Application Management Services
Applications that predict, heal,
and manage themselves.
AI catches anomalies before they become incidents. Models triage alerts, suggest root causes, score CVEs, and trigger self-healing actions. We engineer that intelligence into your operations then own the outcomes.
Our Decade long experience, validated in numbers
Enterprise Customers
What we manage
Six areas. AI embedded in every one
Five testing disciplines, one unified strategy from a single commit to production.
AI Monitoring & Observability
ML models trained on your telemetry learn what normal looks like for every service. Deviations are surfaced 15-30 minutes before threshold breach not after. Logs, metrics, and traces correlate automatically.
Baseline Learning · Noise Suppression
Intelligent Incident Management
AI correlates alerts across services, suppresses duplicates, and suggests probable root causes before an engineer opens a log file. Known failure patterns trigger self-healing runbooks automatically.
Automation · AI Root-Cause
AI Performance Management
ML models track latency trends and predict degradation windows before users notice. Query analysis, N+1 detection and right-sizing recommendations are model-generated.
AI Query Analysis · Capacity Forecasting
AI-Scored Patch & Security
AI scores CVEs by exploitability and blast radius so the highest-risk patches are prioritized. Automated dependency PRs are raised, tested and deployed.
Remediation PRs · Risk-Ranked Patching
AI-Guided Modernization
LLMs assist with legacy code understanding, refactoring suggestions and test generation for untested modules.
Analysis · AI-Generated Test Coverage
LLM-Powered Managed Support
LLMs classify, route and suggest resolutions for L1-L2 tickets. Engineers start with AI-generated context rather than a blank ticket.
Suggestions · Auto-Generated
Escalation Context
Trusted by Leading Enterprises
AI operational loop
Where AI touches every stage of the cycle.
Monitor
Logs · Metrics
ML baseline
Detect
AI anomaly model
15–30m early
Respond
Self-healing
SLA-backed resolve
Optimise
Degradation AI
Perf · Cost · Scale
Patch
AI CVE scoring
Auto-PR · Rollback
Evolve
LLM refactor assist
Modernise · Scale
Tools & ecosystem
The complete AI application management stack.
Essentials
Monitored & Supported
Reliable ops coverage with AI-assisted monitoring without a full-time dedicated team overhead.
- AI anomaly detection & alerting
- 24/7 monitoring coverage
- L1/L2 incident response
- Monthly patch cycles
- SLA-backed ticket resolution
- Quarterly service reviews
Most common
Managed & AI-Optimised
Full operational ownership with AI running across incidents, performance, security, and support.
- Predictive anomaly detection
- Alert correlation & self-healing
- AI-scored CVE patching
- LLM ticket triage & resolution
- L3 engineering support
- Monthly AI performance & cost reports
Full lifecycle
Managed & Evolving
Everything in Managed & AI-Optimised, plus an active AI-guided modernization programme.
- AI root-cause analysis
- LLM-assisted refactoring & test gen
- AI code risk analysis before migration
- Cloud migration & re-platforming
- Architecture advisory
- Feature development support
Tools & ecosystem
The complete AI application management stack.
AI & AIOPS
Monitoring & Observability
Incident & Service Management
APM & Performance
Patch & Security
Cloud & Infrastructure
CI/CD & Release
Everything you need to know
AI-powered application management uses machine learning models trained on a system's own telemetry (logs, metrics, and traces) to learn what normal behaviour looks like for each service, then detect anomalies, correlate alerts, suggest root causes, and trigger self-healing actions automatically. Instead of engineers discovering problems after a threshold is breached, deviations are typically surfaced 15 to 30 minutes before an actual incident occurs.
AI anomaly detection works by learning a baseline of normal behavior for every service from historical telemetry, then flagging deviations from that baseline as they start to emerge, rather than waiting for a fixed alert threshold to be crossed. This early detection window, typically 15 to 30 minutes, gives engineers time to investigate or lets a self-healing runbook trigger automatically before the deviation turns into a customer-facing incident.
A self-healing runbook is an automated response to a known, previously seen failure pattern that resolves the issue without requiring a human to manually intervene, such as automatically restarting a failed service or scaling resources in response to a detected load pattern. AI identifies which known pattern is occurring and triggers the matching runbook automatically, which is what allows incidents that match a known pattern to resolve faster than the typical SLA for manual response.
AI-scored patching evaluates CVEs (Common Vulnerabilities and Exposures) based on exploitability and blast radius rather than just severity score, so the highest actual risk gets prioritized instead of every vulnerability being treated equally. Automated remediation, including dependency update pull requests that are tested and deployed, is often raised directly from this scoring so the highest-priority patches move through review faster.
LLM-assisted modernization uses large language models to help understand legacy code, suggest refactoring approaches, and generate test coverage for modules that previously had none, before a migration or architecture change happens. This matters because modernizing legacy code without adequate test coverage is a common source of regressions, and generating that coverage manually for old, undocumented code is often the slowest part of a modernization project.
Essentials-level support typically covers AI-assisted monitoring, 24/7 coverage, and L1/L2 incident response without a dedicated full-time team, while fully managed support adds predictive anomaly detection, self-healing automation, AI-scored CVE patching, LLM-based ticket triage, and L3 engineering support. A further tier can also include an active AI-guided modernization program, covering code risk analysis, cloud migration, and architecture advisory on top of day-to-day operations.
Where these capabilities apply
Edge-to-Cloud AI across our platforms and services.
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