Domain-Driven LLM Services
Discovery & Customization
We begin with a comprehensive assessment of your industry, identifying data sources and integrating the LLM to meet domain-specific needs.
Deployment & Integration
After development, we implement the LLM into your workflows, integrating it with your current systems and ensuring minimal disruption to operations.
Monitoring & Optimization
Post-deployment, we continuously monitor model performance, collecting data to make improvements as needed for optimal functionality and ROI.
Approaches
Retrieval-Augmented Generation
Combining pre-trained LLMs with real-time document retrieval, allowing the model to pull in the most relevant information dynamically for precise responses.
Prompt Engineering
Structuring prompts to provide context that guides the model toward specific answers, especially useful in complex, multi-step tasks. For example, framing a prompt with additional context or guidelines helps the model focus on the required tone or specificity.
Ranked Retrieval & Semantic Search
Implementing ranking algorithms and vector-based similarity search, ensuring the model accesses prioritized and contextually relevant data sources.
Prompt Compression
By applying advanced prompt compression techniques, we reduce input length without losing essential context, minimizing token usage and lowering operational costs.
Agentic Modeling
Equipping LLMs with agent-like behaviors that can autonomously perform multi-step tasks, ideal for automated decision-making or customer service applications.
Fine-tuning of LLMs
Training the LLM on data for a specific task (e.g. QA, reasoning on specific company related), so it becomes highly adept at performing that function accurately and efficiently.