Enterprise AI platforms integrated
LLM ecosystems supported
Reduction in AI hallucination risks via VICE framework validation
Enterprise AI use cases enabled
DataXWorks defines scalable AI architectures aligned to organizational goals using platforms such as Databricks, Snowflake, Azure, AWS and Google Vertex AI.
Establish structured evaluation frameworks using LangSmith, DeepEval and OpenAI Evals to measure accuracy, relevance, consistency and hallucination rates.
Benchmark and optimize proprietary and open source models, including OpenAI, Claude, Gemini, Llama, Hugging Face etc., to identify the best fit for performance, governance and cost.
Implement prompt optimization, fine tuning and feedback driven improvements.
Design Retrieval Augmented Generation (RAG) solutions using LangChain, LangGraph to connect foundation models with enterprise data, documentation and knowledge repositories.
Build governance frameworks with Databricks Unity Catalog, Snowflake Cortex AI, LangSmith and AWS Bedrock Guardrails to support transparency and responsible AI adoption.
Assess business objectives, data readiness, regulatory needs, and enterprise AI goals to define the right LLM architecture and implementation roadmap.
Build LLM-powered AI capabilities with model selection, RAG implementation, prompt engineering, and enterprise system integration.
Validate LLM outputs for factual accuracy, relevance, consistency, hallucination risks, and business reliability through structured evaluation workflows.
Monitor LLM performance, feedback loops, audit controls, and governance frameworks to support continuous optimization and long-term AI reliability.
Connect with our LLM expert to discuss your business goals, model requirements and roadmap for building an enterprise ready LLM solution.
