Read Executive Outcomes of Our Generative AI Consultation Engagements
Discover how our expert generative AI solutions equip our enterprise clients to streamline workflows and boost productivity.
Formulating an Enterprise Generative AI Strategy
Evaluated building proprietary base model from scratch vs fine-tuning open-source LLMs vs licensing commercial foundation models. Selected approach balancing security, performance, cost, speed-to-market and risk management.
Conducted landscape analysis of generative AI vendor solutions, examining technical capabilities, task specializations, differentiated offerings, limitations and risks.
Benchmarked performance of leading generative AI foundation models on key metrics, such as: accuracy / performance, F1 score, generalization, latency, safety, explainability and auditability.
Analyzed the AI strategies of industry-leading companies building their in-house generative AI assistants and fine-tuning their LLMs on proprietary data. Identified key lessons on model architecture and engineering, training methodologies, cloud infrastructure and skills.
Defined high-impact business use cases where conversational AI can boost employee productivity, enhance customer experience and increase operational efficiency.
Outlined an agile roadmap to build, deploy and iterate on generative AI, balancing business impact, risk management and technical complexity.
Adopted a hybrid approach combining Supervised Fine-Tuning (SFT) as a strong starting point and Reinforcement Learning from Human Feedback (RHLF) for iterative continuous improvement.
Architected a secure, scalable AI infrastructure on AWS to enable enterprise-grade model development, training, deployment, inference and monitoring. Tech stack includes: Bedrock, SageMaker, EC2 (Trainium 1 & Inferentia 2), Redshift Spectrum, Athena, S3, Glue, Lambda, Python and PyTorch.
Provided recommendations on team structure, roles, skills and culture needed to effectively and responsibly scale generative AI across the organization.
Architecting Scalable Large Language Model (LLM) Retrieval-Augmented Generation (RAG) Infrastructure Leveraging Industry-Leading Vector Database
Recommended industry-leading cloud-native vector database solution based on performance benchmarks and empirical assessment of Pinecone vs Chroma vs Amazon OpenSearch vs Amazon RDS for PostgreSQL pgvector.
Architected an end-to-end AI infrastructure for large language model (LLM) retrieval-augmented generation (RAG).
Enabled storage and retrieval of massive volumes of high-dimensional vector data.
Optimized architecture for ultra-fast similarity searches on vector embeddings.
Established robust LLMOps pipeline from raw data and vector embeddings to model training and model deployment.
Outlined guidelines for migrating from traditional databases to vector database.
Delivered customized training sessions to skill up data scientists on leveraging vector databases.
Identified opportunities to optimize vector database costs through dynamic provisioning.
Enhanced vector database scalability to support growing data volumes and evolving data retrieval patterns.