ADGTech Labs
Research that ships into the enterprise.
Our research focuses on making AI controllable, measurable and deployable inside the organisations that need it most.
- R-01
Agentic AI
Planning, delegation and safe tool use for multi-agent systems in enterprise workflows.
- R-02
Fine-tuning & adaptation
Parameter-efficient methods (LoRA / QLoRA) to adapt open models to domains and tasks.
- R-03
RAG innovation
Hybrid retrieval, reranking and permission-aware indexing for grounded answers.
- R-04
Model evaluation
Task-specific evaluation suites that measure what matters for each deployment.
- R-05
Private inference
Efficient serving of models on customer-controlled hardware.
- R-06
Dataset engineering
Pipelines for clean, governed, PII-aware training data.
- R-07
Enterprise orchestration
Connecting agents to ERP, CRM and line-of-business systems with approvals.
- R-08
AI security
Prompt-injection resistance, tool sandboxing and security testing of AI systems.
- R-09
Future AI infrastructure
Packaging the full stack as sovereign, portable AI-in-a-Box deployments.
Publications
Papers, guides and benchmarks
We publish technical material once it has been reviewed and approved. Benchmarks are released only with reproducible methodology.
- Architecture guideSovereign AI reference architectureDiscuss in a briefing
- Technical noteEvaluating fine-tuned models against baselinesPlanned topic
- Technical notePermission-aware retrieval for enterprise RAGPlanned topic
- White paperHuman-in-the-loop design for enterprise agentsPlanned topic
Work with our research team
Pilot new capabilities on your use cases, with rigorous evaluation.