Training LLMs on Specific Behaviors, Formats, or Domain Knowledge
Our team creates input-output or question-answer pairs with clear, correct responses to train LLMs on specific behaviors, formats, or domain knowledge. We can also systematically train your model using parameter-efficient methods (LoRA, QLoRA) or full fine-tuning based on your performance requirements and budget.
Best for:
- Data extraction and classification tasks requiring high accuracy, like contract clause extraction, named entity recognition, and sentiment analysis
- Domain-specific applications needing specialized terminology, like medical documentation, legal research, and financial analysis
- Structured output generation, like report creation, form filling, and code generation, that follows specific patterns
- Repetitive specialized tasks where consistency matters more than creativity, like document review, compliance checking, and quality control automation
Aligning LLM models with Subjective Quality and Human Values
To help AI models identify and judge tone, appropriateness, safety, brand alignment, quality standards, etc., our experts rank multiple AI responses to the same prompt (marking which response is more helpful, safer, more appropriate.
Best for:
- Customer-facing applications where quality is subjective, like support chatbots, advisory systems, and conversational agents
- Safety-critical systems requiring alignment with human values, like content moderation, mental health support interfaces, and educational platforms
- Brand-aligned content generation, like marketing copy, social media responses, and customer communications
- Applications with competing objectives that require nuanced trade-offs, like being concise yet thorough, friendly yet professional, or helpful yet safe
For Simpler, Faster LLM Fine-Tuning without RLHF's Complexity
For low-stakes projects with tight timelines and budgets, we offer a simpler alternative to reinforcement learning from human feedback. Our team optimizes preferences directly by curating preference pairs (which response is better and why), so your model can be directly optimized without the need to train a reward model.
Best for:
- Projects testing AI before full deployment, like pilot programs for new chatbots and proof-of-concept conversational tools
- Time-sensitive launches with compressed development cycles, like seasonal campaign support tools and product launch assistants with fixed deadlines
- Iterative development projects requiring frequent experimentation, like A/B testing brand voice implementations
- Mid-complexity applications where RLHF would be overkill, like internal knowledge base assistants, departmental productivity tools, and specialized content generators
Fine-Tuning AI for Safety, Ethics, and Brand Values
Train an LLM to critique and revise its own outputs based on a written set of rules and values - a constitution. Our team creates a set of concrete, testable rules (based on enterprise values, safety requirements, behavioral standards, and edge cases) that your AI should follow, evaluates the responses, and monitors ongoing alignment with your constitution.
Best for:
- Regulated Industries with compliance requirements, like healthcare, financial services, legal, and government
- Brand-sensitive customer-facing applications, like customer service chatbots, marketing AI, and social media agents
- Multi-stakeholder platforms with diverse safety needs, like social networks, marketplaces, and community forums
- Enterprise AI with complex operational policies, like internal productivity tools, HR systems, and knowledge management systems
