Compliance Intelligence
Ask your compliance data anything
GraphRAG-powered reasoning answers questions like “Why must this facility file?” with cited, explainable answers grounded in your actual compliance data — not hallucinated guesses.
The Problem
Your compliance data has answers. You just can't ask it.
When an auditor asks “Why does this facility file Tier II for Sulfuric Acid?”, the answer lives across six disconnected systems: the SDS library, the inventory spreadsheet, the threshold lookup, the state portal rules, the LEPC contact list, and someone’s email. Compliance Intelligence collapses that into a single conversational query with a fully cited evidence chain.
6+
Systems queried per obligation question
45 min
Avg. time to trace one filing rationale
2–5s
SafeGenics answer with full citations
100%
Answers grounded in your graph data
Live Interface
Natural language meets compliance graph
Ask questions in plain English. The system analyzes your intent, traverses the Compliance Intelligence Graph, and returns cited answers with the full evidence chain — so every response is traceable back to your source data.
- Compliance Intelligence — Facility: Plant A
Why must this facility file Tier II for Sulfuric Acid?
Plant A must file Tier II for Sulfuric Acid (CAS 7664-93-9) because its maximum on-site quantity of 12,400 lbs exceeds the EPCRA §312 reporting threshold of 10,000 lbs for non-EHS hazardous chemicals.
The quantity was computed from ingredient decomposition of 3 products in your inventory: Industrial Cleaner Pro (38% H₂SO₄), Battery Acid Concentrate (93% H₂SO₄), and pH Down Solution (15% H₂SO₄).
SDS v2.1 → CAS 7664-93-9 → Inventory Snap 2026-01 →Threshold 40 CFR §370.10
Which products contribute the most?
Battery Acid Concentrate contributes 8,370 lbs (67.5%), followed by Industrial Cleaner Pro at 3,040 lbs (24.5%) and pH Down Solution at 990 lbs (8.0%).
Inventory Snap 2026-01 → ingredient_inventory decomposition
Ask anything about your compliance data…
Every answer is grounded
Unlike generic AI chatbots that guess at compliance answers, SafeGenics traverses your actual compliance graph — following edges from chemicals to SDSs, inventory snapshots to thresholds, facilities to jurisdictions — and constructs answers from real data.
Every claim in the response links back to a specific node or edge in the graph. No hallucinations. No disclaimers. Just your data, explained.
How It Works
From question to cited answer in seconds
Compliance Intelligence combines intent analysis, graph traversal, and answer generation into a pipeline that returns explainable answers grounded in your compliance data — not a general-purpose language model’s best guess.
01
Intent Analysis
AI parses your natural language question to extract entities (chemicals, facilities, regulations), determine query type (threshold check, obligation explanation, inventory review), and identify the graph traversal pattern needed.
“Why must this facility file for Sulfuric Acid?” → entity: Sulfuric Acid (CAS 7664-93-9), context: Facility A, intent: obligation_explanation, traversal: facility→inventory→chemical→threshold→regulation
02
Graph Retrieval
The system traverses the Compliance Intelligence Graph, following typed edges between entities: Facility → Inventory → Chemical Identity → SDS → Threshold → Regulation → Obligation. Multi-hop queries retrieve connected subgraphs spanning up to 6 relationship hops.
Retrieved subgraph: 1 Facility node, 3 Inventory records, 1 Chemical Identity (CAS 7664-93-9), 3 SDS versions, 2 Threshold nodes (EPCRA §312, state-specific), 1 Obligation node with status=TRIGGERED
03
Context Assembly
Retrieved graph nodes and edges are serialized into a structured context window with entity properties, relationship types, and computed values (ingredient-level quantities from SDS decomposition). This context replaces generic RAG chunk retrieval with precise, relational data.
Context payload: { facility: “Plant A”, chemical: { cas: “7664-93-9”, totalQty: 12400, unit: “lbs”, sources: […] }, threshold: { value: 10000, regulation: “40 CFR §370.10” }, exceedance: 2400 }
04
Answer Generation
The LLM generates a natural language answer constrained to the retrieved context — it cannot reference information outside your graph. Every claim is tagged with the source node or edge it came from, producing an audit-ready evidence chain.
Output: Natural language answer + evidence_chain: [SDS v2.1 → CAS 7664-93-9 → Inventory Snap 2026-01 → Threshold 40 CFR §370.10 → Obligation OBL-2026-0147]
Explainability
Every answer has a provenance trail
Compliance Intelligence doesn’t just tell you what — it shows you why. Every generated answer includes an evidence chain linking back through the graph entities that produced it. Click any node to see the source record.
📄
SDS
Battery Acid Concentrate v2.1
⚗️
Chemical
Sulfuric Acid · CAS 7664-93-9
🧪
Inventory
12,400 lbs (Jan 2026 snap)
▲
Threshold
10,000 lbs · 40 CFR §370.10
✓
Obligation
File Tier II — TRIGGERED
Full Graph Lineage
Every answer traces back through the specific nodes and edges in the graph that produced it — from SDS version to chemical identity to threshold to obligation.
Audit-Ready Export
Export any evidence chain as a structured JSON or PDF document. Hand it to an auditor and they can verify every data point independently.
Temporal Versioning
Evidence chains include version timestamps. If an SDS is updated or a threshold changes, historical evidence chains remain intact for prior-period compliance.
Capabilities
Questions your EHS team asks every day
Compliance Intelligence handles five categories of queries — each powered by different graph traversal patterns optimized for the question type.
Why must this facility file Tier II for Ammonia?
Traces the full obligation path: chemical identity → SDS ingredient decomposition → inventory aggregation → threshold comparison → regulation match → filing obligation.
obligation explanation
Which chemicals exceed reporting thresholds?
Scans all chemicals across the facility graph, comparing aggregated ingredient-level quantities against federal and state-specific thresholds. Returns ranked list with exceedance margins.
threshold analysis
What changed since our last Tier II filing?
Compares current inventory snapshot against the snapshot used for the prior filing. Identifies new chemicals, removed chemicals, quantity changes, and new threshold exceedances.
temporal comparison
Which of our 12 facilities have EHS chemicals above TPQ?
Multi-facility fan-out query. Traverses each facility’s inventory, filters for EHS-flagged chemicals, compares against Threshold Planning Quantities, and returns a cross-site summary.
multi-site rollup
Show me all unverified chemicals in our inventory
Filters chemical nodes by verification status, returns list with CAS numbers, source SDSs, and date of last review. Highlights chemicals with missing or expired SDS versions.
inventory review
How would the proposed EPCRA hazard category expansion affect us?
Simulates the impact of a regulatory change by re-evaluating thresholds under the proposed rule. Identifies which facilities would gain new obligations and which chemicals would be newly reportable.
regulatory impact
What's our overall compliance score?
Aggregates obligation statuses across all facilities: filed, pending, overdue, not yet triggered. Returns a weighted score with breakdown by regulation type and critical-path items.
compliance summary
Which SDSs are older than 3 years and still in active inventory?
Joins SDS revision dates with active inventory records. Flags documents that may need manufacturer re-request or supplier outreach for updated versions.
document lifecycle
GraphRAG vs. Traditional RAG
Why graph-structured retrieval matters
Traditional RAG systems chunk documents into text fragments and retrieve by similarity. That works for general Q&A — but compliance questions require multi-hop relational reasoning across structured entities. GraphRAG retrieves connected subgraphs, not text snippets.
| Capability | Traditional RAG | SafeGenics GraphRAG |
|---|---|---|
| Retrieval unit | Text chunks (512–2048 tokens) | Connected subgraphs (nodes + edges) |
| Multi-hop reasoning | ✗Limited to single-chunk context | ✓Traverses up to 6 relationship hops |
| Structured data | Flattened into text — loses schema | Native entity properties, typed edges, computed values |
| Citation granularity | Links to source document | Links to specific graph node, edge, and property |
| Temporal queries | ✗No version awareness | ✓Immutable snapshots with temporal edges |
| Ingredient decomposition | ✗Cannot compute derived quantities | ✓Precomputed from SDS Section 3 extraction |
| Multi-facility queries | Requires separate retrieval per site | Single fan-out traversal across facility subgraphs |
| Hallucination risk | Model may extrapolate beyond context | Answer constrained to retrieved graph — no external knowledge |
| Regulatory change simulation | ✗Not supported | ✓Re-evaluates thresholds under proposed rules |
Conversation Context
Multi-turn conversations with entity memory
Compliance Intelligence maintains conversational context across questions. Ask “How many chemicals do we have?” followed by “How many are EHS?” and the system understands “we” refers to the current facility and scopes the follow-up accordingly.
Entity resolution maps natural language references to canonical graph nodes: “ammonia” resolves to CAS 7664-41-7, “our Texas plant” resolves to Facility F-004, “the new EPCRA rule” resolves to the November 2025 proposed hazard category expansion.
- Multi-turn session — Plant A
How many chemicals do we have?
You have 24 chemicals in your current inventory at Plant A.
How many are EHS?
5 of your 24 chemicals are classified as Extremely Hazardous Substances: Ammonia, Chlorine, Sulfuric Acid, Hydrochloric Acid, and Formaldehyde.
Ask anything about your compliance data…
Architecture
Built on the Compliance Intelligence Graph
Compliance Intelligence is powered by the same 9-entity, 11-relationship-type graph that drives SafeGenics’ obligation detection, Tier II reporting, and incident management. The AI layer sits on top — it doesn’t replace the graph, it makes it conversational.
Intent Analysis
AI extracts entities, query type, and traversal pattern from natural language. Maps questions to one of 12 predefined graph query templates optimized for compliance workflows.
Graph Traversal Engine
Executes multi-hop queries across the property graph: Facility → Inventory → Chemical → SDS → Threshold → Regulation → Obligation. Returns typed subgraphs with computed properties.
Context Serialization
Converts retrieved subgraphs into structured context payloads. Entity properties, relationship types, and precomputed values (ingredient decomposition, threshold exceedances) are included verbatim.
Constrained Generation
The LLM generates answers strictly from the retrieved context. No external knowledge, no hallucination. Every claim must reference a node or edge in the provided subgraph.
Citation Tagging
Post-generation, each claim is tagged with the graph entities it references. Citations link to specific SDS versions, inventory snapshots, threshold definitions, and regulation sections.
Performance
Intent analysis: 1–2s. Graph query: 0.1–0.5s. Answer generation: 1–2s. Total end-to-end: 2–5 seconds per query with caching for repeated question patterns.
Regulatory Drift Detection
Know how regulatory changes affect you — before they take effect
Compliance Intelligence doesn’t just answer questions about your current data. It also monitors regulatory changes and simulates their impact on your graph. When EPA publishes a proposed rule change, SafeGenics re-evaluates your thresholds under the new parameters and tells you exactly which facilities and chemicals would be affected.
In November 2025, EPA proposed expanding EPCRA hazard categories from approximately 50 to 114 under GHS Revision 7 (90 FR 51187). SafeGenics customers received impact analyses showing which chemicals would require reclassification and which facilities would gain new Tier II obligations — months before any compliance deadline.
⚠️ Regulatory Alert
EPCRA Hazard Category Expansion — Proposed Rule
EPA proposes expanding from ~50 to 114 GHS hazard categories for Tier II reporting. If finalized, this would affect your compliance posture.
Impact on your facilities:
• 3 of 12 facilities would gain new reporting obligations
• 7 chemicals would be newly reportable under expanded categories
• Estimated 14 additional Tier II line items across your portfolio
Status: Proposed — monitoring for final rule
FAQ
Common questions about Compliance Intelligence
Does the AI have access to data outside my graph?
No. Compliance Intelligence is strictly constrained to your Compliance Intelligence Graph. The LLM receives only the subgraph retrieved for your specific query — it has no access to the internet, other customers’ data, or its own pre-trained knowledge during answer generation. Every claim in the response must be traceable to a node or edge in the provided context.
How is this different from asking ChatGPT about compliance?
General-purpose chatbots answer from pre-trained knowledge and may hallucinate regulatory details. SafeGenics Compliance Intelligence answers from your actual data — your SDSs, your inventory, your facilities, your thresholds. It doesn’t guess that Sulfuric Acid might be reportable; it calculates that your specific facility has 12,400 lbs against a 10,000 lb threshold and shows you exactly which products contributed.
Can I use this for auditor inquiries?
Yes — this is one of the primary use cases. When an auditor asks why a facility filed for a specific chemical, you can ask the same question in SafeGenics and receive a cited evidence chain: the SDS version, the inventory snapshot, the threshold crossed, and the regulation matched. Export the evidence chain as PDF or JSON for audit documentation.
What happens when my data changes?
Compliance Intelligence always queries the live graph. When you upload a new SDS, update inventory quantities, or add a facility, the system immediately reflects those changes in subsequent queries. Historical evidence chains from prior queries remain intact through temporal versioning — you’ll always be able to explain why a past decision was made based on the data available at that time.
Is my data used to train the AI model?
No. Your compliance data is never used for model training. SafeGenics maintains multi-tenant isolation at the graph node level. The LLM is used only for intent analysis and answer generation — your graph data flows through the system at query time and is not retained by the AI provider. All data is encrypted with AES-256 at rest and TLS 1.3 in transit.
What compliance questions can it answer?
Compliance Intelligence handles obligation explanations, threshold analyses, inventory reviews, temporal comparisons, multi-site rollups, regulatory impact simulations, compliance scoring, and document lifecycle queries. The system currently supports 12 query templates covering EPCRA Tier II, OSHA HazCom, TRI, CERCLA, and state-specific compliance programs. New query patterns are added as the platform expands.
See compliance intelligence in action
Ask your data a question. See the evidence chain. Understand why.