AI observability: logs, traces, answer quality and cost in one place
See every step your agent takes: which request came in, what the model answered, which tool it called and what it cost. You learn about a failure before your customer does.
What hurts
A customer complains that the bot gave a nonsense answer. You cannot find that conversation or tell which step broke.
The model provider is down for half an hour and you learn about it from managers facing angry customers. There are no alerts.
Nobody knows if the agent works worse today than a month ago. Nobody measures quality until there is a scandal. Then people argue about who is to blame: the model, the prompt or the integration.
What we build
- Logging of every model call with context and response
- Traces of multi-step agents: tools, RAG and latency
- Sampled automatic scoring of answer quality
- Model cost tracking by agent and customer
- Error tracking in Sentry linked to the conversation
- Alerts to Slack or Telegram on outages, latency and overspend
- Masking of personal data in logs, in line with GDPR
What you get
- Grafana dashboards for quality, speed and cost
- Log and trace collection set up inside your perimeter
- Alert rules, each with a note on what to do
- An on-call guide and a short briefing for your team
How we measure the result
- Time from the start of an outage to the alert
- Time to find the cause of a problem conversation
- Average weekly answer quality score
- Cost per conversation by agent
Systems and stack
Industries
Questions
How much does AI observability cost?
From $5,000 over 3–6 weeks. The price depends on the number of agents and services, your existing logging stack and retention requirements. If you already use Grafana or Sentry, we build on them instead of duplicating.
Can you monitor cloud models?
Yes. We log calls from your code, so it does not matter where the model runs: OpenAI, Gemini or self-hosted on vLLM. We monitor the Happ platform the same way: every model call is visible in logs and traces.
How do you score quality without people?
Another model scores a sample of answers against your criteria: accuracy, tone and rule compliance. It does not fully replace a human because the scorer also makes mistakes. So disputed cases go to manual review.
What about personal data in logs?
Phone numbers, names and card numbers are masked before writing. Logs stay in your perimeter with a limited retention period, and only the right people have access. We operate under ISO/IEC 27001:2022.
What happens after launch?
The dashboards and alerts stay yours. We can provide round-the-clock monitoring and incident response under an SLA, or hand on-call duty to your team.
How is this different from regular server monitoring?
Regular monitoring sees that a service is alive. But an agent can be alive and still answer wrongly, loop or call the wrong tool. We add a conversation layer: what was asked, what was answered, and how many steps and how much money it took. That is where most AI system problems are found.
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We reply within one business day. 30 minutes to tell you whether it is worth doing and what it costs.