Technology & Engineering
Technology follows the requirement — not the other way around.
From Hardware to Intelligence
We follow the problem through the entire technology stack: Sensors → Networks → Compute → OS → Data → Software → AI → Integration → Monitoring → Action. We don't stop at artificial technical boundaries. Existing customer ecosystems and infrastructure are valid engineering inputs; we build on suitable environments rather than introduce unnecessary technology.
Modernizing Capabilities, Not Equipment
Existing technology is not obsolete because it lacks modern intelligence. We bridge generations, connecting legacy systems with current AI and data processing where it is technically and economically preferable to replacement.
Engineering Principles over Tooling
The building blocks may vary, but the engineering principles do not. We connect cameras, sensors, monitoring systems, databases, applications, files, cloud services and AI through appropriate interfaces, protocols, gateways and software to turn individual components into engineered systems.
From Concept to Working System
We go beyond consulting. Our process spans Design, Selection, Sourcing, Building, Integration, and Deployment. This is not a mandatory linear process; depending on the project, we may start with existing infrastructure, a blank sheet, or some projects require only software or integration, while others extend into hardware, networks, sensors or complete systems.
A Look Under the Hood
Model Selection
The best model is the one that fits the task. We evaluate GPT, Gemma, Qwen, Llama, and local models based on context window, latency, inference cost, VRAM, quantization, multimodality, privacy, availability and integration effort.
Computational Core
Modern AI transforms inputs into numerical representations. A substantial part of the computational workload consists of large-scale matrix multiplications executed on GPUs and other accelerators. These technical parameters directly affect model size, context, memory consumption, and latency.
AI Imperfection
AI is powerful. AI is imperfect. Both are true. Misclassifications, hallucinations and contextual errors are capabilities and limitations that must be considered and accounted for in system engineering.
Deterministic Boundaries for Agentic AI
AI agents can reason, plan and operate tools — but capability does not imply authority.
Scelaris does not treat prompts or model memory as security boundaries. Critical operational rules should remain enforceable even when context is incomplete or a model makes the wrong decision.
Permissions, execution controls, approval gates and independent verification create deterministic boundaries around non-deterministic intelligence.
Memory is not policy. Prompts define intent. Architecture defines what can actually happen.
Open where it helps. Commercial where it makes sense. Custom where it is needed.
We combine open-source technologies, commercial products, cloud services and custom development according to the requirements of the solution.
The Scelaris Toolbox
AI & Models
- GPT, Gemma, Qwen, Llama
- LLMs, VLMs, RAG
- Local & Cloud Models
Software & Data
- Python, SQL, PostgreSQL
- REST, JSON, MQTT, OPC UA
- Vector Search, Automation
Infrastructure
- Linux, Windows, Zabbix
- Virtualization, VPN, Edge
- Telemetry & Logging
Compute & Hardware
- CPU, GPU, CUDA, VRAM
- NVMe, RAID, Ethernet, 5G
- Cameras, Sensors, Gateways
The list changes. The principle does not.
Resilience & Observability
Systems fail. Good architectures expect them to. We engineer resilience according to the consequence, using Zabbix, caching, queues, retries, buffering, health checks, backups and failover.
Architecture-First Security
Security is part of the architecture, not an afterthought. We address both IT security (VPN, ACL, Encryption) and AI security, including prompt injection, context and RAG data, tool permissions, agents and external AI services.
Responsible Deployment
AI and automation are implemented in accordance with applicable legal and contractual requirements and with safeguards appropriate to the use case.
Technical depth where it matters
Not familiar with every term? Ask us. Understanding technology is part of using it well, and we connect this naturally to our coaching and enablement services.