AI monitoring agent — live in production

DevOps solutions,
not consultants.

We package your DevOps scope into fixed-price, outcome-driven engagements — delivered in weeks, not quarters. You define the problem. We deliver a working system.

Weeks
not months to first value
Scoped
before work starts
Yours
all code & runbooks
A
ops-agent APP #platform-alerts
⚠ InferenceHighLatency | WARNING | production
p95 inference latency: 113s (threshold 90s) — 4th firing today
Status: Likely self-resolving. Heavy job completed 13:10 UTC. Fleet idle. Alert clears ~13:20 UTC.
Root cause
Pod worker-pod-a3f9 ran a long-duration job (160s). Heavy jobs occupying 1–3/6 pods pushes short-job queue p95 above threshold.
Recommended
1. Scale to 8 replicas · 2. Raise threshold · 3. Separate routing for long vs short jobs
Not escalating — fleet healthy · self-audit: 10 claims, 2 hypothesis, 1 did-not-check
Works across
Any cloud stack Greenfield & legacy Startups to enterprise AWS · Azure · GCP On-prem & hybrid
The problem

Your DevOps stack is costing
more than it should.

Engineering teams waste cycles assembling tooling from scratch every project. Consultants leave. Docs drift. Releases break.

🔁

"We redo setup every environment"

Every new project means re-assembling CI/CD, IaC templates, and monitoring from scratch. No golden path, no standards.

🚨

"Deployments break every sprint"

Failed releases, manual rollbacks, engineers pulled into incident bridges instead of building product.

👥

"We can't afford a full platform team"

Hiring senior DevOps/SRE takes 6+ months and costs $180k+ per head. But the work still needs to happen.

How we work together

Three ways to engage.
Each with a clear scope.

Every engagement starts with a scoped assessment. Pricing is agreed upfront based on what's actually needed — not a menu you squeeze everything into.

Defined start & end

Project

A time-bound delivery with a specific outcome. We scope it together, agree what done looks like, and deliver. You own everything at the end.
  • Scoped in a discovery session before any work starts
  • Clear deliverables — systems, not slide decks
  • Milestones and checkpoints throughout
  • Full documentation and runbooks on handoff
  • Your team trained and in control at the end
Example scopes
CI/CD foundation · IaC setup · observability stack · Kubernetes migration · security baseline · compliance readiness
Strategic · long-term

Infra Partner

A deeper relationship where we act as your infrastructure supplier and strategic DevOps partner — across multiple teams, initiatives, or the full platform lifecycle.
  • Multi-team or full platform scope
  • Architecture decisions and vendor evaluation
  • Cloud cost strategy and FinOps
  • Hiring and team capability building
  • Quarterly roadmap planning
  • Dedicated point of contact, senior level
Right for you if
You're scaling fast, facing platform complexity across multiple teams, or want an external senior partner who owns outcomes — not just tasks.

All engagements: cloud-agnostic · you own all code & documentation · pricing agreed in discovery, not upfront

A
ops-agent APP 3:17 PM
#platform-alerts
⚠ InferenceHighLatency | WARNING | production
p95 inference latency: 113s (threshold 90s) — 4th firing today
STATUS
Likely self-resolving — primary slow job completed at 13:10:13 UTC. Fleet now idle. Alert should clear as samples age out of 10-min Prometheus window (~13:20 UTC).
Pattern today
10:43 — 1st fire (p95=109.5s): transient queue buildup, self-resolved
11:07 — 2nd fire (p95=116.4s): queue saturation, self-resolved
12:12 — 3rd fire (p95=115s): 3/6 pods on heavy jobs at ~155s
13:08 — 4th fire (p95=113s): 1 pod at 160.2s ← this one
Root cause (confirmed)
Pod worker-pod-a3f9 processed a long-duration job (160.2s). Heavy jobs occupy a pod long enough to starve the short-job queue when ≥2 land concurrently.
[job-7c4a] type=long-form, duration_est=160s, steps=8
resolution=high, mode=quality
Completed: 160.2s
RECOMMENDED FOLLOW-UP
1. Scale worker pool to 8 replicas — more headroom reduces p95 sensitivity
2. Raise threshold or add job-type-specific alert — current threshold too tight for mixed workload fleet
3. Consider separate routing for long-duration jobs — prevents heavy jobs starving short requests
Not escalating — no data loss, no pod failures, no GPU degradation. Fleet healthy and recovering.
self-audit: 10 claims cited · 2 downgraded to hypothesis · 1 entity marked did-not-check
✦ Live in production — AI Agent

Not just an alert.
A full diagnosis, posted to Slack.

That's a real message from our monitoring agent running in a client's production environment. When an alert fires, it doesn't just forward a metric. It traces the root cause, reconstructs the event timeline, confirms fleet state, and posts actionable recommendations — all before an engineer looks at the screen.

🔍
Root cause analysis, not just forwarding
Correlates logs, metrics, and pod state across your fleet to explain why an alert fired — not just that it did.
📋
Pattern detection across firing history
Tracks repeated firings, identifies whether they're structural or transient, and adjusts recommendations accordingly.
🔧
Actionable follow-up, not noise
Every report ends with specific, prioritised next steps — and a clear "no action needed now / escalate if X" decision. Engineers stop waking up to guesswork.
Want this for your stack?
We adapt and deploy the agent to your infra, alert rules, and Slack setup as part of the engagement. Book an assessment call →
What we typically find

The same patterns, across most startups we work with.

After working across HealthTech, FinTech, and SaaS teams, the same inefficiencies show up — and the same fixes work.

Cloud cost
30%+ cloud bill cut in the first month

Most teams we walk into have years of accumulated waste — leftover volumes, snapshots, and instances nobody owns, compute over-provisioned 3-4x, dev environments the same size as prod. We audit, tag, right-size, and clean up. The bill drops immediately, and budget alerts make sure it stays down.

30%+
typical cost reduction
week 1
first savings identified
Visibility & monitoring
From zero visibility to a live dashboard and weekly AI audit

No retention policies means storing everything forever and paying for it. No tagging means nobody knows what anything costs or who owns it. We set up data retention, tag everything, build a cost and resource dashboard, and deploy the AI agent to run stale resource audits every week — automatically flagging waste before it compounds.

weekly
automated stale resource audit
live
cost & infra dashboard
Security hardening
Security tightened without slowing down the team

Overly permissive security groups, secrets in environment variables, no least-privilege IAM — this is standard in fast-moving startups. We harden the configuration, move secrets to a proper store, and enforce guardrails in the pipeline. Teams ship just as fast — they just do it safely.

0
velocity impact
policy
enforced in pipeline
How it works

From first call to live system in 4 steps.

No open-ended engagements. Every stage has a clear output you own.

1

Assessment

Free 30-min call. We map your current stack, pain points, and target outcomes.

2

Blueprint

Scoped implementation plan with milestones and agreed outcomes. Reviewed and signed off before any work starts.

3

Delivery

We build, configure, and test. Weekly check-ins. You see working systems, not decks.

4

Handoff + Retainer

Full documentation, runbooks, and team training. Handoff to your team — or continue as an ongoing engagement.

Get started

Book a free 30-minute DevOps assessment.

We'll map your current stack against your goals and tell you exactly which package gets you there fastest — no pitch, no obligation.

Or email directly: denys@opspackaged.com · Responds within 24 hours