{
  "id": 744311,
  "title": "A Week as an AI Integration Consultant",
  "url": "https://urgent.news/2026/08/13/a-week-as-an-ai-integration-consultant",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-13T07:32:45.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/lamingsrb/a-week-as-an-ai-integration-consultant-4p27"
  },
  "original_language": "en",
  "account": "A week-long experience working as an AI integration consultant reveals that the majority of the work involves mapping data flows, selecting the appropriate glue for integration, building a retrieval layer, and implementing guardrails to ensure the system performs as expected. The consultant emphasizes the importance of identifying the most reliable source of customer data, often found within an outdated MySQL application, before integrating any AI components. This step is crucial to prevent the AI assistant from hallucinating information and causing potential support issues. The consultant prefers using an EventBridge bus with Lambda workers and a linear workflow for most tasks, as these approaches are more debuggable and reliable compared to more complex agentic tasks. On the retrieval side, the consultant built a system that pulls data from various sources such as Confluence, Zendesk tickets, and PDF manuals, normalizes the information, and stores it in a Postgres database with embeddings and full-text search capabilities. This hybrid retrieval approach, using both vector and lexical results, significantly improved the assistant's performance in answering historical questions. The consultant also highlights the importance of implementing guardrails, such as ensuring idempotency, providing audit logs, and handling malformed JSON responses, which are often overlooked but essential for maintaining the integrity of the AI system.",
  "summary": "A Week as an AI Integration Consultant Most weeks I don't write much new code. I read other people's systems, draw arrows on a whiteboard, and try to figure out which of the twelve places customer data lives is the one I should actually trust. That is the honest shape of AI integration work in a B2B company that has been shipping since 2014. The LLM is the easy part. The plumbing is the job. Here…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}