AI case-resolution copilot for a retail safety solutions company

Context

A retail safety solutions company runs a customer service operation handling product, regulatory, and compliance questions from retailers across multiple regions. The team was under pressure on two fronts: response SLA commitments tightening, and a backlog of tickets that required agents to "swivel-chair" between the CRM, the product knowledge base, the regulatory document library, and prior resolved cases. The time-to-first-response gap was the visible metric; the less-visible issue was how much agent time was spent finding the right information rather than writing the right response.

Challenge

The client evaluated three options: (1) rip-and-replace the existing CRM with a vendor that bundled AI, (2) bolt on a point-solution AI chatbot in front of customers, or (3) augment the existing CS team with an AI copilot that worked inside the tools they already used. The first was cost-prohibitive and would have disrupted operations for a year. The second risked customer experience in regulated scenarios. The third was the right shape but required a system that was CRM-agnostic, confidence-scored, and auditable for compliance.

The specific requirements that drove architecture:

• CRM-agnostic. The solution had to work identically whether the team of the day used Salesforce, Zendesk, or an email inbox. A single pipeline, multiple channel adapters.
• Grounded retrieval. Every response had to cite the product document or prior case it was drawn from. No hallucinated answers.
• Confidence scoring. Low-confidence or high-risk cases had to auto-escalate. The copilot had to know what it did not know.
• Auditable. Every AI recommendation, input context, and agent action logged and retrievable for compliance and QA review.
• Pluggable AI. The client wanted the flexibility to run local LLMs for sensitive content and cloud LLMs for general tasks — without rebuilding the pipeline.

Approach

Thoughtwave deployed the TWSS CS Agent — our production AI case-resolution copilot — connected to the client's CRM via webhook. The copilot runs a five-stage pipeline per new case: ingest, understand (intent classification and entity extraction), retrieve (product KB + regulatory sources + prior resolved cases via MCP providers and semantic vector search), generate (LLM response with citations), and score-and-sync (confidence score + guardrail checks + write-back to the case record).

The 6-week engagement was structured as a focused pilot:

• Weeks 1-2: Connect to the client's CRM and tune intent classes to the product and regulatory catalog.
• Weeks 3-4: Run the copilot side-by-side with a pilot CS team. Measure speed, response quality, and deflection rate.
• Weeks 5-6: Tune confidence thresholds, wire up auto-escalation for low-confidence and high-risk cases, and go live for the target queue with full audit capture.

What we built

The deployed system has five production components:

1. Channel adapters. CRM webhook receivers for Salesforce, Zendesk, and Gmail; extensible to HubSpot, Dynamics, ServiceNow, Freshdesk, and Outlook without code changes.
2. Intent & entity layer. Domain-tuned classifiers that identify the case type and extract product SKUs, regulatory references, and customer account attributes.
3. Grounded retrieval layer. MCP-based knowledge providers covering product documentation, regulatory sources, and the prior-resolved-cases vector store — every cited in the final response.
4. Confidence & guardrail layer. Per-response confidence scoring, PII redaction, and rule-based auto-escalation for defined high-risk intent classes.
5. Write-back & audit layer. The recommended response is posted to the case record inside the agent's existing workflow, and every input, retrieval, and output is captured in an immutable audit log.

The architecture is deliberately CRM-agnostic: a single pipeline with channel adapters at one end and multi-LLM routing at the other (OpenAI, Anthropic, and local Ollama models, swappable without rebuilding the pipeline).

Outcomes

Indicative outcomes from comparable AI copilot deployments in regulated industrial support environments:

• 60% faster average case response time. The time from case arrival to a ready-to-send draft dropped from minutes of research to seconds of review.
• 40% lower cost per case resolved. Agents spend their time on customer interaction and judgment calls, not information retrieval.
• 3x higher knowledge reuse across tickets. Prior resolutions surface automatically in the retrieval step.
• 100% of cases logged with full audit trail and citations — a material improvement in compliance and QA posture.

What's next

The same platform is being extended to adjacent queues and additional channel adapters without architectural change. The client's next phase adds Finance CS AI (the TWSS Finance AI/ML variant) for advisory-follow-up workflows, and evaluates the TWSS AI Email Assistant for shared-inbox triage on the ops and sales mailboxes.

The underlying point is portable: once the platform components (tool registry, guardrails, observability, evaluation) are in place for one workflow, the second and third agentic workflows ship in weeks rather than months.