AI in production
Agents that act. Models that don’t leak.
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
GPT · Claude · Gemini, integrated.
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
Workflows, not just chat.
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
Grounded in your knowledge.
Retrieval over your documents, citations on every answer, access controls inherited from your existing auth. Every answer is sourced and traceable — nothing ungrounded.
You’re talking to one of these right now.
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
The full AI stack. One team.
Infrastructure, applications, governance. Four disciplines covering the lifecycle of production AI.
Frontier models, integrated.
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.
Agents that act.
Tool-using agents wired into your operational systems. Multi-step reasoning, function calling, human-in-the-loop where it matters.
Grounded knowledge.
Retrieval over your documents, codebases, knowledge. Hybrid search. Source citations on every answer. Access controls inherited from your auth.
Audit-ready by default.
EU AI Act readiness, bias testing, explainability, model audit trails. AI you can defend to regulators, customers, and your board.
Infrastructure & MLOps
Production AI. Not notebook demos.
Models are the easy part. Serving, monitoring, retraining, and scaling them is where most teams stall.
Cloud & Model Serving
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.
- AWS SageMaker & BedrockManaged model hosting, fine-tuning endpoints, and foundation model access
- vLLM & TGI servingHigh-throughput inference for open-source models with batched requests
- GPU optimizationCUDA, multi-GPU, quantization (GGUF, GPTQ, AWQ) for cost-efficient inference
- Auto-scaling & load balancingScale with demand, not ahead of it — pay for what you use
ML Lifecycle & Monitoring
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.
- Model registries (MLflow, W&B)Versioned models with experiment tracking, lineage, and promotion workflows
- CI/CD for ML pipelinesAutomated training, evaluation, and deployment on data or code changes
- Drift detection & alertingAutomated alerts when input distributions or model performance degrades
- Cost optimizationRight-sizing instances, spot/reserved capacity, model distillation to cut serving costs
Infrastructure we deploy on
Production tooling, not proof-of-concept stacks.
Governance
AI you can explain to your board.
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
EU AI Act Compliance
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.
- Risk classification & gap analysisMap every AI system to its regulatory tier — unacceptable, high, limited, or minimal
- Conformity assessment preparationTechnical documentation, data governance records, and quality management systems
- Transparency & disclosure obligationsUser-facing disclosures, AI-generated content labelling, interaction notices
- Human oversight mechanismsKill switches, escalation protocols, and human-in-the-loop requirements for high-risk systems
AI Audit & Oversight
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.
- Model audit trailsVersioned logs of training data, parameters, outputs, and decision rationale
- Bias detection & fairness testingStatistical fairness metrics across protected groups — before deployment, not after incidents
- Explainability reportingSHAP values, feature importance, and plain-language explanations for non-technical stakeholders
- Continuous monitoring & drift detectionAutomated alerts when model performance degrades or output distributions shift
EU AI Act risk tiers
Every AI system falls into one of four categories. The obligations scale with the risk.
Where it lands
Finance. Hospitality. Retail. Healthcare.
Deployment patterns we build for production teams across four verticals.
Tourism & Hospitality
- AI chatbot booking assistant
- Dynamic pricing for hotel rooms to maximize revenue
- Guest sentiment analysis (TripAdvisor/reviews)
- Tour recommendation engine
Financial Services
- Fraud detection with real-time monitoring
- Credit risk assessment (AI scoring)
- Document processing (loan applications)
- Customer support chatbot (banking queries)
E-commerce & Retail
- Product recommendation AI
- Inventory forecasting to reduce overstock
- AI-generated product descriptions at scale
- Customer service chatbot (order tracking)
Healthcare
- Appointment scheduling chatbot (24/7)
- Medical record digitization (OCR)
- Patient triage AI (prioritize emergencies)
- Prescription processing automation
How we work
Private by design. Governed by default. Owned by you.
Data never leaves your infrastructure
Your models run on your own servers — no third-party data access, no egress. GDPR compliant by design.
Governance-Ready
Every deployment includes audit trails, explainability, and documentation to meet regulatory standards — including the EU AI Act.
Wired In, Not Bolted On
We integrate AI into your existing systems — CRM, ERP, content pipelines — as a core capability, not a side tool.
Regulated Industry Experience
Financial platforms, securities exchanges, enterprise infrastructure. We understand what it means to build AI for industries that can't afford failure.
FAQ
Before you ask.
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 chatbot: 2-3 weeks. Advanced with integrations: 4-8 weeks. Predictive analytics: 6-12 weeks. Includes training, testing, deployment.
Keep reading
Local LLMs for enterprise
Ollama vs vLLM, RAG architecture, cost vs. cloud APIs, data sovereignty.
Security & compliance
GDPR, EU AI Act, infrastructure hardening — the regulatory side of AI.
Software development
Where AI lives — inside the applications we build, not bolted on after.
SEO & GEO
Visibility in ChatGPT, Perplexity, AI Overviews — the next search surface.
What Recent Research Says About Shipping LLM Agents in Production
Four recent papers and product announcements on LLM agents reveal where the real engineering work sits: citation verification, prompt coordination, GUI grounding, and voice reliability.