The Plumber's Roadmap

The Audit

We map your "Data Graveyard"—identifying legacy SQL databases, messy SharePoint folders, siloed PDFs, and forgotten Confluence pages.

• 01 Inventory all data sources and access patterns
• 02 Identify data quality issues and gaps
• 03 Define priority use cases with stakeholders
• 04 Assess infrastructure and security requirements

Typical Findings

Custom Architecture

We design a custom Chunking Strategy. Different data needs different logic—a 200-page legal brief is indexed differently than a clinical trial summary table.

• 01 Design document-type-specific chunking rules
• 02 Select embedding models for your domain
• 03 Configure re-ranking and retrieval logic
• 04 Design the infrastructure (Infrastructure as Code, Containers)

Sample Chunking Decision

// Legal Contract
{
  strategy: "clause-aware",
  chunk_size: 1500,
  overlap: 200,
  split_on: ["ARTICLE", "SECTION"],
  preserve: ["definitions", "headers"]
}

// Clinical Trial PDF
{
  strategy: "table-preserving",
  chunk_size: 800,
  overlap: 100,
  extract_tables: true,
  preserve: ["figure_refs", "citations"]
}
          

The Deployment

The "Ninja" phase. We spin up the infrastructure (Infrastructure, Database, AI) and deploy a minimalist frontend dashboard for your team.

• 01 Deploy vector database (Vector Database)
• 02 Run initial document ingestion pipeline
• 03 Connect LLM (LLM of choice)
• 04 Launch internal dashboard + user training

Deployment Checklist

The Pulse

Our maintenance engine monitors for drift, updates embeddings as new documents are added, and patches the LLM as better models emerge.

• ↻ Continuous re-indexing of new documents
• ↻ Weekly performance reports
• ↻ Security patches and dependency updates
• ↻ Model upgrades (e.g., Model v1 → Model v2)

Monthly Maintenance Report