How does knowledge transfer work when you take over?

A structured transition — shadowing, reverse shadowing, then steady state — typically over four to eight weeks depending on system complexity, with documented runbooks as the deliverable.

What if ticket volume drops significantly?

Good. Contracts are sized on capacity, and freed effort shifts to enhancements and modernisation rather than being billed for idle support.

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

Application Managed Services

An engineering team that keeps your applications healthy and improving.

Book Free Consultation All Services

30–40%

Ticket-volume reduction year one

< 30 min

Response on critical incidents

20%

Capacity reserved for improvement

Overview

What Application Managed Services means at NeonAITech

We take long-term ownership of business applications — support, defect resolution, enhancements, and continuous improvement — under clear SLAs. Instead of a queue that only ever fights fires, a fixed share of capacity goes to reducing the causes of tickets in the first place.

Tools & Platforms

Jira Service Management
ServiceNow
Grafana
Sentry
PagerDuty
Confluence

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

What We Deliver

L2 / L3 Application Support

Triage, root-cause analysis, and permanent fixes by engineers who know the codebase, not a script-reading first line.

Enhancements & Small Change

A reserved capacity stream for backlog items so the product keeps evolving while support runs.

Proactive Health Management

Monitoring, dependency patching, performance tuning, and technical-debt reduction that steadily lowers ticket volume.

Capabilities

Inside the Engagement

Severity-based SLA models

Root-cause analysis and problem management

Knowledge-base and runbook development

Release and regression management

Transition and reverse-transition planning

How We Work

A delivery rhythm you can see into

Every Application Managed Services 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.