Services

01

We assess observability maturity, operational noise, real coverage, and associated cost.

Gives leadership a clear view of the current situation and which decisions should come first.

• Executive diagnosis
• Risk and opportunity map
• Prioritized improvement plan

For technical leadership, platform teams, and operations owners

Assess current state

02

We design the technical foundation to collect useful signals, correlate them, and make them actionable.

Makes applications, infrastructure, and critical services understandable without relying on intuition.

• Signal model
• Pipelines and integrations
• Operational dashboards

For development, platform, and observability teams

Improve visibility

03

We review alerts, reliability criteria, and operating practices to separate signal from noise.

Helps reduce disruption, prioritize incidents, and keep teams focused on what affects users.

• Reliability indicators
• Actionable alerts
• Response guides

For SRE, NOC, operations, and service owners

Reduce operational noise

04

We are building an observability agent with AI to prioritize and analyze signal on your stack. Active project: scoped PoC and exploratory conversation, without replacing your team or sending data outside the agreed perimeter.

Too many alerts and dashboards; too little time to connect signal with operational context.

• Use-case definition (prioritization, triage, correlation)
• PoC in a non-critical environment
• Governance criteria: data, permissions, and human review

For platform, SRE, and operations teams interested in analytical support on their current stack

Explore the approach

05

We instrument Python workloads with OpenTelemetry and OpenLLMetry: latency, errors, cost, and dependencies, integrated into your existing observability backend (Elastic, Dynatrace, or another deployment).

Python services with LLMs in production lack the same visibility as the rest of the platform.

• Inventory of instrumentation points in Python
• OTEL and OpenLLMetry signal schema agreed with the team
• Pre/production validation against existing backend
• Minimum runbook for AI pipeline failures

For teams with proprietary Python code (APIs, workers, assistants) using or planning LLMs in production

Observe AI workloads

06

We support your teams with practical sessions, technical judgment, and maintainable documentation.

Reduces external dependency and accelerates adoption of good practices inside the team.

• Applied workshops
• Internal playbooks
• Adoption criteria

For technical teams and transformation owners

Enable the team