Backed by
Continuous Learning for Production AI.
Grow your intelligence in-house.
Most agents stall at "good enough"
Latency
Big frontier models are smart but slow
Quality
Outputs drift from acceptable to unpredictable without warning
Business Rules
Alignment with your domain logic breaks on edge cases
Tool & API Usage
Reliable function calling remains fragile at scale
In production, "good enough" is a liability. Your agents should be your advantage.
A reliability loop for your AI agents.
We build evaluation environments around your real workflows, measure what matters, and continuously optimize so your agent improves over time — not degrades.
Measure
Latency, correctness, tool success rate, and business-aligned quality metrics.
Optimize
Prompt tuning, retrieval improvements, tool policy refinement, and fine-tuning when justified.
Monitor
Continuous evaluation and retraining as data drifts, models change, and workflows evolve.
app.carrotlabs.ai/dashboard/usage
Carrot
Usage
7d
30d
90d
Total Requests
12,847
Input Tokens
4.2M
Output Tokens
1.8M
Unique Models
3
Daily Token Usage
Traces
All
Success
Error
generate_report
gpt-4o
2.1k tok
1.2s
2m ago
extract_entities
gpt-4o-mini
840 tok
380ms
5m ago
tool_call_search
gpt-4o
1.4k tok
4.1s
12m ago
summarize_doc
ft:custom-v2
3.2k tok
890ms
18m ago
classify_intent
gpt-4o-mini
520 tok
210ms
24m ago
Evaluations
24h
7d
30d
Model Comparison
Correctness
Tool Success
Relevance
Coherence
Baseline
Distilled
Fine-tuned
ModelCorrect.Tool UseRelev.
Baseline
8.2
7.5
8.8
Distilled
6.8
6.1
7.4
Fine-tuned
9.4
8.9
9.1