ML Systems
From data pipelineto validated system.
Custom NLP models, natural language search systems, and production APIs. Built on your data, for your domain. End-to-end delivery without the overhead of a multi-person team. Carefully validated so you avoid expensive mistakes.
Model Training
Custom NLP models built on your domain and your data.
General-purpose NLP APIs are optimized for average performance across many domains. When your use case involves specialized terminology or domain-specific language patterns, off-the-shelf models can underperform — sometimes badly. But the problem usually isn't just the model. It's that the labels are defined too loosely, the evaluation metric doesn't reflect what matters in production, or the training data doesn't represent the cases your system will actually encounter. Getting those things right is upstream of architecture.
I train custom NLP models — text classification, named entity recognition, information extraction, span detection — trained and validated on your specific domain and use cases. I cover the full development lifecycle: label definition and data assessment, preprocessing, architecture selection, training and tuning, error analysis and edge case validation, and documentation. You receive a production-ready PyTorch model with a complete model card, preprocessing code, inference pipeline, and 30 days of post-delivery support.
Trained model weights and configuration files (PyTorch)
Data preprocessing and inference pipeline code
Model card documenting training data, architecture, performance metrics, and known limitations
Performance evaluation report with per-class metrics and error analysis
Integration guidance and deployment recommendations
30 days of post-delivery virtual support and bug fixes
3 – 5 weeks: data assessment and preprocessing pipeline, model training and tuning, optimization, documentation, and handoff.
$20,000 – $55,000
Task complexity is the primary driver — sequence classification sits toward the lower end; span classification, multi-label, or custom architectures toward the upper. Data quality and volume matter significantly: clean, well-labeled data reduces iteration time. Extensive custom preprocessing or particularly noisy data increases it.
Recommender Systems
Semantic recommendations at scale.
Keyword-based search and rule-based recommendation systems have a fundamental ceiling: they can only surface what users already know how to ask for. Organizations with large document corpora, product catalogs, or content libraries need systems that understand meaning — that can recommend a relevant document based on semantic similarity, or surface related content a user didn't know existed.
I design and build semantic recommendation systems using embedding models and efficient vector retrieval. I encode your corpus with a domain-appropriate embedding model, index it in vector database optimized for your scale and latency requirements, and use cross-encoder re-ranking to improve accuracy. I use advanced techniques, including custom bi-encoders, HyDE, and graph-augmented retrieval to improve performance. You get a production-ready API that returns semantically relevant recommendations with filtering, personalization options, and quick response times, along with a monitoring dashboard tracking search quality and system performance over time.
Production recommendation API with filtering and personalization options
Vector database with indexed embeddings, optimized for your scale
Cross-encoder re-ranking model for precision improvement
Hybrid search combining semantic and keyword matching
Monitoring and analytics dashboard for search quality and performance
Docker containerization and deployment configuration
Technical documentation and API integration guide
Performance benchmark report (relevance metrics, latency, throughput)
Training session on system operation and index management
30 days of post-deployment support
6 – 10 weeks: data preprocessing and embedding generation, vector database setup and optimization, cross-encoder development, API development, integration and performance tuning, documentation and deployment.
$30,000 – $100,000
A single cross-encoder system over a moderate corpus sits toward the lower end. Hybrid systems requiring advanced techniques (HyDE, graph-augmented retrieval), a domain-specific bi-encoder, or strict latency requirements sit toward the upper end. Corpus size, query volume, and index complexity are key drivers.
Domain Adaptation
Language models that understand your domain's vocabulary and patterns.
In specialized fields — legal, clinical, financial, scientific — language works differently. Standard BERT-family models are pretrained on general web text, which means they've never encountered your terminology used in context, your field's rhetorical conventions, or the specific ways concepts relate in your domain. For business-critical applications where accuracy matters, this gap produces models that perform well on general benchmarks and poorly on your actual data.
I adapt a BERT-family transformer to your specific domain through continued pretraining on your domain corpus, followed by fine-tuning for your target tasks. I handle corpus collection and preprocessing, custom vocabulary adaptation for domain terminology, pretraining validation, and the development of multiple task-specific models on top of the adapted base. The result is a domain-aware language model that understands your field's language, paired with an automated retraining pipeline so the model can be updated as your corpus grows.
Domain-adapted base model weights and configuration
Custom tokenizer to capture domain terminology
Task-specific fine-tuned models built on adapted base
Automated retraining pipeline with performance evaluation
Testing framework for comparing model versions
Architecture documentation covering design decisions and tradeoffs
Training materials for your team
30 days of post-deployment support with priority response
6 – 10 weeks: domain corpus collection and preprocessing, tokenization and vocabulary adaptation, domain pretraining and validation, task-specific model development, documentation and handoff.
$30,000 – $60,000
Corpus size and domain specificity are the primary drivers, along with compute requirements for pretraining. GPU compute costs for pretraining can be significant and are billed separately. Infrastructure sophistication and the number of task-specific models also affect scope.