A dependable foundation for intelligent solutions
Consistent reliability, security, and cost control across every team working with AI.
Leaders in automotive, banking, and telecommunications rely on our solutions
One platform, two perspectives
Why you need it
Without a platform that handles models, data, and security, AI assistants and analytics stay in demo mode. This gives IT and business teams a consistent way to run and scale AI, with predictable performance and proper oversight.
- From idea to production without rework: PoC, pilot, production
- Predictable AI costs, with visibility by use case
- The same access and quality rules for every new AI use case
- Citations, reason codes, and RLS-safe answers reduce hallucinations and the risk of data leakage
- Compliance with GDPR and the AI Act, EU data residency, and an audit record of AI actions
What the AI infrastructure consists of
The platform brings the data, model, and operations layers together, with an emphasis on security, scaling, and tracking output quality.
- A single entry point to models, including private deployments
- RAG with citations, hybrid retrieval, and RLS-safe data
- Serving: API gateway, serverless and containers, response streaming, caching, and latency budgets
- LLMOps: prompt and model registries, eval sets, A/B and canary releases
- IAM/RBAC/RLS, encryption, audit trail, Multi-AZ and DR, SLOs (for example P95 ≤ 6 s)
Key benefits
Innovation within reach
You turn ideas into a PoC and validate them quickly in a real environment.
Business agility
A flexible architecture adapts to new use cases.
Predictable costs
Quotas, budgets, and FinOps telemetry keep consumption under control.
Reliable scaling
Automated resource provisioning, serverless architectures, and container orchestration.
Consistent environments
IaC templates and version control across teams.
Fast deployment
Prebuilt and tested pipelines, with provisioning on demand.
Traditional operations vs. smart AI infrastructure
- AI model performance is unpredictable
- Scaling requires manual work
- High operating costs for compute
- Environments that differ from team to team
- New capabilities reach the business slowly
- Little flexibility for new AI use cases
- SLOs, caching, and model routing based on load
- Automatic deployment using serverless and containers
- Shared model pools and cost tracking
- Standardization through IaC templates and CI/CD
- Fast deployment using tested pipelines
- A modular architecture for safe experimentation
Who AI Infrastructure is for and what it solves
CIO / CTO / IT operations
They need a standard AI foundation that does not depend on one-off projects. Without a gateway and a RAG layer, the result is fragmented operations, inconsistent logging, and unsustainable costs.
Security and compliance (CISO, DPO)
They need evidence of effective AI controls: who could see what and what the AI did. An answer without citations or RLS-safe controls cannot be audited.
Business product owners
They want assistants and analytics to work quickly, consistently, and reliably. High latency and hallucinations undermine adoption.
Data and ML teams
They need prompt and model governance, impact testing, and a simple rollout. Prompt changes must not disrupt production.
Proven in practice
How Aspecta’s NLP solution is changing public procurement
Public procurement faces significant process and time inefficiencies. Multilabel classification algorithms, semantic similarity analysis, and large language models can deliver revolutionary improvements…
View case study
AI Smart Assistant on AWS: personalized shopping experience
+18 % online conversions
in the first six months after deployment
Turning historical email archives into a generative AI knowledge base
70 % shorter resolution time
from 10–15 minutes to 2–3 minutesQuestions we hear most often
Where do the models and the data run?
On AWS or another cloud. In a multi-cloud setup, they can also run in your own tenant. Your data is not used to train public models.
How do you handle security?
Least-privilege IAM, RBAC/RLS, encryption, redaction of sensitive data, an audit trail, and regular reviews.
How do you track AI quality?
Eval sets, quality thresholds, online monitoring of groundedness and latency, and A/B and canary rollouts.
What happens if there is an outage?
DR with RTO/RPO targets, backups and restore drills, an incident runbook, and on-call coverage.
Build a dependable AI foundation
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