Machine Learning Consulting
Machine learning consulting for businesses that need custom models, not generic APIs. We build, train, fine-tune, and deploy ML systems that solve your specific problems.
What We Deliver
End-to-end solutions — from strategy to production deployment.
Custom Model Development
Purpose-built ML models for classification, prediction, NLP, and computer vision. Trained on your data, optimized for your use case.
LLM Fine-Tuning
Fine-tune GPT, Claude, Llama, or Mistral on your domain data. Get model performance that generic prompting can’t match.
Learn more →MLOps & Infrastructure
Model versioning, experiment tracking, automated retraining, and deployment pipelines. ML engineering done right.
Data Pipeline Engineering
Clean, transform, and prepare your data for ML. Feature engineering, data validation, and automated pipelines.
Model Evaluation & Optimization
Benchmark, test, and optimize model performance. Reduce latency, cut costs, and improve accuracy.
ML Strategy & Roadmap
Not sure where ML fits? We assess your data assets and identify where machine learning beats rules-based approaches.
Learn more →How It Works
From first call to production in 4 weeks. No fluff.
Free Consultation (Day 1)
30-minute call. We learn your pain points, assess feasibility, and scope the project. No commitment.
Discovery & Architecture (Week 1)
Deep dive into your data, workflows, and tech stack. You get architecture diagrams, cost estimates, and a clear timeline.
Build & Deploy (Weeks 2-4)
We build, test, and deploy. Weekly demos so you see progress. Production deployment with monitoring, docs, and training.
Support & Optimize (Ongoing)
30 days post-launch support included. We monitor performance, fix issues, and optimize based on real usage data.
Our in-house model was 71% accurate. ShiftAI fine-tuned it on our domain data and got it to 95% — then deployed it with proper MLOps so it actually stays accurate in production.— Head of Data Science, HealthTech Company
Tech Stack
Frequently Asked Questions
What’s the difference between AI consulting and machine learning consulting?
AI consulting covers the full spectrum — strategy, chatbots, workflow automation, and pre-built model integration. Machine learning consulting focuses specifically on custom model development — training, fine-tuning, MLOps, and data pipelines. If you need a model trained on your data, that’s ML consulting.
When should I fine-tune a model vs use prompting?
Use prompting when generic models give 80%+ accuracy on your task. Fine-tune when you need domain-specific performance, consistent output format, lower latency, or reduced costs at scale. We’ll assess your use case and recommend the most cost-effective approach.
What ML infrastructure do you set up?
We implement full MLOps — model versioning (MLflow), experiment tracking (W&B), automated retraining pipelines, CI/CD for models, GPU-optimized deployment (CUDA/TensorRT), monitoring for data drift, and A/B testing infrastructure. Everything runs on your cloud (AWS, GCP, or Azure).
Do you work with open-source models?
Yes. We work with Llama, Mistral, Phi, and other open-source models alongside commercial APIs (OpenAI, Anthropic). Open-source is often the right choice for cost control, data privacy, or when you need full model ownership. We help you choose based on your specific requirements.
How much data do I need for fine-tuning?
It depends on the task. For classification, a few hundred labeled examples can work. For LLM fine-tuning, 1,000-10,000 high-quality examples typically produce strong results. We start with a data audit to assess what you have and what additional data collection might be needed.
Related Services
Ready to Ship Your AI System?
Book a free 30-minute consultation. No pitch deck — just a technical conversation about what’s possible.