Digital Marketing Analytics

Learn more about the developer, review analytics work, or request an NLP-QA consultation.

Natural Language to SQL (Relational NLP-QA)
$ 2,500 one-time
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  • For structured databases(SQL Server, Azure SQL, etc.)
  • ✓Natural Language to SQL Query Generation
  • ✓Verified Prompt Lifecycle (Input to SQL to Output Traceability)
  • ✓Business Logic Enforcement (rules, filters, constraints)
  • ✓Schema-Aware Query Optimization (joins, aggregations, performance)
  • ✓Metadata Logging & Query Audit Trail
  • ✓High-Confidence Responses with Validation Layer
  • ✓API-Ready Deployment (Azure Function App integration)
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Agentic Email Agents
$ 3,500 one-time
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  • For Gmail and Outlook email automation
  • ✓Automated Email Ingestion & Parsing (Gmail and Outlook APIs)
  • ✓Structured Email & Metadata Logging (sender, subject, timestamps, recruiter signals)
  • ✓LLM-Powered Response Generation with Draft Email Creation
  • ✓Human-in-the-Loop Review and Send Control
  • ✓End-to-End UTM Tracking (email to website to user actions)
  • ✓Behavior Tracking (link clicks, page visits, resume downloads)
  • ✓Natural Language Q&A over Email History and Agent Activity
Custom Enterprise AI & Data Systems
$ 125 /hour
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  • For bespoke, production-grade systems and integrations
  • ✓Custom AI & Data System Architecture (end-to-end design)
  • ✓NLP-QA System Customization
  • ✓Intelligent Agents & Automation (decisioning, workflows, actions)
  • ✓First-Party Tracking & Behavioral Data Platforms
  • ✓Enterprise API Development (Azure Functions, secure endpoints)
  • ✓Fixed Bid or Time & Materials Engagement Models
  • ✓Senior-Level Execution (30+ years building production systems)

NLP-QA FAQ

What is NLP-QA?

NLP-QA is a system that lets users ask questions in natural language and receive validated answers from SQL databases or email activity managed through agentic workflows.

How does natural language to SQL work?

Natural language to SQL converts a user question into a SQL query, applies schema and business-rule controls, executes the query against a relational database, and returns a validated response with auditability.

What data sources are supported?

Supported sources include SQL Server, Azure SQL, Gmail, Outlook, email metadata, tracked user actions, and related workflow activity data.