Beyond the Chatbot
The enterprise adoption of Artificial Intelligence has moved far beyond simple chatbots. By mid-2026, over 40% of Fortune 500 companies have deployed autonomous agents: systems capable of reading proprietary data, making logical decisions, and executing tasks across external APIs without human intervention. To build these systems reliably with sub-100ms inference times, strict architectural discipline is required.
Why TypeScript is Mandatory for AI
Language Models (LLMs) are inherently unpredictable. When you ask an AI model to return JSON data to trigger an API, it might hallucinate a field name or change a data type. This is catastrophic for automated workflows.
Enforcing Strict Schemas
By leveraging TypeScript AI orchestration, we enforce strict type checking on all LLM outputs using libraries like Zod and TypeChat. If the AI returns a payload that does not match the exact TypeScript interface required by the downstream system, the orchestration layer automatically rejects the payload and prompts the AI to correct its formatting. This guarantees type safety in a highly non-deterministic environment.
Retrieval-Augmented Generation (RAG)
Autonomous agents are only as intelligent as the data they can access. We architect custom RAG pipelines utilizing PostgreSQL with pgvector optimizations (now heavily optimized in PG17). This allows custom AI agents to instantly query millions of private enterprise documents, transforming unstructured data into actionable intelligence natively within the database layer.
The Orchestration Layer
At Dazeco, we don't just plug into the OpenAI API. We engineer resilient, type-safe AI infrastructures that fundamentally automate your most complex enterprise operations. Leverage AI to scale your execution, not your headcount.