| Chordia Compass | DIY (Claude, ChatGPT, Your Favorite LLM) | |
|---|---|---|
| Architecture | Purpose-built interaction intelligence platform | Ad-hoc prompts against a general-purpose LLM |
| 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 | Chordia | 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 | Chordia | 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 | Chordia | 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 | Chordia | 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 |
Request a demo and see what a purpose-built analysis engine finds that prompts miss.
