We already pay for a monitoring vendor. Is this duplication?

No — we usually make the existing tool work far harder. Standardising instrumentation on OpenTelemetry also means the data stays yours if you ever change vendor.

How do you keep observability costs under control?

Tail-based sampling, tiered retention, and cardinality limits. Most estates over-collect low-value logs and under-collect traces, and rebalancing typically cuts cost while improving diagnosis.

How quickly can an engagement start?

Discovery usually begins within one to two weeks of scope agreement, and a delivery team is typically mobilised within three to four weeks depending on the skills involved.

Can you work alongside our in-house team?

Yes — most of our engagements are blended. We work in your repositories, ceremonies, and tooling, and we plan for knowledge transfer from the start so capability stays with you.

How are engagements priced?

Fixed price for well-bounded projects, monthly capacity for dedicated pods, and outcome-linked SLAs for managed services. We are explicit about assumptions and what would change the number.

NeonAITech services — AI and data engineering, product engineering, cloud and DevOps, managed operations, security and quality

Observability & AIOps

Know why it broke, not just that it did.

Book Free Consultation All Services

60%

Faster incident diagnosis

1 pane

Correlated traces, metrics, and logs

40%

Lower telemetry storage cost

Overview

What Observability & AIOps means at NeonAITech

We instrument systems for genuine observability — traces, metrics, logs, and profiles unified under OpenTelemetry — then apply anomaly detection and correlation so incidents are surfaced and localised automatically instead of being pieced together across five consoles.

Tools & Platforms

OpenTelemetry
Grafana
Prometheus
Loki
Tempo
Datadog

We are not tied to a single vendor — the stack follows the problem, your existing estate, and your team’s skills.

What We Deliver

Instrumentation & Telemetry

Vendor-neutral OpenTelemetry instrumentation across services, with consistent naming, sampling, and cost control.

Unified Observability Platform

Traces, metrics, and logs correlated in one place, with service maps and dashboards built around user-facing journeys.

AIOps & Anomaly Detection

Machine-learning baselining that flags abnormal behaviour early and groups related alerts into a single actionable incident.

Capabilities

Inside the Engagement

OpenTelemetry standardisation

Distributed tracing across service boundaries

Real-user and synthetic monitoring

Log-volume and telemetry cost management

LLM and AI-workload observability

How We Work

A delivery rhythm you can see into

Every Observability & AIOps engagement runs the same four phases, with AI used wherever it removes effort rather than adds novelty.

01
Discover
We map the current state, agree the outcome, and size the work — so scope is a shared decision, not a surprise.
02
Design
Architecture, delivery plan, and success measures are set before build, with costed options where trade-offs exist.
03
Build
Short increments with working output you can review, steer, and stop — never a black box until go-live.
04
Operate
We measure against the agreed outcomes, hand over documentation, and stay on for support where you want it.

Engagement Models

Buy it the way that fits

Fixed-Scope Project

A defined outcome, timeline, and price. Best when requirements are clear and the deliverable is well bounded.

Dedicated Pod

A cross-functional team working to your backlog and priorities, scaling up or down with a month’s notice.

Managed Service

Ongoing ownership against agreed SLAs, with a share of capacity reserved for continuous improvement.