sales-level-ai

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

1. 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

2. What's your CCaaS/telephony?

   • A) Five9
   • B) Talkdesk
   • C) Zendesk
   • D) Genesys
   • E) Amazon Connect
   • F) Twilio
   • G) Freshworks
   • H) Kustomer
   • I) Other

3. 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 calibration on 30 calls with human reviewers. Check transcription quality — poor transcription = unreliable scoring regardless of criteria.

Calls not appearing or delayed ingestion

Symptom: Calls from today aren't visible in the platform, 24+ hour delay Cause: CCaaS integration pipeline processing lag — known issue in G2 reviews Solution: Check CCaaS integration status in admin panel. Verify call recordings are flowing from your telephony system. Contact Level AI support if delays exceed 24 hours consistently. For same-day monitoring needs, evaluate Balto for real-time visibility.

AgentGPT not surfacing relevant guidance

Symptom: Real-time assist shows generic or irrelevant knowledge articles during calls Cause: Knowledge base not properly configured or not mapped to the right call contexts Solution: Review your knowledge base content in Level AI — articles need clear topic tagging. Test with specific call scenarios. AgentGPT uses semantic matching, so ensure knowledge articles use language similar to how agents and customers discuss topics.