The Problem: AI Query Errors Cost Enterprises Billions
AI-driven query errors are not just theoretical; they have real-world financial implications. For instance:
AI Project Failures: A RAND Corporation study found that 80% of AI projects fail, leading to over $11 billion in direct project failures annually.
Text-to-SQL Failures: A case study identified three main failure modes of text-to-SQL models on real-world applications: long context length, unclear question formulation, and more complex queries. These issues resulted in significant performance degradation in enterprise environments.
These examples highlight the challenges enterprises face when integrating AI with databases without proper governance and semantic layers.
How SED Solves This
SED acts as a semantic firewall, translator, and governance layer between AI and your databases.
🔹 Business Logic Enforcement & Semantic Layering: SED maps your raw data into clear business terms (metrics, KPIs, dimensions), so AI queries always reflect the true business meaning.
🔹 Automatic Query Rewriting: SED intercepts AI-generated queries and rewrites them to follow your business rules and access policies, preventing unsafe or costly queries before they hit the database.
🔹 Policy Engine for Governance: Enforces fine-grained access controls and compliance policies, ensuring each AI query respects role-based permissions and audit requirements.
🔹 Schema Change Detection: When your database schema changes, SED detects these and adapts AI queries automatically, avoiding breakages or stale data retrieval.
Why Existing Solutions Fall Short
Vector Databases: While vector databases are optimized for similarity search, they often lack built-in governance, access controls, and compliance auditing. This oversight can lead to data leakage risks and compliance issues, especially when embeddings are reverse-engineered.
Business Intelligence (BI) Tools: Traditional BI platforms mainly serve human analysts and rely on rigid semantic models that struggle to keep up with dynamic AI-driven, natural language queries. Their architectures are not built to handle AI intents directly, leading to inaccurate or incomplete query results, limited flexibility, and challenges scaling governance in AI workflows.
Custom APIs: Integrating legacy systems with AI workflows often presents difficulties due to outdated data formats and limited documentation. These systems may lack modern APIs, complicating the connection with AI workflows.
Traditional Semantic Layers: While semantic layers aim to provide a unified and consolidated view of data, they often lack AI-native features like intent parsing, query rewriting, and automatic schema adaptation. This gap can hinder the effectiveness of AI-driven workflows.
SED fills this gap by providing an AI-native semantic layer that ensures governed, business-accurate, and compliant AI-to-database interactions, securely and locally.
How SED Works:
SED is a local CLI tool that sits between your AI layer and your databases, acting as a lightweight proxy to enforce governance and maintain your business logic and semantic layer on-premises. No cloud dependencies, no data exfiltration.
When AI sends a query:
Intercept: Captures query intent (natural language or SQL).
Parse & Validate: Checks against your business rules and data schema.
Rewrite: Modifies queries to enforce access controls and optimize performance.
Execute & Log: Runs the safe query on your database and logs activity locally for audit and compliance.
This ensures your data governance stays under your control, with no data leaving your environment
Bottom Line: Smarter AI, safer data, no hassle
SED is your essential guardrail to unlock AI’s potential safely and reliably, protecting privacy, so you can make data-driven decisions you can trust.
Try SED Today
I have built an MVP and would love your feedback.
Visit www.sed.services to learn more and join the journey.
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