AI systems & automation

AI, wired intoyour operations.

Private deployment, workflow orchestration, governance, and production-grade operations.

Private LLMs, autonomous agents, RAG systems, MLOps, governance. Engineered into the software you already run — not bolted on.

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AI in production

Agents that act. Models that don't leak.

We deploy private LLMs on the customer's own infrastructure, build agents that perform real workflow steps, and ground them in the customer's data — sourced, cited, governed.

Private LLMs

Llama · Mistral · Qwen on your stack.

Open-weight models deployed on customer infra. No third-party data egress, no per-token vendor lock-in, no surprise model deprecations.

vLLMllama.cppBedrockSageMaker

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.

Tool useFunction callingEval harnessHITL

RAG · governance

Grounded in your knowledge.

Retrieval over the customer's documents, citations on every answer, access controls inherited from their existing auth. Governance built in, not bolted on.

pgvectorHybrid searchCitationsEU AI Act

You're talking to one of these right now — for a scoped quote.

Seypro builds production AI systems — private LLM deployment (Llama, Mistral, Qwen), autonomous agents, RAG pipelines, and MLOps infrastructure. We built the AI-powered chat agent on sey.pro and implement AI automation across client platforms including CMS content management, dynamic pricing engines, and workflow automation. Your infrastructure, your models, full audit trails.

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.

Capabilities

The full AI stack. One team.

Infrastructure, applications, governance. Four disciplines covering the lifecycle of production AI.

Infrastructure4–10 weeks

Private LLM deployment.

Llama, Mistral, Qwen on your own infrastructure. No data egress. No per-token bill. No vendor lock-in.

OllamavLLMGPU tuningFine-tuningSageMakerBedrock

Applications4–8 weeks

Agents that act.

Tool-using agents wired into your operational systems. Multi-step reasoning, function calling, human-in-the-loop where it matters.

Tool useFunction callingCRM & ERPHITLEval harnessDocument AI

Retrieval3–6 weeks

Grounded knowledge.

Retrieval over your documents, codebases, knowledge. Hybrid search. Source citations on every answer. Access controls inherited from your auth.

pgvectorPineconeHybrid searchCitationsChunking pipelines

Governance4–8 weeks

Audit-ready by default.

EU AI Act readiness, bias testing, explainability, model audit trails. AI you can defend to regulators, customers, and your board.

EU AI ActRisk tieringSHAP & LIMEDrift monitoringConformity docs

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

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.

AWS SageMaker & Bedrock
Managed model hosting, fine-tuning endpoints, and foundation model access
vLLM & TGI serving
High-throughput inference for open-source models with batched requests
GPU optimization
CUDA, multi-GPU, quantization (GGUF, GPTQ, AWQ) for cost-efficient inference
Auto-scaling & load balancing
Scale 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 pipelines
Automated training, evaluation, and deployment on data or code changes
Drift detection & alerting
Automated alerts when input distributions or model performance degrades
Cost optimization
Right-sizing instances, spot/reserved capacity, model distillation to cut serving costs

Infrastructure we deploy on

Production tooling, not proof-of-concept stacks.

AWS SageMakerAmazon BedrockvLLMMLflowWeights & BiasesOllamaPineconepgvector

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.

€35M

maximum fine

for prohibited AI practices under the EU AI Act

Aug 2026

compliance deadline

for high-risk AI systems under the EU AI Act (Article 6)

risk tiers

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 analysis
Map every AI system to its regulatory tier — unacceptable, high, limited, or minimal
Conformity assessment preparation
Technical documentation, data governance records, and quality management systems
Transparency & disclosure obligations
User-facing disclosures, AI-generated content labelling, interaction notices
Human oversight mechanisms
Kill 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 trails
Versioned logs of training data, parameters, outputs, and decision rationale
Bias detection & fairness testing
Statistical fairness metrics across protected groups — before deployment, not after incidents
Explainability reporting
SHAP values, feature importance, and plain-language explanations for non-technical stakeholders
Continuous monitoring & drift detection
Automated 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.

Unacceptable Risk

Social scoring, real-time biometric surveillance, manipulative AI. Banned outright.

Prohibited

High Risk

Credit scoring, recruitment AI, medical devices, critical infrastructure. Full conformity assessment required.

Heavy obligations

Limited Risk

Chatbots, AI-generated content, emotion recognition. Transparency obligations — users must know they are interacting with AI.

Transparency required

Minimal Risk

Spam filters, AI-assisted games, inventory management. No specific obligations — voluntary codes of conduct.

Self-regulated

Talk to us about AI governance

Where it lands

Finance. Hospitality. Retail. Healthcare.

Common deployment patterns we've shipped — or scoped — for production teams.

Tourism & Hospitality

AI chatbot booking assistant with high automation
Dynamic pricing for hotel rooms to maximize revenue
Guest sentiment analysis (TripAdvisor/reviews)
Tour recommendation engine (personalized itineraries)

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 to boost conversions
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

Privacy first. Governance-ready. Your infrastructure, your rules.

100% Data Privacy

Local LLM deployment means your data never leaves your infrastructure. 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.

Before you ask.

What AI services do you provide for businesses?

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.

What does AI implementation cost?

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.

Will AI replace my customer service team?

No - AI augments, not replaces. Handles the majority of routine inquiries (FAQs, bookings, tracking). Your team focuses on complex issues and relationships. Force multiplication.

What is a RAG system and when do I need one?

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.

Can you deploy AI models on our own servers?

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.

How do AI agents differ from chatbots?

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.

What is the EU AI Act and how does it affect my business?

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.

How long does AI implementation take?

Basic chatbot: 2-3 weeks. Advanced with integrations: 4-8 weeks. Predictive analytics: 6-12 weeks. Includes training, testing, deployment.

Keep reading

Insight

Have a real AI problem?

Tell us what you're automating, building, or governing. We'll tell you what's realistic — and if we're not the fit, we'll say so.

Scope your project See our work