing & Automation
Quality Management System
Information Security Management
Azure Solutions Architect
Solutions Architect & CloudOps
RHCE Linux Enterprise
Certified Systems Professional
Certified Administrator
Software Testing & Automation
Audit enterprise workflows, evaluate data readiness, quantify token and compute economics, and prioritize high-ROI AI and automation opportunities.
Build automated ingestion pipelines, curate clean training datasets, provision vector databases, and establish role-based data governance boundaries.
Fine-tune domain models using LoRA/QLoRA, architect hybrid RAG retrieval graphs, and develop cognitive RPA and multi-agent workflows.
Stress-test inference latency, execute adversarial prompt-injection red-teaming, validate hallucination suppression, and verify end-to-end bot accuracy.
Deploy containerized endpoints via automated CI/CD, monitor real-time data and concept drift, and enforce continuous FinOps compute optimization.
We deploy models inside isolated Virtual Private Cloud (VPC) or on-premises enclaves with strict Zero Data Retention agreements. To prevent hallucinations, we combine hybrid dense-sparse RAG pipelines and cross-encoder reranking with NVIDIA NeMo Guardrails that enforce deterministic fact-checking and require verifiable source citations before returning any answer.
Retrieval-Augmented Generation (RAG) is ideal when models need real-time access to dynamic enterprise documents with strict access controls. Fine-tuning open-weights models (like Llama 3 or Mistral via LoRA/QLoRA) is best when you need specialized domain syntax, strict data sovereignty, sub-100ms latency, or 40% to 60% lower unit inference costs at high volume.
Traditional RPA excels at deterministic, rule-based UI and API actions, while Generative AI handles unstructured reasoning. We integrate LLMs and Intelligent Document Processing (IDP) directly into UiPath, Automation Anywhere, and Camunda workflows so bots can parse complex contracts, classify emails, and resolve exceptions autonomously with human-in-the-loop oversight.
Every production deployment includes automated MLOps and LLMOps observability. We track real-time latency, token spend, feature distribution shifts, and concept drift. When accuracy metrics deviate beyond defined thresholds, our pipelines automatically trigger shadow evaluations and model retraining workflows.
Partner with SNWN Tech Solution's AI architects, MLOps engineers, and automation specialists to turn complex enterprise data and manual workflows into measurable competitive advantage.
