AI Service Offerings

Foundation Services

Data Sourcing / Scraping for AI

• Monthly ongoing engagements
• Web scraping, API ingestion, document collection

Data Preparation for AI

• Getting data and documents into format for AI consumption
• Cleaning, structuring, and enriching raw data for model readiness

Development & Tooling

Specific AI "Helpers"

• Create AI specifically around API or development bottlenecks
• Example: Scraped, prepared, and gave Planview API docs to AI to help speed development

Custom Agent Development

• Task-specific agents that chain tools together (e.g., query API → transform data → load)
• Internal ops agents for clients: ticket triage, report generation, alert summarization

Prompt Engineering & Workflow Design

• Design and document prompt libraries for client teams
• Build structured AI workflows (e.g., intake → classify → route → summarize) integrated into existing tools

Rapid Off-Prem Data Mocking

• Provide a DDL — we replicate synthetic data quickly in a cloud-hosted environment
• Stand up example dashboards and reports against mocked data for fast proof-of-concept work
• Enables teams to prototype, demo, and validate without touching production systems

Collaboration & Enablement

Collaborative AI "Playgrounds"

• OpenWebUI
• Shared sandboxes for teams to experiment with models and prompts

Advisory & Assessment

AI Readiness Assessments

• Audit a client's data estate and score how "AI-ready" their data is (completeness, structure, accessibility)
• Deliver a roadmap with prioritized prep work — feeds directly into data preparation services

AI Data Discovery, Quality & Governance

• AI-powered data profiling — automatically detect anomalies, drift, and quality issues across client datasets
• Automated data lineage and metadata mapping
• AI-driven data discovery — surface hidden relationships, key entities, and usage patterns across an organization's data estate
• Auto-generate visualizations and data catalogs to accelerate understanding of unfamiliar datasets

Managed AI Solutions

Natural Language SQL via MCP

• Integrate client data into relational tables and connect an MCP-enabled AI layer
• Users ask questions in plain English — AI writes the SQL, executes it, and displays results
• Supports self-service analytics for non-technical stakeholders without BI tool training

RAG-as-a-Service

• Stand up Retrieval-Augmented Generation pipelines over client internal docs, wikis, Confluence, SharePoint, etc.
• Managed knowledge bases that stay current — combines data sourcing + preparation strengths

AI-Assisted Data Migration & Modernization

• Use LLMs to analyze legacy schemas (e.g., AS/400 / QSYS2) and recommend target architectures
• Auto-generate transformation mappings between source and target systems

Fine-Tuning & Evaluation Services

• Fine-tune small/open models on client-specific data for domain tasks (classification, extraction, summarization)
• Build evaluation harnesses so clients can measure model quality over time

Monitoring & Observability for AI

• Track model usage, cost, accuracy, and drift for clients running AI in production
• Dashboards and alerting — natural extension of data services