Customer Stories
Teams shipping better AI, measurably
From 12-person startups to 90-person platforms — these teams replaced guesswork with a number they trust.
4
Industries
12–90
Team members
<1 hr
To detect regressions
50%+
Primary metric lift
NovaMed AI
“We were flying blind on 15,000 daily conversations. A prompt tweak silently degraded clinical accuracy — we only found out when physicians called us. qualityindex.ai turned that into a one-hour feedback loop.”
Dr. Anika Patel
CTO & Co-founder
Dr. Anika Patel
CTO & Co-founder
4.2% → 0.8%
Hallucination rate
81% reduction across all specialties
3 days → 1 hr
Regression detection
Mean time to catch quality drops
HIPAA + SOC 2
Compliance
Eval Card docs satisfied both audits
A routine prompt update to improve bedside manner caused hallucination rates to spike on rare conditions — but only rare conditions. Aggregate accuracy looked fine because common cases masked the regression. With 15K daily conversations, manual spot-checking covered less than 0.1% of traffic.
Connected OpenTelemetry and LangSmith connectors to capture every conversation turn. Created Eval Cards for Faithfulness, Clinical Accuracy, and Rare Condition Coverage across 18 specialties. The Safety pillar now flags any response that recommends treatment without citing a guideline source.
CartStack
“40% of our search queries were hitting the embedding model unnecessarily. The Efficiency pillar paid for the entire platform in month one — just from cache optimization insights.”
Marcus Chen
Head of AI & Search
Marcus Chen
Head of AI & Search
+23%
Search relevance
nDCG@10 across all categories
$0.12 → $0.04
Cost per query
67% reduction via caching
40%
Cache opportunity
Queries previously all hitting the model
Search relevance varied wildly across product categories — apparel performed well, electronics and home goods had poor nDCG@10. Cost per query had crept to $0.12 with 800K daily queries, and there was no visibility into which pipeline stages consumed the most tokens.
Deployed OpenTelemetry across the full search pipeline. Created per-category Eval Cards for all 12 product categories. The Efficiency pillar computed per-request cost by stage, identifying that embedding generation for near-identical queries was the largest cost driver. GitHub connector triggers Quality Index rescoring on every retrieval PR.
Lexis AI
“When we upgraded to GPT-4 Turbo, extraction accuracy dropped on multi-party contracts but improved on simpler ones. Without per-model quality tracking, we'd have shipped a net regression.”
Sarah Okafor
VP of Engineering
Sarah Okafor
VP of Engineering
89% → 96%
Extraction accuracy
On complex multi-party contracts
2 weeks
Testing time saved
Replaced manual A/B testing cycles
3
Regressions caught
Before any customer impact
After upgrading from GPT-4 to GPT-4 Turbo, extraction accuracy dropped on complex multi-party contracts (3+ parties, 50+ pages) while improving on simpler ones. The aggregate metric showed improvement, masking the regression on their most valuable contract type. Every model change required two weeks of manual A/B testing.
Linked every deployment to a Quality Index snapshot via the GitHub connector. Created Eval Cards segmented by contract complexity (Simple, Standard, Complex). Built a model comparison dashboard — GPT-4 vs. GPT-4 Turbo vs. fine-tuned Mistral, side-by-side. Automated remediation proposed routing complex contracts back to GPT-4.
FlowOps
“50 tool integrations and zero visibility into which ones dragged down our agent success rate. Turns out 3 tools caused 80% of timeouts. Fixed in one sprint.”
James Whitfield
CTO
James Whitfield
CTO
72% → 94%
Workflow success
Reliable for Tier 1 incidents
45 → 12 min
P95 resolution
73% faster after fixing 3 tools
3 of 50
Root cause
Tools causing 80% of all timeouts
Agent workflows had a 72% success rate with P95 resolution times of 45 minutes — barely faster than manual runbooks. Tool calls failed silently, reasoning chains timed out, and debugging meant grepping CloudWatch logs across dozens of services. Customer trust was eroding.
Deployed OpenTelemetry across the LangGraph execution engine. The Reliability pillar gave each of 50 tools its own success rate. The Efficiency pillar computed P95 time per tool and workflow step, revealing 3 tools causing 80% of timeouts. Created Eval Cards for Workflow Correctness, Resolution Quality, and Escalation Appropriateness.
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