End-to-end tracing
Every request traced through retrieval, tools and model calls — with prompts, outputs, latency and cost attached.
AI Infrastructure
AI Observability & Monitoring
See what your AI is doing in production — latency, cost, quality, failures — and act before users feel it.
- 100%of AI calls traced end-to-end
- <5 minfrom anomaly to alert
- 30%typical cost reduction from visibility alone
Capabilities
See inside every AI decision
Traces, metrics, alerts and production monitoring for LLM and agent systems.
Quality monitoring
Continuous scoring of production outputs so degradation triggers alerts, not churn.
Cost intelligence
Token spend attributed to features, teams and customers — with anomaly detection and budgets.
Incident tooling
Replay, diff and root-cause workflows built for probabilistic systems.
How it works
From black box to glass box
When an AI system misbehaves, 'check the logs' isn't enough. You need the full decision path — what was retrieved, what the model saw, what it did and what it cost.
Tracing
The complete story of every request
One trace shows the user's input, retrieval results, agent steps, tool calls, model responses, latency and cost. Debugging drops from days of guesswork to minutes of reading.
- Spans across retrieval, agents, tools and models
- Prompt and output capture with PII-safe redaction
- OpenTelemetry-native — fits your existing stack
Invoice exception workflowRunning
✓Extract invoice fields1.1s
✓Match against PO0.6s
Resolve mismatch with agentrunning
·Post to ERPqueued
Quality signals
Know quality dropped before users tell you
Sampled outputs are scored continuously against your quality criteria. Drift in inputs, a provider's silent model update, a degraded index — all surface as alerts with examples attached.
- Continuous eval scoring on live traffic samples
- Alerting on quality, refusal-rate and latency shifts
- Dashboards for engineers, ops and leadership
99.9%Uptime
1.2sp95 latency
$0.021Cost / task
QualityCostLatencyDrift
Cost control
Every token accounted for
Spend attributed per feature, team, customer and model — with anomaly alerts that catch the runaway loop at request one hundred, not on the invoice.
- Cost attribution to feature and customer level
- Budget alerts and runaway-loop detection
- Optimization insights: caching, routing, right-sizing
−62%p95 latency
Before
3.4s
After
1.3s
Cost / 1k tasks
$41
Optimized
$14
Use cases
Where observability & monitoring delivers value
Production AI debugging
Trace any bad answer to its root cause — retrieval, prompt, tool or model.
Silent-drift detection
Catch provider model updates and data drift before quality craters.
Cost governance
Attribute and control AI spend across products, teams and customers.
SLA management
Latency and availability tracking per feature with real percentiles.
Compliance evidence
Complete interaction logs supporting audits and incident reviews.
Capacity planning
Usage trends and forecasts that keep budgets ahead of growth.
Delivery
How we build it
We start from the business outcome, then design agents, models, tools and guardrails that can survive production — not just a demo.
- Production-ready architecture
- Secure tool integrations
- Measurable business KPIs
- Operate & improve playbooks
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1
Discover
Map workflows, data, constraints and ROI.
-
2
Architect
Define models, tools, memory and trust boundaries.
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3
Build
Ship a production-ready system with evals and observability.
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4
Scale
Optimize cost, quality and adoption across teams.
A provider updated a model over a weekend and our answer quality dropped nine points. We knew by Monday morning with failing examples in hand — last year, we'd have found out from customers.
Works with your stack
Built on the tools you already run
We integrate with your models, clouds, data platforms and enterprise systems — no rip-and-replace.
- OpenTelemetry
- LangSmith
- Langfuse
- Braintrust
- Datadog
- Grafana
- Prometheus
- Honeycomb
- ClickHouse
- BigQuery
- PagerDuty
- Slack
FAQ
Common questions
We have Datadog. Why isn't that enough?
APM shows you services and latency, but not what the model saw, retrieved or decided. AI observability adds prompt/output capture, token cost and quality scoring — we integrate it into your Datadog rather than beside it.
Does capturing prompts create a privacy problem?
Captured payloads pass through PII redaction before storage, with retention and access controls matching your data policies. For stricter environments we support sampling, hashing or full on-prem storage.
What's the overhead?
Tracing is asynchronous and adds negligible latency. Quality scoring runs on samples, not every request, so cost is controllable and tuned to your traffic profile.
Can you instrument systems we didn't build?
Yes. We retrofit tracing into existing AI stacks — LangChain, custom Python, vendor products with APIs — usually within a sprint or two.
Next step
Turn the lights on in production
If you can't explain your AI's last bad answer, you need observability. We'll instrument your stack in weeks.