8–12 week conversational AI pilot to improve CSAT without IVR changes?

Short answer: Run a parallel pilot that uses chat/SMS or a voice overlay (audio mirroring or a SIP edge) to avoid changing IVR or routing. Focus on one high-volume, low-risk intent, gate releases with QA and rollback controls, and measure CSAT plus contained-and-resolved (no repeat contact within 7 days).

Run an 8–12 week conversational AI pilot to improve CSAT without IVR changes

This guide gives a repeatable, low-risk pilot plan you can run in parallel with an existing contact center so you don’t need to touch IVR or routing. It includes realistic use cases, week-by-week milestones for both an 8-week and a 12-week timeline, the exact containment metric to track, integration patterns that avoid modifying IVR, operational changes for day-to-day QA, and a procurement/RFP checklist for an add-on AI agent.

Which pilot use case to choose (parallel, low risk)

Pick a single, well-scoped intent with high volume and low regulatory risk. Examples to consider: order/status checks, billing balance lookups, password resets or account unlocks, appointment confirmations, or simple FAQs. Start on chat or SMS if available — they avoid telephony routing completely — or pick a single voice intent that is scripted and low-risk for a voice-overlay approach.

Why this pattern works: it minimizes scope (one intent), limits exposure (low-risk content), and gives repeatable samples for CSAT and containment measurement.

8–12 week pilot plan (week-by-week milestones)

Two practical schedules: an 8-week compressed pilot (faster ramp, focused scope) or a 12-week phased pilot (more gradual ramp and wider QA). Both assume you have stakeholder sign-off, access to transcripts/knowledge base, and sandbox/test numbers.

8-week (compressed)

1. Week 0 — Prep (days 0–7): define baseline metrics (current CSAT method, containment baseline, volumes), pick the single intent, gather 500–2,000 representative transcripts/recordings, assign owners for product, security and contact center ops, and complete legal/compliance checklist.
2. Week 1 — Sandbox connectors & data: provision test numbers or chat channels, give the vendor access to transcripts and knowledge sources, and confirm data handling controls and DPA/BAA if needed.
3. Week 2 — Build initial agent: create intents, rules and a knowledge set from transcripts; seed canned responses and escalation rules.
4. Week 3 — Internal testing & deterministic simulation: run scripted journeys, deterministic tests and edge-case scenarios; refine prompts and policies.
5. Week 4 — QA pilot with humans-in-loop: run the agent in a mirror mode or as suggestions to agents; collect CSAT surveys and tag failures.
6. Week 5 — Small live beta: allow the agent to handle a controlled slice of real traffic (chat/SMS or mirrored voice overlay) for the target intent; enforce human escalation and guardrails.
7. Week 6 — Ramp and continuous learning: expand volume, apply fixes from the failure backlog, and begin automated changes behind a human approval gate.
8. Week 7 — Measure and validate: analyze CSAT deltas and containment (see measurement below) and review golden-suite test gating.
9. Week 8 — Decision: present results, agree on next steps (production roll, extended pilot, or rollback).

12-week (phased)

1–4. Weeks 0–4: same prep + longer internal testing and stakeholder training. 5–8. Weeks 5–8: longer mirror and human-in-loop phases with staged increases in autonomy and additional intents if stable. 9–12. Weeks 9–12: wider production pilot, integration with CRM for context, capacity testing and final acceptance criteria.

Success metrics: CSAT and containment (what to track and how)

Primary KPIs to prove value in 8–12 weeks

• CSAT change: collect the same post-interaction CSAT survey you already run (same wording and timing) and compare the agent-handled interactions against baseline for the same intent.
• Contained-and-resolved (recommended primary containment metric): measure whether the customer had no repeat contact for the same issue within 7 days. This is more robust than raw “single-contact containment” because it accounts for reopens and retries.
• Escalation rate: percent of sessions that hand off to a human.
• Automation rate and quality: percent of incoming intent volume handled autonomously, and the percent of those that passed QA/golden tests.

How to measure contained-and-resolved

1. Define the matching rules that tie interactions to the same issue (case ID, CRM ticket, or an NLP-based intent+entity match).
2. Flag any repeat interaction from the same customer matching those rules within 7 days as unresolved.
3. Contained-and-resolved = (autonomously handled sessions with no repeat within 7 days) / (total sessions for that intent).

Note: capture a baseline for each metric for at least 2–4 weeks before the pilot so you can show statistically meaningful deltas.

How to connect an AI agent without changing IVR or routing

Common overlay integration patterns (no IVR rewrite required)

• Chat/SMS first: connect AI to your web chat or SMS provider and route conversations there; no IVR change needed.
• Voice overlay (audio mirroring / SIP edge): place the AI in parallel to the live call using media forking, a SIP/RTP edge, or platform-specific audio hooks so the AI can hear and respond without changing existing IVR flows or contact-routing rules.
• Agent-assist (whisper/screen-pop): have the AI run in the background and surface suggested replies to the agent rather than replace routing.

Practical steps to ask from any vendor

1. Request a sandbox connector for your platform (or a SIP test trunk) so calls can be mirrored to the AI without touching production IVR.
2. Require a mirror or whisper mode so the AI can be evaluated in live conditions while humans remain in control.
3. Ensure transcripts and metadata (ANI, case ID) flow to the AI for correct context mapping, but do not change routing rules.
4. Start on chat/SMS where possible, then apply the same agent model to voice overlay once intent performance is proven.

Technical and contractual risks to mitigate (and contract terms to insist on)

Technical risks

• Sensitive-data leakage to public LLMs: insist on private-endpoint model routing or explicit model-hosting options.
• Silent failures or hallucinations that appear as resolved sessions: require explicit escalation rules and automatic handoff on guardrail violations.
• Insufficient testing under real audio conditions: require voice replay and noise testing in the pilot.

Contractual protections to request

• Data Processing Agreement (DPA) and a HIPAA BAA when PHI is expected.
• Written description of how models are routed and an option for private-endpoint routing for sensitive workloads.
• Right to export audit logs and transcripts and retention controls (off by default unless you opt in).
• Acceptance tests and golden-suite gating before any traffic ramp; versioned releases with human approval.
• Rollback and release commitments: require a documented rollback procedure and the ability to stop/roll back a release within a defined window.
• SLAs to request in the RFP: connector availability, incident response times, and support SLAs for critical failures (define the thresholds and remedies in the contract).

RFP checklist and pricing questions (for an add-on AI agent)

Include these items in the RFP

• Channels and connectors required (chat, SMS, voice) and whether a SIP/RTP edge or audio mirroring is available.
• Staging/sandbox and test-number availability for the pilot.
• Model routing and privacy options (private endpoints, model choices, and where LLM inference runs).
• Data handling: redaction, retention defaults, audit log export, and a HIPAA BAA if needed.
• Testing and simulation capabilities and how releases are gated.
• Rollback controls and human approval workflows.
• Acceptance criteria and what constitutes pilot success (CSAT and contained-and-resolved uplift definitions).
• Pricing model: ask for pilot price, per-interaction or per-concurrent-session pricing, overage fees, and support costs.

Pricing questions to include

• What’s included in the pilot fee versus what’s billable (connectors, integrations, custom intents, analytics)?
• How is production priced after the pilot (per-minute, per-concurrent-session, per-interaction)?
• What are estimated model inference and data egress costs and how are they billed?

Procurement evaluation: what to verify beyond a demo

• Integration points: can the vendor attach via your CTI, chat API, or a SIP trunk without IVR changes?
• Test capabilities: deterministic simulation, voice-in-noise replay, golden-suite gating and rollbacks.
• Metrics and reporting: real-time dashboards for CSAT, containment, escalation and a failure backlog export.
• Hidden costs: CRM integrations, custom connector development, model usage fees, and storage/e-discovery fees.

Day-to-day operational impact

• QA: expect an initial burst of manual QA and weekly golden-suite reviews; require a human approval gate for auto-promoted changes.
• Monitoring: daily dashboards should include CSAT, contained-and-resolved, fallback/escalation reasons, and top failing utterances.
• Agent workflow: AI should surface context and suggested replies; agents must be trained on when to accept or override AI suggestions.
• WFM: update forecasts for handle time and wrap if the AI changes average handling time for the intent.

Comparison table: pilot approaches

Where Nexus AI fits

Nexus AI runs on top of existing contact-center platforms and provides real-time audio connectors for Genesys (AudioHook), Twilio, Amazon Connect, and a SIP/RTP telephony edge.

Nexus AI supports live voice, web chat and SMS with a single agent definition that shares intents, policies and tests across channels; memory carries across channels so a customer can start on chat and finish on a call.

For voice, Nexus AI can use speech-to-speech engines (Amazon Nova Sonic, OpenAI Realtime, Gemini Live) or a chained pipeline (speech-to-text, an LLM and text-to-speech), and the engine can be swapped while flows, policies and tests remain with the agent.

Language models from OpenAI, Google Gemini and Anthropic Claude (via AWS Bedrock) are routed per workload through an LLM gateway, and Nexus AI offers private-endpoint routing so sensitive data does not go to public model APIs.

The Agent Builder converts plain-language intents or transcripts into testable agents; every agent is shipped with guardrails (policies, PII/PHI redaction and escalation rules) and an auto-generated test suite.

Teams can create agents from real transcripts and record-based analysis so failure modes are promoted into a ranked backlog of fixes.

A Flow Builder canvas wires agents into journeys with handoffs and channel logic; flows with dead ends or loops are prevented from deploying.

Nexus AI includes voice testing that replays journeys turn-by-turn under real audio conditions (background noise, barge-in, poor cellular lines) and a deterministic simulation mode so golden suites gate every release.

Continuous learning observes, proposes improvements, gates releases for human approval, and every change can be rolled back in one click.

Testing, simulation and continuous learning can also be applied to agents built on other platforms using those agents' transcripts.

Call Intelligence analyses recorded calls from other bot platforms and contact centers without integrating with them, turning failures into a ranked backlog of fixes.

Agents answer from the customer’s own knowledge sources (PDFs, Confluence, SharePoint, S3, APIs and databases) that sync automatically and flag knowledge gaps.

A guardrail library enforces soft and hard blocks; repeated soft violations escalate to a hard block that hands the conversation to a human.

PII and PHI are redacted in the transcript pipeline, tenants are isolated, retention is off by default, audit logs can be exported, and AI-disclosure prompts are included.

For payment conversations, Nexus AI offers a PCI mode that keeps plaintext caller speech and tool data out of storage and logs; this is a design capability and is not presented as a formal PCI attestation.

Nexus AI offers HIPAA-aligned controls and provides a HIPAA business associate agreement (BAA) at contract signature.

When the AI escalates, the human agent receives the conversation context so the customer does not have to repeat themselves.

An MCP registry connects agents to CRM, ERP, databases and APIs in real time, including MCP servers that a customer already runs.

Nexus AI’s analytics measure contained-and-resolved conversations defined as no repeat contact for the same issue within 7 days.

A healthcare intent pack ships today; additional industry packs have been built with design partners and custom intents can be added.

When Nexus AI is not the right fit

Nexus AI may not be the right fit if you require a vendor-held payment certification (ask for your precise certification needs), if you cannot provide access to transcripts or test endpoints for the pilot, or if you need a full platform migration rather than an overlay.

Quick next steps (numbered checklist)

1. Select one high-volume, low-risk intent and record a 2–4 week baseline for CSAT and containment.
2. Prepare transcripts/knowledge sources and provision sandbox channel(s).
3. Issue the RFP checklist above to vendors and require sandbox connectors and acceptance tests.
4. Run the 8-week compressed or 12-week phased pilot and gate each release with QA and rollback controls.
5. Decide to produce-roll, expand to more intents, or stop based on measured CSAT and contained-and-resolved improvements.

Frequently asked questions

Can I run a pilot without changing IVR or routing?

Yes — run the agent in chat/SMS channels or attach it in parallel to voice using audio mirroring or a SIP edge so existing IVR and routing remain unchanged.

Which containment metric should I track during the pilot?

Track contained-and-resolved (no repeat contact for the same issue within 7 days) as your primary containment metric and compare it to your pre-pilot baseline.

What contractual protections should I require for LLM routing and data privacy?

Insist on a DPA and a BAA if PHI is involved, private-endpoint model routing for sensitive workloads, audit-log export, retention controls, and documented rollback/release gating.

Does Nexus AI provide connectors for major contact-center platforms and a SIP edge?

Nexus AI runs on top of existing contact-center platforms and provides real-time audio connectors for Genesys (AudioHook), Twilio, Amazon Connect, and a SIP/RTP telephony edge.

Will Nexus AI keep PHI and PII out of logs by default?

PII and PHI are redacted in the transcript pipeline, tenants are isolated, retention is off by default, and audit logs can be exported.

Learn more

• Nexus AI: AI orchestration platform
• Voice AI Connector