AI systems & automation
Private deployment, workflow orchestration, governance, and production-grade operations.
Private LLMs, autonomous agents, RAG systems, MLOps infrastructure, and governance — built on your stack, owned by you.
Seypro builds production AI systems — private LLM deployment (Llama, Mistral, Qwen), autonomous agents, RAG pipelines, MLOps infrastructure on AWS SageMaker and Bedrock, and governance frameworks for the EU AI Act. Your infrastructure, your models, full audit trails.
Powered By Leading AI Platforms
Most businesses don't need more AI tools. They need AI that works inside their operations — agents that orchestrate multi-step workflows, RAG systems that search internal knowledge, and private LLMs (Llama, Mistral, Qwen) running on your own infrastructure via Ollama and vLLM. We build the MLOps infrastructure — AWS SageMaker, Bedrock, model registries, CI/CD for ML pipelines — so your models run in production, not in notebooks.
As AI becomes a regulatory concern, we've built a governance and ethics practice around it. EU AI Act readiness, risk classification, bias detection, explainability reporting, model audit trails — the same rigor we apply to security and compliance work, applied to your AI deployments. Your AI is owned by you, explainable to your stakeholders, and documented for the people who'll ask hard questions about it.
Eight disciplines — from infrastructure to governance — covering the full lifecycle of production AI.
Run Llama, Mistral, or Qwen on your own infrastructure. No data leaves your network. No per-token costs. Full control over model behavior, fine-tuning, and access.
Best for teams with privacy, cost, or sovereignty constraints.
Autonomous agents that execute multi-step workflows — processing documents, coordinating across systems, making decisions within defined guardrails.
Best when repetitive work already spans people, systems, and approvals.
Retrieval-augmented generation over your internal documents, codebases, and knowledge. Semantic search that understands intent, not just keywords.
Best when answers need to be grounded in your own documents and systems.
Production-grade model serving, monitoring, and lifecycle management. AWS SageMaker, Bedrock, and open-source MLOps tooling — configured for your scale.
Best when experimentation exists but production operations are the real blocker.
EU AI Act readiness, risk classification, conformity documentation, and ongoing compliance monitoring. For organizations deploying AI in regulated environments.
Best for regulated deployments or teams that expect executive and legal scrutiny.
Bias detection, fairness testing, and explainability reporting. Ensure your models make decisions you can defend — to regulators, to customers, to your board.
Best when model outputs affect trust, fairness, or regulated decisions.
Custom machine learning models for demand forecasting, pricing optimization, churn prediction, and anomaly detection. Trained on your data, deployed in your stack.
Best when you already have usable historical data and a measurable commercial decision to improve.
Automated extraction, classification, and routing of invoices, contracts, forms, and unstructured documents. OCR, NLP, and structured output.
Best when teams are buried under repetitive documents and manual review.
Infrastructure & MLOps
Models are the easy part. The infrastructure to serve, monitor, retrain, and scale them is where most teams stall. We build the platform so your AI actually ships.
Whether you're running open-source models on your own GPUs or using managed services, we configure the infrastructure to handle production traffic — not demo loads.
Training a model once isn't a product. We build the pipelines to version, retrain, evaluate, and deploy models continuously — with the same rigor as software CI/CD.
Production tooling, not proof-of-concept stacks.
AI Ethics & Governance
The EU AI Act is law. Enforcement is underway. If your AI systems can't be audited, documented, and explained — you have a liability, not a product.
The Act classifies AI systems by risk level — from banned practices to minimal-risk tools. We map your AI deployments to the right tier and build the documentation, processes, and technical controls to match.
When regulators, clients, or your own board ask how a model made a decision — you need an answer. We build the audit infrastructure so every prediction, recommendation, and classification is traceable.
Every AI system falls into one of four categories. The obligations scale with the risk.
Common applications across finance, hospitality, e-commerce, and operations.
Privacy-first. Governance-ready. Your infrastructure, your rules.
Local LLM deployment means your data never leaves your infrastructure. GDPR compliant by design.
Every deployment includes audit trails, explainability, and documentation to meet regulatory standards — including the EU AI Act.
We integrate AI into your existing systems — CRM, ERP, content pipelines — as a core capability, not a side tool.
Financial platforms, securities exchanges, enterprise infrastructure. We understand what it means to build AI for industries that can't afford failure.
Everything you need to know about implementing AI in your business
Custom AI agents, private LLM deployment, RAG systems for knowledge retrieval, predictive analytics, workflow automation, ML models for recommendations, and intelligent search. From simple FAQ automation to complex multi-step decision engines.
No - AI augments, not replaces. Handles the majority of routine inquiries (FAQs, bookings, tracking). Your team focuses on complex issues and relationships. Force multiplication.
Basic chatbot: 2-3 weeks. Advanced with integrations: 4-8 weeks. Predictive analytics: 6-12 weeks. Includes training, testing, deployment.
Tell us what you're trying to automate, build, or govern. We'll tell you what's realistic — and if we're not the right fit, we'll say so.
