| Compass | DIY (Claude, ChatGPT, Your Favorite LLM) | |
|---|---|---|
| Platform Type | Compass: Conversation Quality, Compliance, Coaching, and Findings Intelligence. Omni-channel across sales, CS, support, and contact center. | Ad-hoc prompts against a general-purpose LLM |
| Core Analysis Approach | Findings Intelligence. Compass aggregates insights across every customer touchpoint into structured, actionable findings, each anchored to the exact transcript moment and delivered to the team that needs to act on it, with recommendations on what to do next. | Prompt-and-response. Whatever the LLM returns on the transcript you paste, one call at a time. |
| Primary Users | Revenue, CS, Support, QA, and Compliance leaders | Whoever has the ChatGPT or Claude login |
| Coverage | 100% of interactions, automatic | Manual - someone has to paste each transcript (or build and maintain a pipeline) |
| Context Awareness | Learns your business, your rubrics, your team's patterns over time | No memory between sessions. Every transcript starts from zero context. |
| Consistency | Same evaluation framework applied identically across every interaction | Prompt drift, model updates, and session variability mean different results on different days |
| Agent Evaluation | Patent-pending Lift adjusts for call difficulty and context | No concept of difficulty adjustment. Treats every call the same. |
| Rubric Customization | Purpose-built rubrics for sales, support, informational, recovery - tested before deployment | You write the prompt. Hope it holds. No way to test at scale before rolling out. |
| Scalability | Analyzes thousands of interactions automatically | Breaks down past sampling. Manual copy-paste doesn't scale. Even API pipelines need constant prompt maintenance. |
| Evidence Trail | Every finding links to specific transcript moments with timestamps | LLM output isn't anchored. You get opinions, not evidence chains. |
| Compliance & Audit | Structured, repeatable, auditable evaluation pipeline | No audit trail. Can't prove to a regulator how evaluations were generated or that they're consistent. |
| Data Security | SOC 2 Type II, PII redaction, dedicated infrastructure | Transcripts with customer PII going through consumer AI tools. Compliance risk. |
| Team Access | Dashboards, role-based access, supervisor workflows | Whoever has the ChatGPT login. No shared workspace, no permissions, no history. |
| Capability | Compass | DIY LLM |
|---|---|---|
| 100% interaction analysis | ✓ Automatic | ✗ Manual or requires custom pipeline |
| Evidence-backed findings with timestamps | ✓ | ✗ LLM output isn't anchored to transcript moments |
| Behavioral detection (364+ signals) | ✓ | ✗ Only finds what you prompt for |
| Auto-classifies interaction type | ✓ | ✗ You'd need to build this into every prompt |
| System confidence scoring | ✓ | ✗ LLMs don't reliably self-assess confidence |
| Predicted CSAT | ✓ | ✗ No training data or calibration |
| Natural language questions | ✓ Built-in across your full dataset | Partial - one transcript at a time, no aggregate queries |
| Cross-interaction pattern detection | ✓ | ✗ No memory across transcripts |
| Sentiment detection | ✓ | ✓ Reasonable |
| Talk pattern analysis | ✓ | ✗ No access to audio signals |
| Capability | Compass | DIY LLM |
|---|---|---|
| Automated QA pipeline | ✓ Evidence-based, runs continuously | ✗ Manual process, runs when someone remembers |
| Works without building scorecards | ✓ Analyzes from day one | ✓ Just paste and ask (but inconsistent) |
| Custom rubrics by interaction type | ✓ Different rubrics for sales, support, informational, recovery | ✗ One prompt fits all, or maintain multiple prompt templates manually |
| Rubric testing before deployment | ✓ Score sample interactions with draft rubric | ✗ No way to test at scale |
| Evaluation quality auditing | ✓ System checks its own work | ✗ No self-audit capability |
| Agent Lift (adjusts for call difficulty) | ✓ Patent-pending | ✗ No concept of call difficulty adjustment |
| Consistent scoring across evaluators | ✓ Same framework every time | ✗ Prompt drift, model updates change results |
| QA calibration | ✓ | ✗ |
| Capability | Compass | DIY LLM |
|---|---|---|
| Coaching recommendations from evidence | ✓ | ✗ Generic suggestions, not tied to behavioral data |
| Agent Lift (which behaviors drive outcomes) | ✓ | ✗ No outcome correlation |
| Per-agent performance tracking over time | ✓ | ✗ No persistent agent profiles |
| Period-over-period comparison | ✓ | ✗ No historical data |
| Supervisor workflow (assignments, feedback threads) | ✓ | ✗ |
| Capability | Compass | DIY LLM |
|---|---|---|
| Automatic ingestion from any telephony | ✓ | ✗ Manual export + paste or custom API build |
| Multi-channel (voice, chat, email, SMS) | ✓ | Partial - text channels easier, voice requires separate transcription |
| Meeting capture (Zoom, Teams, Google Meet) | ✓ | ✗ |
| PII redaction before analysis | ✓ | ✗ Customer data goes through third-party consumer AI |
| Role-based access control | ✓ | ✗ |
| Audit trail for compliance | ✓ | ✗ |
| Built-in CRM | ✓ | ✗ |
| SSO | ✓ | ✗ |
| API access | ✓ | ✓ (via LLM provider APIs) |
| SOC 2 Type II | ✓ | Depends on LLM provider + your pipeline security |
If you're evaluating 10-20 calls a week and want to prove that AI can surface things your team is missing, DIY with an LLM is a reasonable experiment. It validates the concept. The question is what happens next: when you need consistency across thousands of interactions, when compliance requires an audit trail, when you need to know which agent behaviors actually drive outcomes - not just what the LLM thought about one transcript on one day. That's where purpose-built tooling earns its place.
DIY LLM QA proves the value of AI-powered conversation analysis. Most teams that try it become believers fast. The gap is not in the insight, it is in the infrastructure: consistency at scale, evidence trails, security, and a system that turns individual transcript reviews into repeatable team-wide value. Compass delivers Findings Intelligence: structured, evidence-anchored findings surfaced from every customer conversation, with recommendations on what to do next, across calls, email, support tickets, video, and chat, spanning your CCaaS, CRM, helpdesk, email, video conferencing, and VoIP tools. QA and compliance leaders get the automated, evidence-based scoring they'd expect. Revenue and CS leaders get forecasting, deal momentum, and renewal risk from the same platform. One thread. Every customer. From first touch to renewal.
