EU-based SMEResearch → ProductionVerification & evaluationDeployment-ready engineering
We build applied AI systems that scale.
Iterica designs applied AI systems with measurable control — verification, evaluation, and deployment-minded engineering from research to production.
Evidence-first
Metrics, benchmarks, and audit trails.
Statistical rigor
FNR/FPR, stability, confidence.
Deployment-minded
Reliability, integration, iteration.
System view
Quality-driven workflow
Framing
scope → measurable criteriaGates
rules + semantic checksEvaluation
stress tests + stabilityDeployment
logging, thresholds, iterationFocus: verifiable generative AI workflows, evaluation methodology, and auditable decisions.
Typical artefacts
evaluation protocolbenchmark suitegate libraryexperiment logstraining recipedeployment playbookreference implementation
What we do
Applied AI systems with measurable control
We combine applied AI, statistical evaluation, and engineering to build systems that can be trusted in customer-facing workflows.
⌁
AI Verification & Quality Gates
Design of verifiable workflows: deterministic checks plus semantic verification with auditable outcomes.
◷
Evaluation Methodology
Measurement frameworks for model behaviour: error analysis, stability testing, and calibrated thresholds.
▣
AI Product Development
From prototype to deployment: integrations, reliability, observability, and iteration under real constraints.
⟂
Systems Architecture & Orchestration
Control flows that turn probabilistic models into controllable systems (routing, repair, logging).
⟡
Machine Learning & Model Adaptation
Hands-on ML work: fine-tuning workflows, structured outputs, calibration and uncertainty handling.
▦
Software Engineering
Production-grade implementation: APIs, pipelines, containerisation, CI discipline, and reproducible environments.
Capabilities
Core technical expertise
Applied Statistics & Measurement
evidence-driven- Experimental design for ML systems and workflow validation
- False-negative / false-positive analysis by risk class
- Confidence intervals and stability checks under perturbations
- Risk-based automation boundaries (auto-pass vs route-to-human)
Evaluation at Scale
cloud-scale- Adversarial test generation and high-volume evaluation loops
- Parallel inference execution (cloud environments) and repeat-run tests
- Containerised runs with tracked configs and reproducible artefacts
- Experiment tracking: datasets, runs, metrics, comparisons
Machine Learning & Model Adaptation
hands-on- Parameter-efficient adaptation methods (fine-tuning workflows)
- Structured outputs: decision + evidence + confidence for auditability
- LLM-as-judge patterns for policy/facts verification tasks
- Calibration and uncertainty-aware routing
Engineering, Deployment & IT Skills
production-minded- API services, pipelines, and integration layers
- Observability: logging, metrics, and traceable decision records
- Reproducible environments (containers) and versioned artefacts
- Deployment discipline: thresholds, monitoring, rollback-safe iteration
Approach
How we work
Step
Problem framing
Scope → hypotheses → measurable criteria
Output: document + artefact
Step
Architecture
Interfaces, gates, audit trail design
Output: document + artefact
Step
Evaluation
Benchmarks, adversarial tests, stability
Output: document + artefact
Step
Deployment
Thresholds, monitoring, iteration
Output: document + artefact
Contact
Get in touch
General inquiries, partnerships, and applied R&D collaborations. If you need a concise technical summary, a deliverables outline, or a validation plan for an innovation study, we can help.
Company details
Iterica s.r.o.
IČO: 57 425 388
Address: Nevädzová 6/E, 821 01 Bratislava
hello@iterica.eu
We typically reply within 1–2 business days.