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AI Capabilities

Practical AI Integrated Into Real Business Software

AI-Assisted Development

AI may help accelerate implementation, testing, documentation, refactoring, research, debugging, and repetitive technical work. The developer remains accountable for all delivered work: architecture, requirements, security, business logic, data design, code quality, testing, deployment, and production readiness.

Customer Communication

  • Website chat assistants
  • Customer question answering
  • Lead qualification
  • Guided intake
  • Service guidance
  • Escalation to staff

Internal Knowledge

  • Search company policies
  • Ask questions across internal documents
  • Find procedures
  • Summarize information
  • Help employees locate approved answers

Document Processing

  • Extract structured information
  • Categorize documents
  • Summarize content
  • Identify missing fields
  • Route documents
  • Compare documents
  • Draft responses

Decision Support

  • Apply defined conditions
  • Surface relevant information
  • Flag exceptions
  • Recommend actions
  • Prioritize work
  • Identify patterns

Important financial, legal, medical, employment, safety, or compliance decisions should not be fully delegated to an AI model without appropriate safeguards and human review.

Reporting and Analysis

  • Natural-language questions
  • Management summaries
  • Trend explanations
  • Anomaly detection
  • Narrative reports
  • Draft analysis

Workflow Automation

  • Read incoming messages
  • Categorize requests
  • Route work
  • Prepare drafts
  • Update records
  • Trigger approved workflows
  • Request human approval

AI Ownership and Cost Control

  • The client creates and owns the provider account whenever practical.
  • The client owns the API key.
  • The client pays provider usage charges directly.
  • The client can view usage.
  • The client can set limits where supported.
  • Keys can be rotated or revoked.
  • Secrets are stored server-side.
  • Keys are never committed to source control.
  • Provider replacement may be possible, but complete portability cannot always be guaranteed.

AI Safety and Reliability

  • AI output is probabilistic.
  • Responses may be incorrect.
  • Sensitive data requires careful handling.
  • Provider retention policies matter.
  • Logging must be controlled.
  • Human review may be required.
  • Deterministic business rules should remain ordinary code where practical.
  • AI should not be used where simple logic is more reliable.