sales-observe-ai

What this skill does

# Observe.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 Auto QA scorecards and evaluation criteria
   - B) Configuring Agent Copilot for real-time guidance
   - C) Coaching Copilot — post-call performance management
   - D) VoiceAI or ChatAI virtual agent setup
   - E) CCaaS integration (Five9, Amazon Connect, Talkdesk, etc.)
   - F) API integration — pulling interactions, transcripts, evaluations
   - G) Comparing Observe.AI to another tool (Balto, Cresta, CallMiner, Enthu.AI)
   - H) Compliance monitoring and audit trails
   - I) Other

2. **What's your current setup?**
   - A) Evaluating whether to buy
   - B) New — haven't started implementation
   - C) In implementation (3-6 month timeline)
   - D) Running but having issues
   - E) Expanding to new modules (adding Agent Copilot, VoiceAI, etc.)

3. **What's your CCaaS/telephony?**
   - A) Five9
   - B) Amazon Connect
   - C) Talkdesk
   - D) NICE CXone
   - E) Genesys
   - F) Avaya
   - G) Twilio
   - H) 8x8
   - I) Other

4. **Contact center size?**
   - A) Small (< 50 agents)
   - B) Mid-size (50-200 agents)
   - C) Large (200-1,000 agents)
   - D) 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}` |
| General CRM/tool integration patterns (Zapier, webhooks) | `/sales-integration {user's question}` |

Otherwise, answer directly using the platform reference below.

## Step 3 — Observe.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 Auto QA with a starter scorecard (5-8 criteria) — validate transcription accuracy on 50+ calls before trusting scores
3. Set coaching thresholds — which score ranges trigger supervisor alerts
4. Roll out Coaching Copilot for managers with coaching dashboards
5. Add Agent Copilot for real-time guidance once post-call QA is stable
6. VoiceAI/ChatAI agents last — these require the most tuning and governance setup

**When comparing to competitors:**
- vs **Balto**: Balto is stronger on real-time during-call guidance (sub-200ms), Observe.AI is stronger on post-call QA analytics and has broader AI agent capabilities. Balto deploys in 45-60 days vs Observe.AI's 3-6 months.
- vs **Cresta**: Similar enterprise scope. Cresta has Knowledge Agent (RAG from knowledge bases during calls) and stronger virtual agent capabilities. Observe.AI has stronger post-call QA and compliance audit trails.
- vs **Enthu.AI**: Enthu is faster to deploy (hours not months), cheaper (~$15-69/user/mo), and needs no minimums. Observe.AI is for enterprise scale (100+ agents) with deeper analytics and AI agent capabilities.

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.*

- **Transcription accuracy degrades with accents, background noise, and overtalk.** Validate accuracy on your actual call recordings before trusting Auto QA scores. Speaker attribution (agent vs customer) errors are a known pain point — test diarization quality early.
- **No public pricing.** All five tiers require "Talk to sales." Estimated $100-500/user/mo based on review sites. Get a direct quote — pricing varies by agent count, modules, and contract length.
- **Implementation takes 3-6 months** for full deployment. Unlike Enthu.AI (hours) or Balto (45-60 days), Observe.AI requires significant setup for CCaaS integration, QA calibration, and agent rollout.
- **Post-call analytics were the original focus.** Real-time Agent Copilot is newer — if real-time during-call coaching is your primary need, evaluate Balto or Cresta alongside Observe.AI.
- **API docs are JS-rendered and partially gated.** The Redoc page at api-docs.observe.ai exists but requires JavaScript rendering. Plan for limited self-serve API exploration — you may need to request the OpenAPI spec from your account team.
- **Call segmentation on long calls.** Users report that long calls get split into smaller segments, losing full context for QA scoring. Ask about segmentation behavior during evaluation.
- **EU AI Act (August 2026)** will require documenting how AI generates QA recommendations and giving agents the ability to challenge AI feedback. Discuss compliance readiness with Observe.AI before committing.

## 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-enthu` — Enthu.AI platform help (contact center QA, affordable alternative for smaller teams)
- `/sales-balto` — Balto platform help (real-time AI guidance for contact centers, fastest deployment)
- `/sales-cresta` — Cresta platform help (enterprise contact center AI, broadest scope)
- `/sales-salesken` — Salesken platform help (real-time coaching + QA, multilingual, APAC focus)
- `/sales-gong` — Gong platform help (enterprise revenue intelligence, sales-focused CI)
- `/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`

## Examples

### Example 1: Evaluating Observe.AI for a 500-agent contact center
**User says**: "We have 500 agents on Five9 and only review 2% of calls manually. Would Observe.AI help?"
**Skill does**:
1. Confirms Observe.AI's core value — Auto QA on 100% of calls vs manual sampling
2. Explains Five9 native integration and implementation timeline
3. Compares pricing and deployment vs alternatives (Balto, Cresta, Enthu.AI)
4. Recommends starting with Post-interaction AI tier, adding Agent Copilot later
**Result**: Clear evaluation framework with implementation roadmap

### Example 2: Transcription accuracy issues
**User says**: "Our Observe.AI transcripts are inaccurate — agents are being misscored because of bad transcription"
**Skill does**:
1. Identifies common causes: accents, background noise, overtalk, speaker diarization errors
2. Recommends reviewing Auto QA scorecard criteria — make criteria less transcript-dependent where possible
3. Suggests working with Observe.AI support on transcription model tuning
4. Notes workarounds: use sentiment/keyword tracking alongside transcript-based scoring
**Result**: Troubleshooting plan for transcription quality issues

### Example 3: Comparing contact center QA tools
**User says**: "Observe.AI vs Balto vs Cresta — which one for a 200-agent insurance call center?"
**Skill does**:
1. Maps each platform's strengths to insurance use case (compliance, real-time guidance, QA)
2. Recommends Observe.AI or Cresta for post-call QA depth, Balto for real-time compliance alerts
3. Compares pricing ranges and deployment timelines
4. Suggests evaluating all three in a pilot with 20-30 agents
**Result**: Side-by-side comparison tailored to regulated industry requirements

## Troubleshooting

### Auto QA scores seem i