Machine Learning Consulting
Machine learning that works in production, not just notebooks
We build ML systems designed for the real world: observable, cost-optimized, and production-grade. From model selection to deployment to continuous improvement, with full visibility at every stage.
Machine learning services we provide
Machine learning in production requires more than model training. It requires data infrastructure, deployment architecture, monitoring, cost management, and the engineering discipline to keep systems reliable over time.
Model Selection & Architecture
Choosing the right model for each task is the most impactful decision in an ML project. We evaluate pre-trained LLMs, fine-tuned models, and custom-trained models based on accuracy requirements, latency constraints, cost targets, and production needs. Not every problem needs a frontier model.
Multi-Agent ML Systems
Complex problems require multiple specialized ML components working together. We build multi-agent architectures where each agent handles its domain: classification, extraction, analysis, generation, validation. Coordinated by an orchestrator that manages task flow and fault handling.
Production ML Pipelines
From data ingestion to inference to post-processing. We build end-to-end ML pipelines with proper data validation, feature engineering, model serving, output verification, and error handling. Systems designed to handle real-world data, not clean datasets.
ML Cost Optimization
ML inference is the largest ongoing cost in most AI systems. We apply intelligent distillation to convert repetitive ML operations into deterministic functions, implement tiered model routing to match task complexity to model cost, and optimize token usage across every operation.
ML Observability & Monitoring
Every ML operation tracked in real time: accuracy metrics, drift detection, cost per prediction, latency distributions, and quality scoring. Alerting for degradation before users notice. Decision audit trails for compliance and debugging.
Retrieval-Augmented Generation (RAG)
Build knowledge-grounded AI systems that answer questions using your company's data. Vector search, document chunking strategies, retrieval optimization, and hallucination reduction. Systems that cite their sources and admit when they do not know.
The gap between ML in development and ML in production
A model that performs well in a Jupyter notebook is not a production ML system. Production requires handling dirty data, managing model updates without downtime, tracking costs per prediction, detecting quality drift, and maintaining audit trails for every decision.
This is where most ML projects stall. The model works. The data science is sound. But the engineering to make it reliable, observable, and cost-effective in production is a different discipline entirely.
That engineering discipline is our focus. We build the infrastructure that takes ML from "it works on my machine" to "it runs reliably at scale with full visibility into every operation."
What production ML requires
- Data validation that catches quality issues before they reach the model
- Model versioning with rollback capability and A/B testing support
- Drift detection that alerts when model accuracy degrades over time
- Cost tracking per prediction, per model, per operation type
- Fault tolerance with circuit breakers, fallback models, and graceful degradation
- Decision audit trails for compliance, debugging, and continuous improvement
Machine learning consulting FAQ
What is machine learning consulting?
Machine learning consulting helps businesses design, build, and deploy ML-powered systems. This includes data pipeline architecture, model selection and optimization, production deployment, monitoring, and ongoing optimization. The focus is on systems that work reliably in production.
What is the difference between AI consulting and ML consulting?
Machine learning consulting focuses on systems that learn from data: classification, prediction, extraction, and pattern recognition. AI consulting is broader, encompassing ML plus prompt engineering, multi-agent orchestration, workflow automation, and AI cost optimization. Most modern AI engagements involve significant ML components.
Do you build custom models or use pre-trained models?
We use the right approach for each task. Many production problems are best solved with pre-trained LLMs augmented with retrieval and fine-tuning. Others require custom models. Our intelligent distillation methodology identifies which operations need ML inference and which can be converted to cheaper deterministic functions.
How do you handle ML model costs at scale?
Three mechanisms: intelligent distillation converts repetitive ML operations to near-zero-cost deterministic functions (typically 50-70% of operations). Tiered model routing matches task complexity to model cost. Token optimization reduces input and output size without quality loss. Combined, these typically achieve 60-90% cost reduction over baseline.
Need machine learning expertise?
Whether you are building your first ML system or scaling an existing one, we can help. Schedule a discovery call to discuss your project and get an honest assessment of what is possible.