AI in production
We build on frontier models — GPT, Claude, Gemini — and deploy private open-weight LLMs where data has to stay in-house. Agents that perform real workflow steps, grounded in your data — sourced, cited, governed.
Frontier models
We build on the models your customers already trust — OpenAI, Anthropic’s Claude, Google Gemini — with private open-weight deployment when data has to stay on your own infrastructure.
Autonomous agents
Tool-using agents wired into operational systems — retrieval, scheduling, ticketing, fulfilment. Human-in-the-loop where it matters, autonomous where it doesn’t.
RAG · governance
Retrieval over your documents, citations on every answer, access controls inherited from your existing auth. Every answer is sourced and traceable — nothing ungrounded.
The chat on this site is a production agent — the same engineering we build into client platforms. Ask it to scope your use case.
Quick answer
What AI engineering does Seypro deliver? Seypro builds production AI systems on the frontier models clients recognise — OpenAI’s GPT & Codex, Anthropic’s Claude, and Google Gemini — plus private open-weight LLM deployment, autonomous agents, RAG pipelines, and MLOps infrastructure. We built the AI-powered chat agent on sey.pro and integrate AI automation into client platforms — CMS content management, dynamic pricing engines, and workflow automation. Your infrastructure, your models, full audit trails.
The models we build on
Plus private, open-weight models on your own infrastructure when data has to stay in-house.
Most businesses don’t need more AI tools. They need AI that works inside their operations — agents that orchestrate multi-step workflows and RAG systems that search internal knowledge. We build on the frontier models your customers already trust — OpenAI’s GPT & Codex, Anthropic’s Claude, and Google Gemini — with private open-weight LLMs (Llama, Mistral) running on your own infrastructure via Ollama and vLLM when data sovereignty demands it. We build the MLOps infrastructure— AWS SageMaker, Bedrock, model registries, CI/CD for ML pipelines — so your models run in production, not in notebooks. Read how we build with Claude and safeguard it for clients.
The EU AI Act is now enforceable law. We’ve built a governance and ethics practice for exactly this. EU AI Act readiness, risk classification, bias detection, explainability reporting, model audit trails — the same rigor we bring to security and compliance, applied to your AI deployments. Your AI is owned by you, explainable to regulators, customers, and your board, and documented for the auditors who will review it.
Capabilities
Infrastructure, applications, governance. Four disciplines covering the lifecycle of production AI.
GPT & Codex (OpenAI), Claude (Anthropic), and Gemini (Google) wired into your product — the models your customers already trust. Open-weight (Llama, Mistral) on your own infra when data has to stay in-house.
Tool-using agents wired into your operational systems. Multi-step reasoning, function calling, human-in-the-loop where it matters.
Retrieval over your documents, codebases, knowledge. Hybrid search. Source citations on every answer. Access controls inherited from your auth.
EU AI Act readiness, bias testing, explainability, model audit trails. AI you can defend to regulators, customers, and your board.
Infrastructure & MLOps
Models are the easy part. Serving, monitoring, retraining, and scaling them is where most teams stall.
We configure the serving layer for production traffic — not demo loads. Open-source models on your own GPUs, or managed cloud endpoints: we’ve built both.
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.
Governance
The EU AI Act is law. If your systems can’t be audited, documented, and explained — you have a liability, not a product.
for prohibited AI practices under the EU AI Act
for standalone high-risk AI systems under the EU AI Act (Annex III)
from minimal to unacceptable — each with different obligations
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.
Where it lands
Deployment patterns we build for production teams across four verticals.
How we work
Your models run on your own servers — no third-party data access, no egress. 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.
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.
Basic automation (chatbot, FAQ agent): $5K-$15K. RAG systems with private data: $15K-$40K. Custom AI agents with workflow automation: $30K-$80K+. Private LLM deployment: $40K-$100K+. Every project gets a scoped proposal — these ranges give you a starting point. Ongoing API/hosting costs are separate.
No - AI augments, not replaces. Handles the majority of routine inquiries (FAQs, bookings, tracking). Your team focuses on complex issues and relationships. Force multiplication.
RAG (Retrieval-Augmented Generation) connects an AI model to your private data — documents, databases, knowledge bases. Instead of hallucinating, the AI retrieves real facts before answering. You need RAG when: your team wastes time searching internal docs, customers ask repetitive questions, or you need AI that knows your specific business context.
Yes. We deploy private LLMs (Llama, Mistral, Qwen) on your infrastructure — AWS, Azure, GCP, or on-premise. No data leaves your network. This is critical for regulated industries (finance, healthcare, legal) where sending data to OpenAI or Anthropic isn't an option.
Chatbots follow scripted flows and answer questions. AI agents take actions — they read databases, call APIs, make decisions, and execute multi-step workflows autonomously. An agent can process an insurance claim end-to-end; a chatbot can only answer questions about the process.
The EU AI Act regulates AI systems by risk tier. High-risk AI (hiring tools, credit scoring, medical devices) requires conformity assessments, documentation, and human oversight. If you deploy AI in the EU or serve EU customers, you likely need compliance. We help classify your AI systems by risk tier and implement required safeguards.
Basic chatbot: 2-3 weeks. Advanced with integrations: 4-8 weeks. Predictive analytics: 6-12 weeks. Includes training, testing, deployment.
Ollama vs vLLM, RAG architecture, cost vs. cloud APIs, data sovereignty.
GDPR, EU AI Act, infrastructure hardening — the regulatory side of AI.
Where AI lives — inside the applications we build, not bolted on after.
Visibility in ChatGPT, Perplexity, AI Overviews — the next search surface.
