Research
MLOps & Model Operations
Evaluation infrastructure, observability, hallucination and regression tracking, continuous retraining.
What we work on
- 01Evaluation infrastructure: versioned test sets that run automatically for every model
- 02Observability: continuous tracking of input distribution, output quality, latency and cost
- 03Hallucination and regression detection; comparing quality across version transitions
- 04Data and concept drift detection, continuous retraining pipelines
- 05Optimising inference cost and latency
Why we work in this area
Most AI projects die at the demo stage. The model works, the presentation goes well, then production begins, the data shifts, performance quietly degrades and nobody notices.
This area is the technical substance behind our claim of "end-to-end development". Delivering a model is not the end of the work; it is the beginning of monitoring.
Sectors where we apply it
Manufacturing
Visual quality inspection, process optimisation and industrial automation.
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Automotive
Surface defect detection, assembly verification and vision models for driver assistance.
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Plastics
Production optimisation, quality control and recycling efficiency.
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Chemicals
Production monitoring, quality control and materials discovery.
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Pharmaceuticals
Packaging and serial number verification, retrieval with source attribution and decision trails.
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Food
Production quality control, supply chain and adulteration detection.
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Other research areas
Generative AI & LLMs
RAG architectures, domain adaptation and fine-tuning, distillation into smaller models, Turkish evaluation sets and on-premise deployment.
Computer Vision
Object detection and tracking, video understanding, vision-language models, document intelligence and OCR, real-time inference on edge devices.
AI Agents
Tool use, multi-step planning and multi-agent orchestration; MCP integration, human-in-the-loop workflows and safety guardrails.
Reinforcement Learning & Decision Systems
Decision policies that adapt to changing conditions, reward design, training in simulation and safe transfer to the field.
AI Security & Privacy
Robustness against adversarial attacks, prompt injection defence, learning without sharing data, and model confidentiality.
A technical assessment for your AI project
Your project's feasibility, risks and timeline are assessed in a technical consultation.