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DBAI

Insights

How we think about AI systems

No invented numbers. Everything below is either DBAI's own argument or grounded in a system that's actually running — cross-linked to the case study or service page that proves it.

The AI Buyer’s Guide

Make the right call before you build.

Practical guides for business owners choosing where to start with AI, what to buy, what to build, and who to trust with the work.

Explore the buyer’s guide
New operating guide

Claude plans. Codex builds. Show me that it works.

The complete DBAI workflow for using Claude Code to plan and review while Codex builds, tests, and returns evidence.Read the guide

Buying AI

  1. Multi-Client AI Systems: Access Questions to Ask Before Buying

    Ask for evidence across search, conversations, actions, exports, and changing permissions before an AI workflow serves multiple clients.

  2. AI Workflow Adoption: Get Your Team Ready to Use It

    A practical rollout plan for operators: practice real tasks, handle exceptions, and turn feedback into fixes.

  3. What Should Happen When an AI Workflow Fails?

    Define recovery before launch: what to retry, what to reconcile, and when a person takes over.

  4. What to Bring to an AI Project Discovery Call

    Make your first AI project conversation useful with real context, sanitized examples, and a clear decision to make next.

  5. Who Maintains an AI Automation After Launch?

    Define who handles failed runs, changing models, source updates, and output quality before the system becomes part of daily work.

  6. When Your Business Needs a Dashboard Before an AI Agent

    Define the metric, connect the source records, and make discrepancies visible before adding generated summaries.

  7. AI Catalog Automation: Keep Product Answers Grounded

    A convincing answer is not enough. Connect product questions to exact variants, approved facts, and a clear rule for stale or missing data.

  8. Automating Quote Intake Without Inventing Prices

    Turn emails and attachments into reviewable quote inputs, with missing fields visible and prices calculated from approved rules.

  9. How to Measure AI Automation ROI Without Guessing

    Hours saved, lower spending, and additional output are different benefits. Measure each one against a comparable baseline before claiming ROI.

  10. Where Human Approval Belongs in an AI Workflow

    An Approve button is only useful when it controls a specific action. Define the decision, the reviewer, and what happens when the request changes.

  11. AI CRM Integration: Questions to Resolve Before a Build

    Connecting the CRM is only the start. Define who owns each field, which changes need approval, and what happens when a request runs twice.

  12. Internal AI Knowledge Base: A Practical Planning Guide

    Turn scattered company documents into answers your team can check. Start with sources, permissions, and a plan for unanswered questions.

  13. AI Agent vs Chatbot: Which Does Your Business Need?

    Choose by the work to be done: answer a question, follow known steps, or investigate and act within limits.

  14. Is Your Business Data Ready for AI Automation?

    Map sources, owners, record IDs, and stop rules before commissioning an AI workflow.

  15. AI Pilot Acceptance Criteria: A Practical Business Checklist

    Turn “the demo looks good” into a checkable decision: expected outputs, approval boundaries, review effort, and a copyable pilot brief.

  16. AI Automation for Small Business: Where to Start

    Choose your first AI automation project with a workflow scorecard, pilot checklist, and examples from DBAI’s apparel and operations work.

  17. Custom AI Agents vs. Off-the-Shelf Tools: Buy or Build?

    Compare custom AI agents and ready-made tools by workflow fit, integrations, permissions, ownership, and the real cost of operating them.

  18. What Does an AI Automation Project Cost?

    Understand AI automation project costs: discovery, integration, review, hosting, model usage, and support. Use a worksheet to compare proposals fairly.

  19. How to Choose an AI Automation Agency

    Evaluate an AI automation agency using relevant work, a real workflow test, integration details, ownership terms, and a clear plan for support.

Agents & Workflows

  1. Claude Plans. Codex Builds. Show Me That It Works.

    Connect Claude Code and Codex, define the handoff, and make verified results the standard for every AI-assisted build.

  2. Above the Stack: The AI Orchestration Layer

    Why the layer that unifies a business's existing vendors is a stronger position than selling it a thirteenth tool.

  3. Task-Specific Agents, Wired Into Real Workflows

    How narrow, well-scoped agents get wired into a working process — shown through Alice and three real client systems.

AI Security

  1. Malware Supercharged: The Rise of AI-Generated Malware

    Signature detection assumes a known sample. AI-generated malware doesn't hold still — so detection has to change too.

Private AI Infrastructure

  1. Private AI Infrastructure Deployment: Define the Data Boundary

    Choose private AI infrastructure by where data is processed, who can access it, and how the system behaves under load and failure.

Finance & Reporting

  1. Automating Month-End Close Across Multiple Entities

    The four handoffs that slow a multi-entity close, and how to take them out without replacing the ERP.

AI Governance

  1. Governed, Auditable AI For Mid-Market Companies

    Five controls wired into the system itself, who owns each one, and what evidence to keep.

Bring us the workflow, the infrastructure question, or the system that has to prove itself.

We'll show you what DBAI would actually build.

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