Model Training
From raw data to production-ready models — pipelines built for precision, repeatability, and business performance.
When general-purpose models are not enough
Foundation models like GPT-4, Claude, and Llama are trained on broad internet data. They are excellent at general tasks. But when your business requires precision — medical coding accuracy, legal clause classification, financial entity extraction, or manufacturing defect detection — general-purpose models introduce unacceptable error rates.
The solution is not always fine-tuning. Sometimes it is better data, better evaluation, or a better retrieval architecture. We assess your specific task requirements and build the training program that produces the performance your business needs — without over-engineering or over-spending.
Our model training programs are built around measurable outcomes. We define success metrics before we write a line of training code, and we do not ship a model until it meets them.
The Model Training Pipeline
Every stage is designed for quality, repeatability, and production readiness
Data Collection & Preparation
Source, clean, deduplicate, and structure training data. Define annotation schemas and quality standards.
Labeling & Annotation
Build high-quality labeled datasets with inter-annotator agreement checks and quality control workflows.
Training & Fine-Tuning
Run training experiments with systematic hyperparameter search. Apply LoRA, QLoRA, or full fine-tuning based on constraints.
Evaluation & Benchmarking
Test against custom evaluation datasets. Measure accuracy, safety, and business-relevant metrics. Red-team for failure modes.
Deployment & Monitoring
Ship to production with versioning, A/B testing, drift detection, and automated retraining triggers.
What domain-trained models deliver
Performance improvements from specialized model training programs
Model Training Service Areas
Each capability can be engaged independently or as part of a full training program.
Data Preparation & Labeling
Build high-quality training datasets with the right structure and label strategy — the foundation every model needs.
- Data sourcing, cleaning & deduplication
- Annotation workflows & quality control
- Synthetic data generation for edge cases
Fine-Tuning for Domain Performance
Adapt foundation models to your specific domain, terminology, and task requirements for production-grade accuracy.
- Supervised & instruction fine-tuning
- RLHF & preference optimization
- Parameter-efficient methods (LoRA, QLoRA)
Evaluation & Benchmark Design
Measure model performance against business-relevant metrics — not just academic benchmarks.
- Custom evaluation dataset construction
- Automated scoring & regression suites
- Human evaluation & red-teaming
Continuous Retraining & Monitoring
Keep models accurate and relevant as your data, business context, and user needs evolve over time.
- Drift detection & retraining triggers
- Automated retraining pipelines (MLOps)
- Production monitoring dashboards
Real-World Use Cases
Where specialized model training delivers precision that general-purpose models cannot match
Medical Coding Automation
A hospital system needed to automate ICD-10 coding from clinical notes. General-purpose LLMs produced 72% accuracy — below the 95% threshold required for billing. A fine-tuned model trained on 200,000 annotated clinical notes achieved 96.4% accuracy, enabling full automation of routine coding with human review only for edge cases.
Manufacturing Defect Detection
A precision parts manufacturer needed a vision model to detect surface defects at 0.1mm resolution on a production line running at 200 parts per minute. A custom-trained computer vision model on their defect image library achieved 99.2% detection accuracy with a false positive rate under 0.5%.
Customer Sentiment Classification
A telecom company needed to classify customer feedback across 15 sentiment categories specific to their service taxonomy. A fine-tuned classifier trained on 50,000 labeled support interactions replaced a manual tagging team of 8 people and processes 10,000 items daily with 93% accuracy.
Insurance Claims Triage
An insurance company's claims team spent 2–3 hours per claim on initial assessment and routing. A fine-tuned model trained on historical claims data classifies claim type, estimates complexity, flags fraud signals, and routes to the right adjuster — reducing initial triage to under 10 minutes.
Multilingual Customer Support
A global e-commerce platform needed consistent support quality across 12 languages. Fine-tuned multilingual models trained on their product catalog and support history handle Tier-1 queries in all languages with consistent brand voice and policy adherence.
Financial Entity Extraction
A fintech company needed to extract structured financial entities — companies, amounts, dates, instruments — from unstructured news and filings at scale. A fine-tuned NER model processes 50,000 documents daily with 97% entity extraction precision, feeding their analytics platform.
Fine-Tuning vs. RAG: Choosing the Right Approach
We help you pick the method that fits your task, data, and cost constraints
| Consideration | Fine-Tuning | RAG | Combined |
|---|---|---|---|
| Domain terminology & style | ✅ Excellent | ⚠️ Partial | ✅ Best |
| Up-to-date knowledge | ❌ Requires retraining | ✅ Real-time | ✅ Real-time |
| Factual accuracy on your data | ⚠️ Can hallucinate | ✅ Grounded | ✅ Best |
| Inference cost | ✅ Low (smaller model) | ⚠️ Higher (retrieval + LLM) | ⚠️ Highest |
| Time to first results | ⚠️ Weeks | ✅ Days | ⚠️ Weeks |
| Handles novel queries | ⚠️ Limited | ✅ Good | ✅ Best |
| Consistent output format | ✅ Excellent | ⚠️ Variable | ✅ Excellent |
Our Model Training Process
A repeatable, benchmark-driven process from raw data to production-ready models.
Task & Data Scoping
Define the target task, success metrics, and data requirements before any training begins
Data Pipeline Build
Prepare, clean, label, and version training datasets with quality controls at every step
Training & Fine-Tuning
Run training experiments with systematic hyperparameter optimization and ablation studies
Evaluation & Validation
Benchmark against business KPIs, safety requirements, and regression test suites
Deploy & Retrain
Ship to production and establish automated retraining cadence with drift monitoring
Need a model that actually performs on your task?
Share your task requirements and current performance gaps. We will assess whether fine-tuning, RAG, or a combined approach is the right path — and what it takes to get there.
Plan Your Training Program