Adam Lindsey

I build 𝒎𝒐𝒅𝒆𝒓𝒏 𝒅𝒂𝒕𝒂 𝒑𝒍𝒂𝒕𝒇𝒐𝒓𝒎𝒔 and 𝒑𝒓𝒐𝒅𝒖𝒄𝒕𝒊𝒐𝒏-𝒈𝒓𝒂𝒅𝒆 𝑨𝑰 𝒂𝒈𝒆𝒏𝒕 𝒔𝒚𝒔𝒕𝒆𝒎𝒔 that turn messy enterprise data into reliable insights and grounded automation. Most AI projects die between the demo and production - the agent hallucinates, RAG drifts, and the underlying data was never trustworthy. I fix the foundation so AI actually ships.

𝟏𝟎+ 𝐲𝐞𝐚𝐫𝐬 𝐢𝐧 𝐝𝐚𝐭𝐚 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐚𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬. 𝐒𝐜𝐚𝐥𝐞𝐝 𝐁𝐲𝐭𝐞'𝐬 data platform through hypergrowth to a $1B exit, and led analytics and data integration at Ryman Hospitality.

𝑫𝒂𝒕𝒂 𝑬𝒏𝒈𝒊𝒏𝒆𝒆𝒓𝒊𝒏𝒈

Cloud warehouses and lakehouses on Snowflake (Cortex AI, AISQL, Cortex Analyst, Cortex Search) and Databricks (Unity Catalog, Mosaic AI, Delta Lake). Transformations and semantic models in dbt. Orchestration with Dagster, Airflow, Prefect, Azure Data Factory. Ingestion through Fivetran, Airbyte, dlt, CDATA. Open table formats on Iceberg and Delta. Data quality and observability with Great Expectations and Monte Carlo. Infrastructure as code in Terraform.

𝑨𝑰 𝑨𝒈𝒆𝒏𝒕𝒔 & 𝑮𝒆𝒏𝑨𝑰

Production agents built on LangGraph, CrewAI, Claude Agent SDK, Microsoft Agent Framework (Semantic Kernel + AutoGen), Pydantic AI. LLM integrations across Claude (Opus, Sonnet), GPT-4o and o-series, Gemini, Llama, Mistral. MCP (Model Context Protocol) servers for governed tool and data access. RAG systems with hybrid search, reranking, agentic retrieval, and evaluation harnesses. Vector stores including Pinecone, Qdrant, Weaviate, Chroma, pgvector. Observability and evals through LangSmith, Langfuse, Arize.

𝑩𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝑺𝒚𝒔𝒕𝒆𝒎𝒔 & 𝑩𝑰

Integrations across NetSuite, Shopify, Salesforce, AXS, HubSpot, marketing platforms, and custom APIs. Power BI semantic models, governed metric layers, and natural-language interfaces over warehouse data.

𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬 𝐈 𝐬𝐨𝐥𝐯𝐞 - AI prototypes that fail in production, agents hallucinating without grounding, disconnected ERP/CRM/e-commerce data, fragile pipelines, dashboards no one trusts.

𝐎𝐮𝐭𝐜𝐨𝐦𝐞𝐬 - AI agents grounded in clean enterprise data, 30–50% reduction in manual reporting, production reliability with evals and guardrails built in, a single source of truth for both humans and agents.

𝐁𝐞𝐬𝐭 𝐟𝐢𝐭 𝐞𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭𝐬 - Snowflake or Databricks platform modernization, AI agent development, RAG and knowledge base builds, MCP server development, system integrations feeding BI and AI workloads, Power BI semantic layer rebuilds.