From custom LLMs to deep learning pipelines — we build every layer of your AI stack, tailored to your exact problem.
Bafar Labs designs and builds custom AI systems from the ground up. Whether you need a fine-tuned LLM, a computer vision pipeline, an agentic workflow, or a full deep learning system — we handle the full lifecycle: problem framing, data strategy, model architecture, training, evaluation, and production deployment. No templates, no off-the-shelf wrappers — pure bespoke AI engineering delivered with startup speed and enterprise precision.
Domain-specific LLM training with RLHF, LoRA, and QLoRA — models that understand your industry, your data, your terminology
End-to-end: problem framing, data strategy, model architecture, training, evaluation, and production deployment
State-of-the-art architectures — transformers, diffusion models, graph neural networks — applied to real enterprise problems
We work across the full spectrum of modern AI — from research-grade deep learning to production agentic systems.
Custom neural architectures — CNNs, Transformers, Diffusion Models, GNNs — trained on your data.
Detection, segmentation, classification, and generation pipelines for images and video.
Multi-agent systems that plan, use tools, and execute complex workflows autonomously.
Domain-specific language model fine-tuning, instruction tuning, and alignment.
Forecasting, classification, anomaly detection, and recommendation systems.
Custom ASR, TTS, NER, summarization, and multilingual text understanding.
Custom LLM fine-tuning: LoRA, QLoRA, RLHF on domain-specific datasets
Deep learning system design: CNNs, Transformers, Diffusion Models, GNNs
Computer vision pipelines: detection, segmentation, classification, generation
Agentic AI systems: multi-agent orchestration, tool use, autonomous workflows
Predictive ML: forecasting, classification, anomaly detection, recommendation
NLP & speech: custom NER, sentiment, summarization, ASR, TTS pipelines
Data strategy: collection, labelling, augmentation, and dataset engineering
MLOps: model versioning, monitoring, CI/CD, and production infrastructure
Custom diagnostic model trained on proprietary medical imaging data — disease detection with explainable AI outputs.
Personalization engine combining recommendation ML, visual search CV, and conversational LLM in one unified system.
Fraud detection model with real-time inference, anomaly scoring, and adaptive retraining on streaming transaction data.
Computer vision quality inspection system detecting micro-defects on production lines with 99%+ accuracy.
Start with a 14-day pilot. See it working on your data before you commit.