Document Version: 2.3.0 | Last Updated: 2026-07-01
ASR Feedback is an enterprise AI Response Quality Intelligence service operated by Acadify Solutions. We provide structured, multi-dimensional feedback on AI-generated responses using our proprietary 4-Pillar Methodology (Good, Bad, Learned, Remember). We help AI teams identify issues, reinforce strengths, capture novel insights, and build persistent institutional memory.
ASR Feedback is designed for organizations that build, deploy, or operate:
- Large Language Models (LLMs) — GPT, Claude, Gemini, Llama, etc.
- AI Agents — LangChain, AutoGen, CrewAI, custom agent frameworks
- Code Generation Tools — GitHub Copilot, Cursor, Codex, custom tools
- Conversational AI — Customer support bots, virtual assistants
- Domain-Specific AI — Medical, legal, financial, educational AI systems
| Aspect | Traditional Data Labeling | ASR Feedback |
|---|---|---|
| Depth | Binary (correct/incorrect) or simple categories | 4-dimensional analysis with severity, root cause, and remediation |
| Insight Capture | Not captured | Dedicated Learning Capture pillar |
| Institutional Memory | None | Memory Persistence pillar with durable rules |
| Quality Control | Basic consensus voting | Calibrated evaluators with κ ≥ 0.80 |
| Actionability | Labels for training data | Actionable intelligence for improvement |
We evaluate responses from any AI system, including GPT-4o/4.1, Claude Opus/Sonnet, Gemini 2.5 Pro/Flash, Llama 3.x, Mistral, DeepSeek, and custom fine-tuned models. We also evaluate agent frameworks (LangChain, AutoGen, CrewAI) and multimodal systems.
- ✅ Good Signal — What the AI did well (reinforcement)
- ❌ Bad Signal — What the AI did poorly (correction + severity)
- 📘 Learning Capture — Novel insights discovered during evaluation
- 🧠 Memory Persistence — Durable rules that must be applied in future evaluations
See Methodology for the complete deep dive.
| Range | Band | Meaning |
|---|---|---|
| 90–100 | Excellent | Exceptional quality, minimal issues |
| 75–89 | Good | Strong quality, minor issues only |
| 60–74 | Acceptable | Adequate, some issues needing attention |
| 40–59 | Poor | Below expectations, significant issues |
| 0–39 | Critical | Unacceptable, major remediation needed |
| Level | Name | Impact |
|---|---|---|
| P0 | Critical | User safety risk, data breach, harmful content |
| P1 | High | Major factual errors, significant hallucinations |
| P2 | Medium | Moderate quality issues, reasoning gaps |
| P3 | Low | Minor issues — tone, formatting, slight redundancy |
| P4 | Trivial | Cosmetic issues, marginal improvement suggestions |
Every insight captured through the Learning pillar goes through a review process:
- Evaluated for novelty and significance
- Tagged with domain and model context
- Analyzed for cross-client patterns (anonymized)
- Fed back to client as actionable recommendations
- Used to refine evaluation criteria over time
Memory rules follow a lifecycle: Proposed → Reviewed → Active → Applied → Quarterly Review → Reconfirmed/Deprecated. Active rules are applied by all evaluators in every future session for the applicable scope (per-client, per-domain, or global).
Three options:
- REST API — Direct HTTP integration for full control
- Python SDK —
pip install asr-feedback-client - JavaScript SDK —
npm install @asr-feedback/client
See the Integration Guide for step-by-step instructions.
| Integration Type | Typical Time |
|---|---|
| SDK integration | 2–4 hours |
| REST API integration | 1–2 days |
| Custom integration (webhook + batch) | 3–5 days |
Yes. Use the Batch Upload API to submit historical AI responses for evaluation. This is commonly used during initial onboarding to establish a quality baseline.
Yes. Enterprise Critical tier clients receive evaluation turnaround within 2 hours. For sub-minute feedback, we offer a pre-screening API that provides automated initial assessment (note: human-in-the-loop deep evaluation follows).
- Calibrated Evaluators — All evaluators must achieve and maintain κ ≥ 0.80
- Dual Evaluation — 10% of entries are independently evaluated by two evaluators
- Regular Audits — 5% real-time audit, daily spot checks, weekly deep audits
- Gold Standard Calibration — Monthly calibration against benchmark datasets
- Memory Rules — Persistent rules ensure consistent treatment of recurring scenarios
See Quality Framework for details.
Current Cohen's Kappa: 0.87 (target: ≥ 0.80). This means substantial agreement between independent evaluators, accounting for chance agreement.
When dual evaluations disagree beyond acceptable thresholds, a senior evaluator adjudicates. The adjudication rationale is documented and used to update calibration materials.
- All data is encrypted at rest (AES-256) and in transit (TLS 1.3)
- PII is automatically detected and redacted before evaluation
- Client data is never shared across clients
- Full audit trail on all data access
- Data retention follows client-specific agreements (default: 24 months)
See Privacy Framework for details.
Yes. Clients can request data deletion at any time. We comply within 30 days per our data handling policy.
No. Client data is used exclusively for the contracted evaluation service. We do not use client data for model training, benchmarking, or any purpose beyond the agreed scope.
| Tier | Monthly Volume | Turnaround SLA | Features |
|---|---|---|---|
| Standard | Up to 500 entries | 24 hours | Core 4-pillar feedback |
| Professional | Up to 2,000 entries | 8 hours | + Analytics dashboard |
| Enterprise Standard | Up to 5,000 entries | 4 hours | + Custom taxonomy, SDK access |
| Enterprise Critical | Unlimited | 2 hours | + Priority queue, dedicated team |
Pricing is based on:
- Monthly evaluation volume
- Turnaround SLA tier
- Number of AI models under evaluation
- Custom requirements (domain-specific expertise, compliance, etc.)
Contact your account manager or email sales@acadifysolutions.com for a customized quote.
Yes. We offer a 2-week pilot program with 50 evaluation entries to demonstrate our methodology and impact. Contact us to get started.
See Also: