Trustable AI Interactions Engineering Manifesto

by Shahid N. Shah

This whitepaper educates engineering teams to build trustworthy AI-native systems by mastering the discipline of knowledge transformation and context engineering. Teams will learn how to convert enterprise documents from traditional formats (PDF, Word, Excel) into semantically structured, provenance-enriched Markdown or HTML - the canonical trust layer for LLM-powered applications.

Rather than treating files as static content, the strategy reframes every document as a unique knowledge container that must be unpacked, structured, cited, and verified. Engineers will learn why AI-native systems require custom per-document scripts (not per file type), how to embed traceable metadata into every chunk, and how to design for downstream compatibility with LLMs, ontology frameworks, and graph-based retrieval.

Designed for TypeScript-first teams (with Python alternatives), this hands-on strategy equips engineers with reusable Deno-based tooling, integration best practices, and strategic thinking around trust, explainability, and auditability in enterprise-grade AI systems.

Key takeaways:

• Why Markdown + Frontmatter or HTML with rich semantics is the only defensible knowledge format for trustworthy AI
• How to treat PDFs, Word, and Excel not as formats - but as knowledge interfaces
• How to write custom document transformers in TypeScript/Deno or Python
• How to embed provenance, structure, and intent into Markdown or HTML for citation and MCP usage
• How to prepare data for ontology mapping, vector stores, and graph-based retrieval (Graph RAG)
• How to integrate structured knowledge into context engineering workflows for LLMs

This strategy is not about AI model tuning - it’s about the foundation every trustworthy AI system is built on: structured, contextual, explainable knowledge.

Architecture Overview

Table of Contents

1. Trustworthy AI in B2B Systems
2. Expectations Engineering and UAT
3. Markdown and HTML as Trust Layers in Document AI
4. Markdown-Native Source Document Strategy (with HTML as a peer option)
5. Custom Document Transformation Script
6. Context Engineering and AI Prompting
7. Vector Database and Enterprise RAG Strategy
8. Multi-Layered Retrieval for Verifiable AI
9. Conversational UX (Chat UIs)
10. Feedback Loops Improve AI Interactions over time
11. surveilr as Ingestion layer
12. surveilr as SQL-Native Grounding Infrastructure
13. Backend Design with MCP and AnythingLLM
14. Roles in AI-Native Engineering
15. AI Gateway
16. Text to SQL
17. Spry: Literate Programming with Executable Markdown

Extended Reading & Case Studies

In this project, we are not inventing a new AI algorithm or platform from scratch – we are assembling best-in-class tools and practices into a cohesive, trustworthy system. The emphasis on expectations, structured data, and verification is what will set our solution apart and make it enterprise-grade. Always remember: start with understanding the user’s needs (the why), prepare the knowledge and context (the what), and only then implement the technical solution (the how). This ensures we solve the right problem the right way.

The strategy we’ve outlined is designed to make the system auditable and maintainable. Any answer the AI gives can be traced back to a Markdown or HTML source document and even a specific section, which means external auditors or internal reviewers can verify it. We’ve baked in processes to minimize hallucinations and enforce citations so that users gain trust in the system from day one. By using open-source components (for UI, orchestration, databases), we also ensure we’re not locked into proprietary limitations and we can tweak things as needed. The role breakdown shows that our team’s identity is as Integration Engineers who glue components together and Expectation Engineers who align technology with business needs – rather than pure “AI engineers” chasing the latest model. This is a crucial distinction; it keeps us focused on delivering value quickly and reliably.

Moving forward, as we implement this plan, keep communication open. Regularly review the UAT cases against what’s being built to confirm we’re on track. If something isn’t working (e.g., the vector search isn’t accurate enough, or a document is too messy), pause and address it – don’t just hack around it, since that can erode trust later. Given our small team size, prioritize tasks that have the highest impact on end-user experience (for example, getting citations right is more important than a fancy UI animation).

Finally, remember that simplicity is not our enemy; it’s our goal. A simpler system is easier to understand and trust. Every time we consider adding a complex component, we ask: is there a simpler way that achieves the goal? This mindset will serve us well, especially in maintenance and scaling phases. By following this comprehensive strategy and adhering to these principles, we will deliver an AI Q&A solution that is not only effective but also transparent and dependable.

Let’s build this step by step, always keeping in mind the end-users – busy B2B professionals who just want correct, quick answers with evidence. By making things understandable, maintainable, and verifiable at each stage, we ensure that the final product meets their needs and earns their confidence. We have the plan and the tools; now it’s about disciplined execution and continuous learning. Let’s get to work on creating an AI assistant the users will love and trust, using the roadmap we’ve laid out.