Where Breakthrough Systems Begin
DBERT Labs is an applied artificial intelligence engineering facility and technical research division based in Delhi NCR. We investigate multi-agent orchestration architectures, local LLM edge quantization, custom RAG evaluation harnesses, and algorithmic financial intelligence—open-sourcing our findings and feeding validated systems directly into our incubation and learning ecosystems.
Applied Research Divisions
Peer-audited preprints, empirical latency benchmarks, and sovereign model weights.
arXiv-Style Technical Briefings
Empirical evaluation bounds, reproducible hyperparameters, and Zenodo open data archives.
Deterministic Conformal Prediction for Low-Latency Time Series in Emerging Markets
Empirical evaluation of non-exchangeable conformal prediction bounds over high-volatility tick data streams. Shipped to production under the Alkame quantitative risk engine.
Parameter-Efficient LoRA Quantization for Offline-First Edge Classrooms
Compression benchmarks for 3B–8B parameter models running on consumer hardware without external API connectivity. Powers Gayatri AI local school agent deployments.
Technical Publications & Codebases
Deploy our open-source weights and inspect reproducible benchmarks.
Research Segments
Inspect our active structural investigations: multi-agent system governance, sovereign LLM compression, hybrid vector RAG architectures, and automated evaluation toolboxes.
Technical Publications
Examine our peer-reviewed style engineering briefs, complete with academic citations (DOI/Scholar), reproducible hyperparameters, and empirical hardware latency benchmarks.
Open Source Repositories
Deploy DBERT's contributions to public open-weights registries (DBERT_AI custom LLM on Ollama) and educational systems (Gayatri AI syntax parsers).
Academic & Institutional Collaboration
Partner with DBERT Labs on custom engineering research sprints, sovereign AI cluster benchmarks, or specialized dataset alignment projects under verified legal frameworks.