Transform ideas into production-grade AIat supersonic speed
From rapid prototypes to massive ML pipelines. We design, fine-tune and deploy state-of-the-art models on robust data foundations. Built with Hugging Face, PyTorch, JAX, and rock-solid MLOps.
Latency optimized with vLLM + TensorRT-LLM. Streaming tokens in < 50ms.
AI & Machine Learning Services that ship
Architecture, modeling, and delivery performed by hands-on AI researchers and production engineers.
- • Grid search and random search
- • Integration with Optuna and Ray Tune
- • GPU-accelerated experiments
- • Fine-tuning on custom datasets
- • Feature extraction pipelines
- • Cross-domain knowledge transfer
- • Bagging, boosting, stacking
- • Diversity maximization techniques
- • Calibrated confidence scores
Models: Llama, Mistral, Qwen. Retrieval with ColBERT/TS. Guardrails + evals.
GPU packing, quantization (AWQ/GPTQ), KV caching, streaming, cost controls.
Vision (SAM, CLIP, DETR), Speech (Whisper, MMS), Multimodal (LLaVA).
Feature stores, labeling, synthetic data, drift detection, privacy.
Machine Learning for Predictive Maintenance
Leverage advanced ML techniques to anticipate failures, optimize operations, and drive efficiency in manufacturing and beyond.
AI Development Engagement Models
Simple, outcome-driven pricing designed for velocity and measurable impact.
- Scoping + success metrics
- Prototype (LLM, RAG or Vision)
- Live demo + next steps
- Team: 2 engineers + 1 researcher
- Dedicated GPU infra + CI/CD
- Observability, evals, guardrails
- Security, privacy, compliance ready
- Multi-cloud / on-prem (K8s)
- Advanced research sprints
AI Success Stories: Proof, not promises
We partner with product teams and research labs to ship AI outcomes that matter.
Why Codefex AI Solutions vs DIY
With Codefex
- Production-ready templates: vLLM, TGI, KServe
- Evals, guardrails, drift + feedback loops baked in
- Security and governance from day zero
Roll-your-own
- ✕ Weeks of infra before first token
- ✕ Hidden costs: GPUs, egress, failures
- ✕ Hard-to-measure quality without evals