Specialized AI Models Trained on Your Industry Data
Deploy domain-specific models with fine-tuning, RAG, and tokenizers to reduce hallucinations and meet HIPAA, SEC, and compliance requirements.
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Trusted and top rated tech team
Generic AI models hallucinate on specialized content.
General-purpose language models lack deep industry knowledge and misinterpret technical terminology. A finance model confuses “AML” regulations with medical terms, legal models cite non-existent case law, and healthcare models provide dangerous medical advice. We build domain-specific language models trained on your industry data using fine-tuning, RAG, and custom tokenizers so models understand specialized vocabulary, reduce hallucinations, and maintain compliance with HIPAA, SEC, and regulatory frameworks.
Our capabilities include:
- Domain-specific model fine-tuning
- RAG implementation with private knowledge bases
- Custom tokenizer development for industry jargon
- Healthcare AI with HIPAA compliance
- Financial models for regulatory requirements
- Legal AI for contract and case law analysis
Who we support
Generic language models trained on broad internet data lack depth in specialized fields. We help organizations build domain-specific models using curated industry data that produces accurate, compliant outputs for regulated environments.
Healthcare Organizations with Clinical Data
Your AI must understand medical terminology, drug interactions, and clinical protocols without hallucinating diagnoses or treatments. Generic models confuse symptoms, misinterpret lab values, and can't navigate HIPAA requirements for patient data protection.
Financial Services with Regulatory Data
Your team analyzes SEC filings, credit reports, and compliance documents where accuracy is legally required. General models misinterpret financial terminology, confuse regulatory frameworks across jurisdictions, and lack understanding of anti-money laundering patterns.
Legal Firms Analyzing Case Law
Your firm needs AI that cites real case law, understands jurisdiction-specific statutes, and interprets contract language precisely. Generic models invent precedents, misapply legal principles across practice areas, and can't distinguish between binding and persuasive authority.
Ways to engage
We offer a wide range of engagement models to meet our clients’ needs. From hourly consultation to fully managed solutions, our engagement models are designed to be flexible and customizable.
Staff Augmentation
Get access to on-demand product and engineering team talent that gives your company the flexibility to scale up and down as business needs ebb and flow.
Retainer Services
Retainers are perfect for companies that have a fully built product in maintenance mode. We'll give you peace of mind by keeping your software running, secure, and up to date.
Project Engagement
Project-based contracts that can range from small-scale audit and strategy sessions to more intricate replatforming or build from scratch initiatives.
We'll spec out a custom engagement model for you
Invested in creating success and defining new standards
At Curotec, we do more than deliver cutting-edge solutions — we build lasting partnerships. It’s the trust and collaboration we foster with our clients that make CEOs, CTOs, and CMOs consistently choose Curotec as their go-to partner.
Why choose Curotec for specialized AI models?
Our engineers fine-tune models on your curated datasets and build RAG systems connecting to proprietary knowledge bases. We develop custom tokenizers for industry terminology and implement compliance guardrails for regulatory requirements. You get models that understand specialized vocabulary, reduce hallucinations, and maintain accuracy as standards evolve.
1
Extraordinary people, exceptional outcomes
Our outstanding team represents our greatest asset. With business acumen, we translate objectives into solutions. Intellectual agility drives efficient software development problem-solving. Superior communication ensures seamless teamwork integration.
2
Deep technical expertise
We don’t claim to be experts in every framework and language. Instead, we focus on the tech ecosystems in which we excel, selecting engagements that align with our competencies for optimal results. Moreover, we offer pre-developed components and scaffolding to save you time and money.
3
Balancing innovation with practicality
We stay ahead of industry trends and innovations, avoiding the hype of every new technology fad. Focusing on innovations with real commercial potential, we guide you through the ever-changing tech landscape, helping you embrace proven technologies and cutting-edge advancements.
4
Flexibility in our approach
We offer a range of flexible working arrangements to meet your specific needs. Whether you prefer our end-to-end project delivery, embedding our experts within your teams, or consulting and retainer options, we have a solution designed to suit you.
Specialized capabilities that master industry knowledge
Domain-Specific Fine-Tuning
RAG with Private Knowledge Bases
Custom Tokenizer Development
Hallucination Reduction
Compliance-Aware Training
Expert Evaluation Frameworks
Tools that create industry-trained models
Foundation Models & Frameworks
Our engineers select base models and training frameworks that provide the foundation for domain-specific customization and fine-tuning.
- OpenAI GPT-4 — Foundation model with fine-tuning API for domain adaptation using custom datasets and evaluation metrics
- Anthropic Claude — Large language model with extended context windows supporting in-depth domain document processing
- Meta Llama 3 — Open-source foundation model enabling full control over fine-tuning, deployment, and inference infrastructure
- Google Gemini — Multimodal model with enterprise API supporting domain-specific training on text, images, and structured data
- Mistral AI — Efficient open-source models optimized for domain specialization with lower computational requirements
- Cohere — Enterprise-focused models with RAG capabilities and domain-specific embedding generation for retrieval systems
Fine-Tuning & Training Platforms
Curotec implements training pipelines with distributed compute, hyperparameter tuning, and version control for reproducible results.
- Hugging Face Transformers — Open-source library with pre-trained models, fine-tuning scripts, and trainer APIs for domain adaptation
- LangChain — Framework for building LLM applications with prompt templates, chains, and agents for domain-specific workflows
- Azure Machine Learning — Managed training platform with distributed compute, experiment tracking, and model registry for enterprise deployments
- Amazon SageMaker — AWS service providing managed training, hyperparameter tuning, and model deployment with built-in algorithms
- Google Vertex AI — Unified ML platform with AutoML, custom training, and model monitoring for production domain-specific models
- Weights & Biases — Experiment tracking and visualization platform monitoring training metrics, hyperparameters, and model performance
RAG & Knowledge Base Integration
We build retrieval systems connecting models to proprietary documents, ensuring responses ground in verified knowledge.
- LlamaIndex — Data framework connecting LLMs to private knowledge bases with document loaders and query engines
- Pinecone — Vector database storing embeddings for semantic search and retrieval-augmented generation at scale
- Weaviate — Open-source vector database with hybrid search combining semantic similarity and keyword matching
- Chroma — Embedding database designed for LLM applications with built-in document chunking and metadata filtering
- Elasticsearch — Search engine with vector similarity support for hybrid retrieval combining full-text and semantic search
- Azure AI Search — Enterprise search service with vector search, semantic ranking, and security features for RAG implementations
Custom Tokenization & Vocabulary
Our teams develop tokenizers that treat specialized terms as single units, preserving meaning in medical, legal, and technical domains.
- SentencePiece — Unsupervised tokenizer training library creating domain-specific vocabularies from custom corpora
- Hugging Face Tokenizers — Fast tokenization library with algorithms for building custom vocabularies optimized for domain text
- BPE (Byte Pair Encoding) — Subword tokenization algorithm balancing vocabulary size with representation of specialized terminology
- WordPiece — Tokenization method used in BERT-based models, adaptable for domain-specific vocabulary construction
- Unigram Language Model — Probabilistic tokenizer selecting optimal subword units based on domain corpus statistics
- spaCy — NLP library with tokenization, named entity recognition, and custom pipeline components for domain text processing
Evaluation & Quality Assurance
Curotec establishes evaluation frameworks with domain experts measuring accuracy against professional standards, not generic metrics.
- RAGAS — RAG evaluation framework measuring retrieval relevance, answer faithfulness, and context utilization
- TruLens — Evaluation toolkit for LLM applications with feedback functions, tracing, and guardrails for quality assurance
- PromptFoo — Testing framework for LLM outputs with assertion libraries, regression testing, and benchmark comparisons
- MLflow — Experiment tracking platform logging model versions, evaluation metrics, and comparison across training runs
- Evidently AI — Model monitoring platform detecting drift, evaluating quality, and tracking performance over time
- Human-in-the-Loop Platforms — Annotation tools like Labelbox and Scale AI for expert validation and ground truth creation
Compliance & Security Frameworks
We implement guardrails, content filtering, and audit logging that ensure model outputs meet regulatory requirements and data governance.
- NeMo Guardrails — NVIDIA framework defining safety rails, content policies, and behavioral constraints for LLM applications
- Azure AI Content Safety — Managed service detecting harmful content, PII, and policy violations in model inputs and outputs
- AWS Comprehend — NLP service with PII detection, sentiment analysis, and entity recognition for compliance screening
- Presidio — Open-source framework for PII detection and anonymization protecting sensitive data in training and inference
- LangKit — Security toolkit for LLM applications with prompt injection detection, output validation, and anomaly monitoring
- Guardrails AI — Validation framework enforcing output structure, content policies, and correctness requirements for production models
FAQs about specialized AI models
How do specialized models differ from ChatGPT?
General models like ChatGPT are trained on broad internet data and lack depth in specific fields. Specialized models are fine-tuned or trained from scratch on curated industry datasets, understanding technical terminology, regulatory frameworks, and domain-specific contexts that generic models misinterpret or hallucinate.
What does model specialization involve?
We assess your domain data quality, select appropriate base models, and implement fine-tuning on curated datasets or build RAG systems connecting to proprietary knowledge bases. Specialization includes developing custom tokenizers for industry terminology, establishing evaluation frameworks with domain experts, and implementing compliance guardrails.
Should we use fine-tuning or RAG?
Fine-tuning adapts model weights using your data, ideal for mastering specialized vocabulary and consistent domain knowledge. RAG connects models to live knowledge bases, better for frequently updated information and traceability to source documents. We assess use cases, data volume, and update frequency to recommend the right approach.
How do you reduce hallucinations?
We ground models in verified domain data through fine-tuning on curated datasets or RAG retrieval from proprietary sources. Custom tokenizers preserve specialized terminology, evaluation frameworks catch errors before deployment, and guardrails enforce output constraints. This reduces hallucinations by anchoring responses in verified knowledge rather than statistical patterns.
Can models meet HIPAA or SEC requirements?
Yes. We implement compliance guardrails detecting and filtering sensitive information, audit logging tracking model usage, and PII detection preventing data leakage. Training happens on secure infrastructure with access controls, and RAG systems connect only to compliant knowledge bases. Domain experts validate outputs meet regulatory standards.
How long does model development take?
Our engineers typically build proof-of-concept RAG systems or fine-tune initial models within 3-4 weeks. Full production implementation with custom tokenizers, evaluation frameworks, compliance validation, and performance optimization takes 8-12 weeks depending on data volume, domain complexity, and regulatory requirements.
Ready to have a conversation?
We’re here to discuss how we can partner, sharing our knowledge and experience for your product development needs. Get started driving your business forward.
