Agentic AI
Bounded agent roles, orchestration, tool permissions, typed handoffs, independent validation, human approval and fail-closed execution.
About
We build AI systems you can verify.
We design processes, automation, websites, mobile and desktop applications for macOS, Windows and Linux. Architecture, integrations and access are tailored to your organisation.
What we do
A system for employees.AI assistants for customers.
We build systems that support employees and AI assistants for sales and customer service. Each solution is tailored to the company's processes and data.
01
A system that supports departmental work
Core Logic coordinates company processes, while Neuro Edge modules support the work of individual departments within assigned permissions.
System02AI assistants for your customers
An assistant helps customers clarify their needs and compare products or services using approved company data. Actions depend on the agreed integrations and approval rules.
AI Assistants03Audit and implementation
We review your business, design the solution, adapt the modules and models, and connect the systems it needs.
Implementation- Audit before deployment
- Permissions and human approval
- Evidence of operation
Who builds the system
Damian Kamiński
AI Systems Architect | Agentic AI | Python | LLM & Evals | RAG | Local Models & Fine-Tuning | Inference Infrastructure | AI Security & Governance
I design and build production-grade AI systems for business and software workflows where models and agents interact with private data, documents, APIs, ERP/CRM systems, repositories and operational tools.
- The focus is the system around the model: architecture, authority boundaries, data flow, evaluation, runtime control, security, observability and evidence.
- Based in Poland. Available for remote collaboration across Europe.
LLM engineering & evals
Model integration, structured outputs, behavioural evaluation, failure analysis, guardrails and validation.
RAG & private knowledge systems
Ingestion, embeddings, vector and hybrid retrieval, source attribution and auditable evidence.
Local models & fine-tuning
Private inference, controlled data approval, training and fine-tuning, evaluation, artifact validation, promotion and rollback.
AI infrastructure
Python, FastAPI, PostgreSQL, Qdrant, APIs, workers, queues, Docker/Kubernetes and cloud infrastructure.
AI security & governance
Least authority, scoped tool access, separation of duties, preflight checks, human-in-the-loop and audit trails.
Autonomy should not grow faster than control.
Before an AI system is allowed to act, I explicitly define decision ownership, component access, independent validation, approval requirements, preflight checks, failure containment and retained evidence.
Architecture status, assumptions and limitations are stated explicitly. Proposal-stage designs are not presented as deployed systems, benchmark results as production guarantees, or compliance mappings as certifications.
Current work and collaboration
My current work includes controlled agentic systems, governed coding-agent infrastructure, private operational intelligence and local model lifecycle and inference infrastructure.
I work with founders, product teams and engineering organizations moving beyond isolated AI demos toward systems that can be operated, inspected and controlled in production.
Engagements typically begin with a fixed-scope architecture diagnostic, a bounded pilot using representative data, or production hardening of an existing AI system.
What you can see
An example of working with the system.
Follow a task from data comparison to a recorded decision, using sample data.
Working together
How we work together.
Before launch, we agree on the system’s tasks, permissions and execution rules.
- We start with an auditWe review processes, data, employee roles and security requirements. Only then do we agree on the scope of the first implementation.
- Scope and acceptance criteriaBefore implementation, we document the process scope, permissions, integrations and acceptance criteria. We assess the result against them.
- Access to data and servicesPermitted external services, the data shared with them and actions requiring human approval are agreed separately.
- Evaluation after launchWe assess result quality, working time and process cost, then agree on the scope of further work.