Building a deterministic validation layer for Banking and Healthcare using Vertex AI and Knowledge Graphs.
We are currently witnessing the “Trust Gap” in Generative AI.
For creative tasks — writing emails, summarizing PDFs, or coding assistants — models like Gemini Pro models are phenomenal. Standard RAG (Retrieval Augmented Generation) works well here because being “mostly right” is usually acceptable.
But what happens when you deploy an agent into Banking or Healthcare?
If a user asks: “Can I take Lisinopril with Potassium supplements?”, and the LLM hallucinates a “Yes,” it’s not a minor error — it is a life-threatening safety violation. If a Banking Agent advises a retiree to buy high-volatility crypto, it is a regulatory compliance breach (AML/KYC).
In these high-stakes industries, probabilistic accuracy (99%) is not enough. We need deterministic certainty (100%).
In this post, I will share an architectural pattern I built called the Truth Anchoring Network (TAN). It is a Neuro-Symbolic layer that forces Gemini to adhere to strict business logic (OWL Ontologies) before responding to the user.
The Concept: Neuro-Symbolic AI
Standard GenAI is Probabilistic. It predicts the next likely word based on training data.
Old-school AI (Expert Systems) was Deterministic. It followed strict IF-THEN rules but couldn’t understand natural language.
Neuro-Symbolic AI combines both:
- The Neural Layer (Gemini): Handles the messy human language, entity extraction, and conversation.
- The Symbolic Layer (Ontology): Stores the rigid “Ground Truth” rules (e.g., Drug A interacts with Drug B or Customer Type X cannot buy Product Y).
By treating the Ontology as a “Validation Guardrail,” we can catch hallucinations before they reach the user.
The Architecture on Google Cloud
The solution is designed to scale within the Google Cloud ecosystem. While standard grounding fetches text chunks (RAG), this architecture fetches logic constraints.
To achieve enterprise-grade reliability, we move beyond simple scripts by leveraging Vertex AI Agent Builder for orchestration and Google Spanner Graph for high-performance rule storage.
The Workflow:
The system follows a strict pipeline to separate “Reasoning” from “Compliance.”
Here is the step-by-step process:
- User Query: The user asks a critical health question via the interface: “Can I take ibuprofen if I am on warfarin?”
- Entity Extraction (Vertex AI): We use Gemini Pro not to answer immediately, but to parse the natural language and identify key entities (e.g., Drug A: Warfarin, Drug B: Ibuprofen).
- Logic Lookup: The system uses these entities to query the Knowledge Graph (OWL Ontology). In a production environment, this queries Google Spanner Graph to check for specific “Edge” relationships (e.g., Has_Severe_Interaction).
- Neuro-Symbolic Validation: The agent compares Gemini’s draft response against the strict clinical rules returned by the graph.
- Enforcement:
- BLOCK: If the graph returns a CRITICAL signal (e.g., Warfarin + Ibuprofen = High Bleeding Risk), the system overrides the LLM and issues a hard refusal or safety warning.
- SAFE: If the graph returns no adverse edges, the LLM is allowed to generate the response.
Note on Scalability: While this Proof of Concept uses local .owl files for simplicity, the architecture is designed to swap the backend for Google Spanner Graph. This enables low-latency querying (<30ms) of millions of compliance rules, satisfying the strict SLAs required by Banking and Healthcare enterprises.
The Implementation
We use Python to bridge the gap between the Gemini API and the OWL Ontology. Here is how the “Validation Logic” works under the hood.
Instead of just letting the LLM chat, we inject a validation step:
def validate_compliance(user_query, entities):
"""
Cross-reference LLM intent with Ontology Rules.
"""
# 1. Query the Knowledge Graph for strict rules
risk_level = ontology.get_interaction_risk(entities)
# 2. Check for "Hard Fail" conditions
if risk_level == "SEVERE":
return {
"status": "BLOCKED",
"reason": f"CRITICAL: Interaction detected between {entities}."
}
# 3. If safe, allow Gemini to generate the response
return {
"status": "APPROVED",
"context": "No known interactions found."
}
This ensures that the “Thinking” (LLM) never overrides the “Rules” (Ontology).
Results: Before vs. After
Let’s look at a real-world Medical Safety scenario.
The Query: “Can I take Lisinopril with Potassium supplements?”
❌ Without Validation (Standard LLM)
“It is generally safe to take supplements, but you should consult your doctor. Potassium is essential for health…”
(Risk: The LLM misses the specific contraindication between ACE inhibitors and Potassium.)
✅ With Neuro-Symbolic Validation (TAN)
TAN VALIDATION REPORT:
- Entities Detected: Lisinopril (ACE Inhibitor), Potassium_Supplement
- Ontology Check: Interaction_Severity: SEVERE
- Description: Risk of Hyperkalemia.
Final Output: “⚠️ WARNING: This combination is FLAGGED. Lisinopril can increase potassium levels. Taking supplements may cause dangerous Hyperkalemia. Consult a physician immediately.”
Why This Matters for Enterprise
For Google Cloud customers in regulated industries, “Hallucination” remains the biggest barrier to Generative AI adoption. Banks and hospitals cannot afford “mostly correct” answers.
By moving from pure RAG to a Neuro-Symbolic architecture on Vertex AI, we can unlock:
- Auditability: Every decision can be traced back to a specific rule ID.
- Safety: Compliance is enforced mathematically, not probabilistically.
- Scalability: By leveraging Google Spanner Graph, this logic allows enterprises to manage millions of complex rules with low latency.
This hybrid approach finally bridges the gap between the creative power of LLMs and the strict requirements of the enterprise.
💻 Try the Code
The code for this Proof of Concept is open source. You can clone the repo and try the “Drug Interaction” and “Banking Compliance” demos yourself.
GitHub Repo: github.com/sadanandsl/gemini-neuro-symbolic-agent
(If you found this architecture useful, please consider giving the repo a ⭐ star!)
Beyond RAG: Solving “Compliance Hallucinations” with Gemini & Neuro-Symbolic AI was originally published in Google Cloud – Community on Medium, where people are continuing the conversation by highlighting and responding to this story.
Source Credit: https://medium.com/google-cloud/beyond-rag-solving-compliance-hallucinations-with-gemini-neuro-symbolic-ai-b48fcd2f431f?source=rss—-e52cf94d98af—4
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