Data consultancies sell to engineering and analytics leaders who speak in stack names and outcomes, not abstract "data transformation" language. A prospect lands and wants to confirm four things in under two minutes: do you work in our warehouse (Snowflake, BigQuery, Databricks, Redshift), do you handle our transformation layer (dbt, Coalesce, Dataform), how have you sized engagements at our data volume, and what does typical reporting and observability look like during delivery. SleekAI reads your services, case studies, and stack-specific pages so the bot answers those four with real engagement detail.

The data path is plain WordPress. Case studies live as a custom post type with ACF fields for warehouse, transformation tool, orchestration layer (Airflow, Dagster, Prefect), reverse-ETL, BI tool, data volume band, engagement type (modernization, migration, modeling, observability, governance), and outcomes (query latency, cost reduction, reporting accuracy). The data-source wizard maps it into context, so the bot quotes "Snowflake plus dbt modernization for a 4TB warehouse, 38% Snowflake cost reduction in 90 days" instead of generic claims.

Routing splits inquiries by engagement type. Warehouse migrations to one partner, dbt modeling and refactor to another, observability and governance to a third, audits to a self-serve form. Conversation logs in wp_posts become a quiet feed of which warehouse and tool combinations prospects keep asking about, which is useful signal for the consultancy's case study production and content plan.

Data consultancies sell to engineering and analytics leaders who evaluate practices through a very narrow lens: warehouse, transformation tool, orchestration, BI tool, data volume, and reported outcomes by engagement type. A head of data lands on a consultancy site and wants to confirm in under two minutes that the practice has handled Snowflake plus dbt at their data volume with credible cost or latency outcomes. Most consultancy sites flunk that test by accident.

The case studies are organized by client logo or industry rather than by warehouse and transformation tool, and the homepage talks about "modern data stack delivery" because that's the breadth pitch. The result is technical buyers bouncing on a positioning gap they could have resolved in three messages, and the practice never seeing the lost deal. A semantic chatbot fixes the gap because it reads the structured fields on the case study CPT and answers a stack-shaped question with a stack-shaped reply.

The Snowflake plus dbt prospect gets the Snowflake plus dbt case study with the cost reduction number, the engagement timeline, and the volume band. Routing pulls the second lever. Data practices usually have at least four intake flows hiding behind one Contact form: warehouse migrations, dbt modeling and refactor, observability and governance, and audits.

Each one belongs with a different partner. A bot that asks two qualifying questions and sends each prospect to the right partner compounds conversion across the entire book. The logs add a quiet third benefit.

Data practices live on engagement productization, and the conversation logs become a structured feed of which warehouse and tool combinations prospects expect the practice to cover but can't easily confirm from the site. Every stack pairing that shows up three times in a month is either a service page worth sharpening or a case study worth publishing next quarter.