AI‑Driven Intent‑Based Routing Engine for Real‑Time Vendor Questionnaire Collaboration

Vendor security questionnaires have become a bottleneck for fast‑growing SaaS companies. Every new customer request triggers a cascade of manual hand‑offs: a security analyst pulls the latest policy, a legal reviewer validates wording, a product engineer clarifies technical implementations, and the final answer is assembled in a PDF. This fragmented workflow leads to long turnaround times, inconsistent answers, and audit‑risk exposure.

What if the platform itself could understand why a question is asked, who is best suited to answer it, and when an answer is needed, then automatically route the request to the right person—in real time? Enter the AI‑Driven Intent‑Based Routing Engine (IBRE), a core component of the Procurize AI platform that marries knowledge‑graph semantics, retrieval‑augmented generation (RAG), and continuous feedback to orchestrate collaborative questionnaire responses at machine speed.

Key takeaways

• Intent detection transforms raw questionnaire text into structured business intents.
• A dynamic knowledge graph links intents to owners, evidence artifacts, and policy versions.
• Real‑time routing leverages LLM‑powered confidence scoring and workload balancing.
• Continuous learning loops refine intents and routing policies from post‑submission audits.

1. From Text to Intent – The Semantic Parsing Layer

The first step of IBRE is to convert a free‑form question (e.g., “Do you encrypt data at rest?”) into a canonical intent that the system can act upon. This is achieved with a two‑stage pipeline:

1. LLM‑based Entity Extraction – A lightweight LLM (e.g., Llama‑3‑8B) extracts key entities: encryption, data at rest, scope, compliance framework.
2. Intent Classification – The extracted entities feed a fine‑tuned classifier (BERT‑based) that maps them to a taxonomy of ~250 intents (e.g., EncryptDataAtRest, MultiFactorAuth, IncidentResponsePlan).

The resulting intent object includes:

• intent_id
• confidence_score
• linked_policy_refs (SOC 2, ISO 27001, internal policy IDs)
• required_evidence_types (configuration file, audit log, third‑party attestation)

Why intent matters:
Intents act as a stable contract between the questionnaire content and the downstream workflow. Even if the phrasing changes (“Is your data encrypted while stored?” vs. “Do you use encryption for data at rest?”) the same intent is recognized, ensuring consistent routing.

2. Knowledge Graph as the Contextual Backbone

A property‑graph database (Neo4j or Amazon Neptune) stores the relationships among:

• Intents ↔ Owners (security engineers, legal counsel, product leads)
• Intents ↔ Evidence Artifacts (policy documents, configuration snapshots)
• Intents ↔ Regulatory Frameworks (SOC 2, ISO 27001, GDPR)
• Owners ↔ Workload & Availability (current task queue, time‑zone)

Each node’s label is a string wrapped in double quotes, conforming to Mermaid syntax for later visualizations.

  graph LR
    "Intent: EncryptDataAtRest" -->|"owned by"| "Owner: Security Engineer"
    "Intent: EncryptDataAtRest" -->|"requires"| "Evidence: Encryption Policy"
    "Intent: EncryptDataAtRest" -->|"complies with"| "Regulation: ISO 27001"
    "Owner: Security Engineer" -->|"available"| "Status: Online"
    "Owner: Security Engineer" -->|"workload"| "Tasks: 3"

The graph is dynamic—every time a new questionnaire is uploaded, the intent node is either matched to an existing node or created on the fly. Ownership edges are recomputed using a bipartite matching algorithm that balances expertise, current load, and SLA deadlines.

3. Real‑Time Routing Mechanics

When a questionnaire item arrives:

1. Intent detection yields an intent with a confidence score.
2. Graph lookup retrieves all candidate owners and the associated evidence.
3. Scoring engine evaluates:
   • Expertise fit (expertise_score) – based on historical answer quality.
   • Availability (availability_score) – real‑time status from Slack/Teams presence APIs.
   • SLA urgency (urgency_score) – derived from questionnaire deadline.
4. Composite routing score = weighted sum (configurable via policy-as-code).

The owner with the highest composite score receives an auto‑generated task in Procurize, pre‑filled with:

• The original question,
• The detected intent,
• Links to the most relevant evidence,
• Suggested answer snippets from RAG.

If the confidence score falls below a threshold (e.g., 0.65), the task is routed to a human‑in‑the‑loop review queue where a compliance lead validates the intent before assignment.

Example Routing Decision

Alice receives the task instantly, and the system logs the routing decision for auditability.

4. Continuous Learning Loops

IBRE does not remain static. After a questionnaire is completed, the platform ingests post‑submission feedback:

• Answer Accuracy Review – Auditors score the relevance of the answer.
• Evidence Gap Detection – If evidence referenced is outdated, the system flags the policy node.
• Owner Performance Metrics – Success rates, average response time, and re‑assignment frequency.

These signals feed back into two learning pipelines:

1. Intent Refinement – Mis‑classifications trigger a semi‑supervised retraining of the intent classifier.
2. Routing Policy Optimization – Reinforcement Learning (RL) updates weightings for expertise, availability, and urgency to maximize SLA compliance and answer quality.

The result is a self‑optimizing engine that improves with each questionnaire cycle.

5. Integration Landscape

IBRE is designed as a micro‑service that plugs into existing tooling:

All communication occurs over mutual TLS and OAuth 2.0, ensuring that sensitive questionnaire data never leaves the secure perimeter.

6. Auditable Trail & Compliance Benefits

Every routing decision produces an immutable log entry:

{
  "question_id": "Q-2025-437",
  "intent_id": "EncryptDataAtRest",
  "assigned_owner": "alice@example.com",
  "routing_score": 0.85,
  "timestamp": "2025-12-11T14:23:07Z",
  "evidence_links": [
    "policy://encryption/2025-09",
    "artifact://config/production/db"
  ],
  "confidence": 0.93
}

Storing this JSON in an append‑only ledger (e.g., Amazon QLDB or a blockchain‑backed ledger) satisfies SOX and GDPR requirements for traceability. Auditors can reconstruct the exact reasoning behind every answer, dramatically reducing the evidence‑request cycle during SOC 2 audits.

7. Real‑World Impact – A Quick Case Study

Company: FinTech SaaS “SecurePay” (Series C, 200 employees)
Problem: Average questionnaire turnaround – 14 days, 30 % missed SLA.
Implementation: Deployed IBRE with a 200‑node knowledge graph, integrated with Slack and Jira.
Results (90‑day pilot):

The ROI was calculated at 6.2× in the first six months, primarily from reduced deal velocity loss and audit remediation costs.

8. Future Directions

1. Cross‑Tenant Intent Federation – Allow multiple customers to share intent definitions while preserving data isolation, leveraging federated learning.
2. Zero‑Trust Verification – Combine homomorphic encryption with intent routing to keep sensitive question content confidential even to the routing engine.
3. Predictive SLA Modeling – Use time‑series forecasting to anticipate questionnaire influx spikes (e.g., after a product launch) and pre‑scale routing capacity.

9. Getting Started with IBRE

1. Enable the Intent Engine in Procurize → Settings → AI Modules.
2. Define your intent taxonomy (or import the default one).
3. Map owners by linking user accounts to intent tags.
4. Connect evidence sources (document storage, CI/CD artifacts).
5. Run a pilot questionnaire and observe the routing dashboard.

A step‑by‑step tutorial is available in the Procurize Help Center under AI‑Driven Routing.

See Also

• Zero‑Trust Architecture for AI‑Driven Automation – NIST SP 800‑207