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Customer Support Domain Cookbook

Complete Working Example

from director_ai.core import CoherenceScorer, GroundTruthStore

store = GroundTruthStore()  # empty — populate with your KB
store.add("refund policy", "Refunds are available within 30 days of purchase.")
store.add("shipping", "Standard shipping takes 5-7 business days.")
store.add("pricing", "Pro plan costs $49/month, Enterprise is $199/month.")
store.add("support hours", "Support is available Monday-Friday 9AM-5PM EST.")

scorer = CoherenceScorer(threshold=0.6, ground_truth_store=store)

# Correct → approved
approved, score = scorer.review("What is your refund policy?",
    "We offer refunds within 30 days of purchase.")
print(f"Correct: approved={approved}, score={score.score:.2f}")

# Wrong → rejected
approved, score = scorer.review("What is your refund policy?",
    "We offer full refunds within 90 days, no questions asked.")
print(f"Wrong:   approved={approved}, score={score.score:.2f}")
if score.evidence:
    for chunk in score.evidence.chunks:
        print(f"  Evidence: {chunk.text}")

Configuration

scorer = CoherenceScorer(
    threshold=0.55,
    soft_limit=0.65,
    use_nli=True,
    ground_truth_store=store,
    cache_size=2048,   # high cache for repeated product queries
    cache_ttl=1800,    # 30-min TTL for stable product facts
)

Knowledge Base

store = VectorGroundTruthStore()
store.ingest([
    "Refunds are available within 30 days of purchase with receipt.",
    "Standard shipping takes 5-7 business days within the US.",
    "Express shipping takes 1-2 business days for $15 extra.",
    "Pro plan costs $49/month billed annually or $59/month billed monthly.",
    "Enterprise plan costs $199/month with custom onboarding.",
    "Support is available Monday-Friday 9AM-5PM EST via chat and email.",
    "Phone support is available for Enterprise customers only.",
])

CSAT Improvement (Illustrative Estimates)

These are illustrative estimates based on industry benchmarks (Zendesk, Intercom), not measured Director-AI deployment data. Validate on your own ticketing system.

Metric Without guardrail (industry baseline) With Director-AI (threshold=0.55)
Wrong policy quoted to customer 8–12% of responses (industry average) < 1% estimated with product KB
CSAT score 3.2/5 (industry avg for AI chat) 4.5–4.7/5 estimated (verified answers + evidence)
Escalation rate 25% (customer doesn't trust AI) 8% estimated (evidence builds confidence)
Agent handle time (hybrid AI + human) 6 min 3.5 min estimated

Cost model (illustrative): At 10,000 tickets/month, reducing escalations from 25% to 8% saves 1,700 agent-handled tickets/month. At $12/ticket agent cost, that's ~$245K/year. These are planning estimates. The CSAT correlation (15–20% higher retention) is from Zendesk industry benchmarks, not Director-AI data.

Key Considerations

  • Lower threshold (0.55): Customer support tolerates minor phrasing differences
  • High cache size: Product queries repeat frequently
  • Always return evidence: Customers trust answers backed by specific KB entries
  • Audit trail: Enable AuditLogger for quality assurance review