A reliable foundation for building scalable intelligent solutions
Consistent reliability, safety, and cost control across all teams.
In cooperation with AWS, we will implement your proof-of-concept free of charge, create the infrastructure, and support the infrastructure costs for its validation.
years of experience
IT solutions
IT experts
What is AI Infrastructure & Trust?
For business (why you need it)
Without a platform that can handle models, data, and security, assistants and NL analytics end up in "demo mode." This approach gives IT and business a standardized way to launch and scale AI (HR/IT support, Data → Decisions, Smart digitization) with guaranteed performance and oversight.
- Quick transition from idea to operation: PoC → Pilot → Production without infrastructure modifications
- Predictable costs of AI: FinOps guardrails pre LLM spotrebu, transparentný cost-to-serve na use-case/tenant.
- Scaling across teams: The same rules for access, logging, and model quality for every new AI.
- AI trust and compliance without compromise: Quotes, reason codes, and RLS-safe responses eliminate hallucinations and the risk of data leakage.
- GDPR/AI Act, EU data residency, and AI intervention audits increase trust.
For technical teams (which form the AI infrastructure)
The platform connects data, model, and operational layers with an emphasis on security, scalability, and output quality monitoring.
- Model & Prompt Gateway: Unified access to AI models (including private deployment).
- Data/RAG Plane: Ingestion connectors (Applications/DMS/DB/BI), chunking + metadata, embedding services; hybrid retrieval (BM25 + vectors + rerank) with citations, RLS-safe data processing and exports.
- Serving Plane: API/App Gateway, serverless/containers, response streaming, caching (retrieval/responses), latency budgets, and backpressure.
- LLMOps & Quality: Prompt/model registry, curated evaluation sets, offline/online evaluations (groundedness, answerability), A/B and canary rollout; reason codes and labeling of AI outputs.
- Security & Compliance: IAM + RBAC/RLS/ABAC, encryption (KMS/Key Vault), EU data residency, audit trail of conversations and actions, DPIA/DPA; policies on the AI Act.
- Reliability & Cost: Multi-AZ/HA, DR (RTO/RPO), SLO/SLAs (e.g., P95 ≤ 6 s), FinOps (quotas, limits, chargeback), IaC, and portability (separate accounts/environments, Private Endpoints/VNET, "runs in client tenant").
Try out our AI infrastructure, ready for operation
Discover how standardized and secure AI infrastructure can accelerate solution deployment, reduce costs, and simplify model management in production. A brief consultation will show you how to build a scalable foundation for trustworthy AI without disrupting your existing infrastructure.
Key benefits of AI Infrastructure
Available innovations
Transforming ideas into PoC and verifying them in a real environment in a short time
Business agility
Flexible architecture adapting to new use cases
Predictable costs
Quotas, budgets, and telemetry FinOps
Reliable scaling
Automatic provisioning, serverless and container orchestration
Consistent environments
IaC templates and version control
Rapid deployment
Pre-made plans and instant delivery
Traditional operation vs. Smart AI infrastructure
- The unpredictable performance of AI models
- Infrastructure scaling requires manual intervention
- High operating costs for calculations
- Incoherent environments across teams
- Slow delivery of new business functionalities
- Weak flexibility for new AI use cases
- Implemented SLO, caching, and adaptive routing of models based on load and context
- Automated deployment via serverless and container orchestration
- Shared model funds, consumption optimization, and cost monitoring
- Standardization using IaC templates and CI/CD blueprints
- Rapid deployment via pre-prepared and tested pipelines
- Modular architecture enables safe and rapid experimentation
What problems does AI Infrastructure solve, and for whom?
CIO / CTO / IT Operations
ISSUE: they need a standardized AI chassis that does not rely on one-off projects.
EXAMPLE: Every team wants a different model and connector – without Gateway and RAG plane, chaos, inconsistent logging, and unsustainable costs arise.
Security / Compliance (CISO, DPO)
ISSUE: require proof of AI control – who could see what and what the AI did.
EXAMPLE: A response without citations or RLS-safe guarantees is unauditable; this is addressed by citations-first RAG, RBAC/RLS, and audit trail.
Business product owners
ISSUE: They want assistants or analytics to work quickly, consistently, and reliably.
EXAMPLE: Latency and hallucinations destroy adoption; SLO-driven serving, eval sets, reason codes, and fallback/handoff solve this.
Data / ML teams
ISSUE: They need prompt and model management, impact testing, and easy rollout.
EXAMPLE: Changing the prompt breaks production; this is solved by prompt/model registry, A/B/canary rollout, and policy-gated deployment.
Success stories of our clients
Our AI experts have successfully completed numerous AI projects of varying scope. Our most significant AI solutions include:
Transforming historical email archives into a Generative AI knowledge base
A large retail chain in the office supplies, electronics, and printing services segment has transformed its historical email...
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The implementation of Generative AI for Skill Management Platform Solvedio
The integration of Generative AI solutions into the platform Solvedio transformed the traditional management skills to a smart and contextually conscious system.
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Automating order classification using AI
A leading manufacturer of flexible packaging films in Central Europe ("Client") implemented an AI solution for automatic classification...
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AI Chatbot speeds up work with HR data
The implementation of an AI chatbot for HR reporting in the Azure environment provides fast and secure access to...
Read More ⮕FAQ
In AWS/Azure; in Azure also within your own tenant. The data will not be used to train public models.
Least-privilege IAM, RBAC/RLS/ABAC, encryption, PII redaction, audit trail, and regular security checks.
Eval sety, prahy kvality, online monitoring (acceptance/groundedness/latency), A/B a canary.
DR with RTO/RPO targets, backups, and practice restores; incident runbook and on-call.