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AI Observability · LLM & Agents

Observe AI
before AI fails users.

Monitor LLMs, AI agents, prompts, vector databases, and infrastructure — with AI-powered root cause analysis and automated remediation that resolves issues before customers feel them.

Applicare — AI Observability⬤ LIVE
LLM Latency (p95)1 slow
gpt-4o
1.45s
claude
620ms
gemini
540ms
llama
410ms
Token Usage & Costtoday
Tokens
48.2M
in 31M · out 17M
Spend
$412
↓ 18% vs avg
Cache Hit
63%
saving $/req
Hallucination
1.4%
flagged for review
AI Agent Health1 degraded
Support agentOK
Search agentOK
Billing agenttimeout ×2
CRM agentOK
Vector DBhealthy
recall
0.94
p95 query
90ms
99.98%
AI availability
30-day SLA
🧠
ArcIn AI: billing-agent timeouts traced to gpt-4o p95 ↑ 1.45s after prompt change #A-318. Root cause: oversized context. Auto-remediation: route to fallback model + trim context.
AI architecture

Full visibility — from prompt to self-healing.

Every layer of your AI stack — apps, agents, RAG, vector stores, model providers — flows into ArcIn AI, which correlates, finds root cause, and remediates automatically.

Sources
👥Users
Application layer
🤖AI Applications🧩AI Agents
AI & retrieval layer
🔎RAG Pipeline🗄️Vector Database🧬Embedding ModelsOpenAI · Claude · Gemini
Integration & telemetry
🔌Business APIs📊Metrics · Logs · Traces
Intelligence layer · ArcIn AI
🧠ArcIn AI🕸️Entity Graph🎯Root Cause Analysis🛠️Auto Remediation
Outcome
💚Self-Healing Systems
How it works

Observe → understand → self-heal.

👁️
Observe
Metrics, logs, traces, prompts, tokens & model events
🔗
Correlate
Connect apps, agents, vector DBs & infrastructure
🧠
Understand
ArcIn AI performs causal root cause analysis
Optimize
Find latency bottlenecks & token costs
💚
Self-Heal
Trigger automated remediation & response
AI request waterfall

Where an AI response spends its time.

Every AI request is broken down stage-by-stage — embedding, retrieval, model, business logic — so the bottleneck is obvious. Here, the LLM call dominates.

Embedding
110ms
Vector Search
90ms
LLM Processing
1450ms
Business API
60ms
Response Gen
70ms

82% of latency is the LLM call — ArcIn flags model, prompt size, and provider as the levers, and can auto-route to a faster model.

Multi-agent dependency graph

See how your agents really connect.

ArcIn maps every agent, model, and datastore. When one degrades, the impacted path lights up red — here, the billing agent's OpenAI dependency.

👤User 🎧Support Agent 🔎Search Agent 💳Billing Agent 📇CRM Agent OpenAI 💬Claude 🔷Gemini 🗄️Vector DB 🧱Redis 🐘PostgreSQL degraded pathhealthy
Prompt analytics

Every prompt, measured.

Most Expensive Prompt
$0.082
checkout-recommend
Slowest Prompt
1.9s
summarize-ticket
Token Usage
48.2M
today
Cache Hit Ratio
63%
cost saved per req
Input Tokens
31M
context + retrieval
Output Tokens
17M
generated
Hallucination Rate
1.4%
auto-flagged
Prompt Success Rate
98.6%
error rate 1.4%
Token cost analytics

Know exactly where the spend goes.

Cost by provider
OpenAI
$5,240
Claude
$3,090
Gemini
$1,780
Llama
$820
Avg Cost / Request
$0.011
↓ 22% MoM
Daily Token Usage
48M
7-day avg
Top Expensive Model
gpt-4o
42% of spend
Est. Monthly Savings
$3.1k
via routing + cache
Agent observability

Watch every agent — and how they talk to each other.

Agent Success Rate
97.2%
across 4 agents
Agent Latency (p95)
1.6s
billing agent high
Agent Failures (24h)
18
12 auto-recovered
Retries
214
avg 1.3 / task
Escalations
6
routed to humans
Execution Paths
37
distinct flows mapped
A2A Messages
9.4k
agent-to-agent / day
Dependencies Mapped
100%
live topology
Failure propagation

One slow model. A revenue problem.

ArcIn traces a failure end-to-end — from an upstream model slowdown all the way to customer and revenue impact — so you see the whole chain, not just the symptom.

⏱️ OpenAI Latency
p95 ↑ 1.45s
⌛ Agent Timeout
billing agent times out
🛒 Checkout Recommendation Failure
recommendations don't load
😟 Customer Impact
degraded experience
💸 Revenue Loss
abandoned checkouts
AI health dashboard

Your AI estate at a glance.

Availability
99.99%
30-day
Latency (p95)
640ms
all models
Error Rate
0.6%
↓ vs last week
Token Consumption
48M
today
Cost Trend
↓ 18%
month over month
Slowest Model
gpt-4o
1.45s p95
Most Active Agent
Support
4.1k tasks / day
SLA Score
A+
all objectives met
How it compares

Traditional monitoring wasn't built for AI.

Metrics, logs, and traces miss the things that break AI apps — prompts, tokens, agents, and retrieval. Applicare sees all of it.

Capability
Applicare AI ObservabilityAI-native
Traditional monitoring
Metrics · logs · traces
Included & correlated
Yes
Prompt tracing
Full prompt & completion
None
Token & cost analytics
Per model, prompt, agent
None
Agent monitoring
Multi-agent + A2A
None
RAG / vector visibility
Recall & query latency
None
Root cause analysis
ArcIn causal AI
Manual
Remediation
Auto & self-healing
Reactive, manual
Time to resolve
Minutes
Hours
Customer outcomes

Real AI workloads. Real results.

Global Retail
ChallengeSlow AI-powered product recommendations hurting conversion.
82%lower latency
74%lower token costs
99.99%uptime
Financial Institution
ChallengeUnpredictable AI agent failures with no clear cause.
91%faster root cause
Zerocustomer-facing outages
lower MTTR
Global SaaS
ChallengeEscalating LLM costs and slow AI responses.
43%lower token spend
72%fewer support tickets
96%fewer escalations
Business outcomes

Reliability the business can measure.

↓ 90%
Reduced MTTR on AI incidents
↓ 80%
Less alert noise
↑ 99.99%
Improved AI availability
↓ 40%
Lower AI / LLM costs
Proactive
Outages prevented before customers notice
↓ OpEx
Reduced operational expense
AI observability that acts

Observe your AI before it fails your users.

Book a demo or start a free trial. We'll instrument your LLMs, agents, and RAG pipeline — with ArcIn root cause and auto-remediation — in under an hour.

  • Prompt & token analytics
  • Multi-agent monitoring
  • RAG visibility
  • Self-healing