sales-level-ai

What this skill does

# Level AI Platform Help

## Step 1 — Gather context

If `references/learnings.md` exists, read it first for accumulated platform knowledge.

1. **What do you need help with?**
   - A) Setting up InstaScore QA scorecards and evaluation criteria
   - B) Configuring Real-Time Agent Assist (AgentGPT) for live guidance
   - C) Voice of Customer analytics — sentiment, iCSAT, trends
   - D) AI Virtual Agent setup (voice/chat automation)
   - E) CCaaS integration (Five9, Talkdesk, Zendesk, Genesys, etc.)
   - F) API integration — pushing/pulling interaction data
   - G) Comparing Level AI to another tool (Observe.AI, Cresta, CallMiner, Balto)
   - H) Compliance monitoring (PCI, HIPAA, script adherence)
   - I) Screen recording and agent coaching workflows
   - J) Other

2. **What's your current setup?**
   - A) Evaluating whether to buy
   - B) New — haven't started implementation
   - C) In implementation
   - D) Running but having issues
   - E) Expanding to new modules

3. **What's your CCaaS/telephony?**
   - A) Five9
   - B) Talkdesk
   - C) Zendesk
   - D) Genesys
   - E) Amazon Connect
   - F) Twilio
   - G) Freshworks
   - H) Kustomer
   - I) Other

4. **Contact center size?**
   - A) Mid-size (50-200 agents)
   - B) Large (200-1,000 agents)
   - C) Enterprise (1,000+ agents)

Skip-ahead rule: if the user's prompt already contains enough context, skip to Step 2.

## Step 2 — Route or answer directly

| Problem domain | Route to |
|---|---|
| Building a coaching program or training cadence | `/sales-coaching {user's question}` |
| Reviewing a specific call transcript for coaching | `/sales-call-review {user's question}` |
| Choosing between note-taker/conversation intelligence platforms | `/sales-note-taker {user's question}` |
| CCaaS platform comparison/selection | `/sales-ccaas-selection {user's question}` |
| General CRM/tool integration patterns (Zapier, webhooks) | `/sales-integration {user's question}` |

Otherwise, answer directly using the platform reference below.

## Step 3 — Level AI platform reference

**Read `references/platform-guide.md`** for the full platform reference — modules, pricing, integrations, data model, workflows.

Answer the user's question using only the relevant section. Don't dump the full reference.

## Step 4 — Actionable guidance

You no longer need the platform guide — focus on the user's specific situation.

**Implementation priority order:**
1. Connect your CCaaS first — call data must flow before anything else works
2. Configure InstaScore with a starter scorecard (5-8 criteria) — validate transcription accuracy on 50+ calls before trusting scores
3. Calibrate QA — have human reviewers score the same 20-30 calls and compare to InstaScore. Adjust criteria until alignment is acceptable
4. Roll out coaching workflows — use screen recordings and QA findings to identify coaching priorities
5. Enable AgentGPT for real-time guidance once post-call QA is stable
6. AI Virtual Agent last — requires the most tuning and governance

**When comparing to competitors:**
- vs **Observe.AI**: Similar scope. Level AI differentiates on semantic intelligence (intent-based, not keyword). Observe.AI has more mature VoiceAI/ChatAI agents and 250+ integrations. Both ~$150-200/agent/mo.
- vs **Cresta**: Cresta targets larger enterprises with Knowledge Agent and deeper virtual agent capabilities. Level AI is more accessible for mid-market.
- vs **CallMiner**: CallMiner has deeper post-call analytics and compliance for regulated industries. Level AI has stronger real-time agent assist. CallMiner averages ~$102K/yr.
- vs **Balto**: Balto deploys in weeks and leads in real-time guidance. Level AI combines real-time with post-call QA in one platform. Balto is ~$100-150/agent/mo.

If you discover a gotcha, workaround, or tip not covered in `references/learnings.md`, append it there.

## Gotchas

> *Best-effort from research — review these, especially items about plan-gated features and integration gotchas that may be outdated.*

- **Call ingestion can be delayed 24+ hours.** G2 reviewers report calls not appearing until the next day, making same-day QA monitoring unreliable. Ask about ingestion SLAs during evaluation.
- **InstaScore accuracy requires calibration.** AI QA scores won't match human QA out of the box — scoring issues tied to language barriers and subjective criteria. Start with binary/objective criteria and run a calibration period against manual scores.
- **Sentiment analysis misclassifies neutral conversations.** Users report neutral conversations flagged as negative. Don't rely on sentiment alone for coaching decisions — cross-reference with QA scores.
- **No public pricing.** Estimated ~$185/agent/month. Custom quotes required — pricing varies by agent count, modules, and contract length.
- **API exists but is not publicly documented.** GraphQL-based per their engineering blog. Enterprise customers get access — ask your account team for the API spec during evaluation.
- **Mid-market focus may limit scalability for very large operations.** The platform excels at 100-1,000 agent contact centers. For 5,000+ agents, also evaluate Observe.AI, Cresta, or NICE CXone.
- **Self-improving**: If you discover something not covered here, append it to `references/learnings.md` with today's date.

## Related skills

- `/sales-call-review` — Review specific sales calls and extract coaching insights
- `/sales-coaching` — Build coaching programs, onboarding, role-plays, certifications
- `/sales-note-taker` — Compare AI note-takers and conversation intelligence tools or wire APIs into CRM
- `/sales-observe-ai` — Observe.AI platform help (enterprise contact center QA, deepest AI agent capabilities)
- `/sales-cresta` — Cresta platform help (enterprise contact center AI, Fortune 500 focus)
- `/sales-callminer` — CallMiner platform help (enterprise post-call analytics, strongest compliance)
- `/sales-balto` — Balto platform help (real-time agent guidance, fastest deployment)
- `/sales-enthu` — Enthu.AI platform help (affordable contact center QA for smaller teams)
- `/sales-ccaas-selection` — Compare CCaaS platforms (Genesys, NICE, Talkdesk, Five9, etc.)
- `/sales-do` — Not sure which skill to use? The router matches any sales objective to the right skill. Install: `npx skills add sales-skills/sales --skill sales-do -a claude-code -y`

## Examples

### Example 1: Setting up automated QA
**User says**: "We manually review 3% of calls. How do I set up InstaScore in Level AI to auto-score everything?"
**Skill does**:
1. Reads platform guide for InstaScore module
2. Explains 100% auto-scoring with custom scorecards — define criteria, assign weights, auto-evaluate all interactions
3. Walks through scorecard creation with binary criteria (compliance, verification) and scaled criteria (empathy, resolution)
4. Recommends a 2-week calibration period comparing InstaScore to manual reviews
**Result**: User has a plan to move from 3% manual sampling to 100% automated QA

### Example 2: Comparing Level AI vs Observe.AI
**User says**: "We're evaluating Level AI and Observe.AI for our 300-agent insurance contact center"
**Skill does**:
1. Compares both platforms on QA, real-time assist, compliance, integrations, and pricing
2. Notes Level AI's semantic intelligence vs Observe.AI's post-call analytics depth
3. Highlights Observe.AI's more mature VoiceAI agents vs Level AI's stronger mid-market positioning
4. Recommends piloting both with 20-30 agents over 30 days
**Result**: Side-by-side comparison tailored to insurance contact center requirements

## Troubleshooting

### InstaScore QA scores don't match manual evaluations
**Symptom**: Auto-scores diverge significantly from human QA reviewer scores
**Cause**: Scorecard criteria may be too subjective, transcription errors affecting scoring, or language barriers causing misclassification
**Solution**: Make criteria binary where possible ("Did agent verify identity?" not "Was the opening professional?"). Run calibr