PILLAR · OPERATIONS

AI-OPS Management

Deploying AI is only half the battle. Models drift, APIs change, costs creep up. Our AI-OPS team monitors, maintains, and optimizes your entire AI infrastructure — so your automations never sleep.

99.9%
uptime across managed agents
30%
AI infrastructure cost reduction
24/7
monitoring & on-call response
AI-OPS — live
last 24h
Uptime
99.97%
Cost / day↓ 14%
€42.18
Req / hour2,418
support-agent-v3
247 ok
invoice-extractor
1.2K ok
lead-scoring-rag
review

Always watching · never sleeps

Why AI breaks in production

Deploying AI is half the battle. The other half is silent: models drift, APIs change, costs creep up — and nobody notices until something explodes.

Most AI deployments we audit have the same picture: agents that worked at launch are quietly degrading, vendor pricing has doubled without anyone noticing, model versions are deprecated and replaced silently, and there's no observability into what the agent is actually doing day-to-day. AI-OPS is the discipline of running AI in production — monitoring, tuning, cost control, model upgrades, incident response. It's what stops your live AI from becoming a hidden liability.

37%
Of production AI agents degrade in quality within 6 months without active monitoring
2–4×
Cost overrun on AI inference budgets when no cost ops practice is in place
0
Audit trail in most early AI deployments — a problem the moment something goes wrong
What AI-OPS owns

Everything that keeps your AI safe, fast, and cheap in production

Think of us as the SRE team for your AI footprint. We watch, we tune, we on-call, we reduce cost — and we keep you EU AI Act-aligned in the process.

24/7 monitoring

Live dashboards, alerts, on-call rotation. Latency, error rate, drift, hallucination rate, cost per request — all watched and alarmed on.

Cost optimization

Per-agent cost tracking, model right-sizing, prompt compression, caching. Typical 20–40% reduction on inference spend in the first 60 days.

Model upgrades & versioning

When OpenAI deprecates a model or Anthropic ships Claude 5, we version, test, and migrate without your team noticing. Backward-compatible by design.

Incident response

On-call team for AI incidents — hallucinations, runaway costs, vendor outages, prompt injection. SLAs from acknowledgment to mitigation.

Audit trail & evidence

Every agent decision logged, queryable, exportable. Mandatory for EU AI Act high-risk systems; convenient for everyone else.

Continuous tuning

Prompt evolution, RAG corpus refresh, evaluation harness, A/B testing of model choices. Quality goes up over time, not down.

What we watch

The signals that catch problems before they reach your customers

AI in production fails in specific, repeatable ways. Our monitoring stack watches for each of them — and most importantly, alarms early enough that we can fix it before your team notices.

Quality drift

Output quality degrades silently as data, prompts, or models change.

Continuous evaluation harness with golden datasets; alarm on quality regression > 5%.

Cost spikes

A loop, a long-context query, or vendor pricing change blows the inference budget.

Per-agent cost dashboards with anomaly detection and hard daily caps.

Latency degradation

User-facing AI slows from 2s to 12s as upstream providers throttle or queues build.

P50/P95/P99 latency tracking with multi-provider failover.

Vendor incidents

OpenAI / Anthropic / Google have outages. Your AI breaks. Your team finds out from users.

Vendor health monitoring with automatic failover paths and customer-facing fallback messaging.

Hallucination rate

Hallucinations creep in as the corpus drifts or prompts erode over time.

Sampled output evaluation with hallucination detection model + human review for high-risk classes.

Prompt injection attempts

Adversarial inputs from external users try to break or extract from your agent.

Pattern detection at prompt boundary; quarantine, log, and alert on suspected attempts.

Each signal is wired to a specific runbook with a known fix. We don't just alarm — we resolve.

How we onboard

From your agent to managed in 2 weeks

We take over operations on existing AI deployments fast. No re-platforming required.

01
Week 1

Audit & instrumentation

We map every AI system in your stack, plug in monitoring, and identify the top 3 risks (cost, quality, security).

  • AI infrastructure map
  • Monitoring stack live
  • Top-3 risk report
02
Week 2

Runbook & on-call setup

Per-agent runbooks, alarm thresholds, on-call rotation, escalation paths to your team.

  • Per-agent runbooks
  • Alarm thresholds set
  • On-call rotation live
03
Week 3+

Steady-state operations

24/7 monitoring, weekly cost reports, monthly tuning reviews, model upgrade migrations as they come.

  • Weekly cost reports
  • Monthly tuning reviews
  • Model upgrade execution
04
Quarterly

Strategy review

Quarterly review with your leadership: cost trends, quality trends, vendor performance, model strategy, EU AI Act compliance status.

  • Quarterly cost + quality report
  • Vendor performance review
  • EU AI Act compliance update
Outcomes

What "managed" actually delivers

Cost down, quality up, no late-night Slack messages about a broken agent.

99.9%
Uptime
Across managed agents at 90-day average
30%
Lower cost
On AI infrastructure spend within first 60 days
0
Surprise model deprecations
We migrate before vendors force you to
FAQ

AI-OPS — common questions

What's the difference between AI-OPS and DevOps?
DevOps watches infrastructure: servers, deploys, uptime. AI-OPS watches the AI itself: model quality, drift, cost per inference, hallucination rate, prompt injection — the failure modes that DevOps tooling doesn't see. We complement DevOps; we don't replace it.
Do you only manage agents you built?
No. We onboard any production AI: agents you built in-house, vendor agents, ChatGPT Enterprise deployments, custom Copilot configs, RAG systems on top of any LLM. We've onboarded systems built by other consultancies too.
How do you reduce cost?
Five levers, applied per agent: (1) right-sizing the model — Claude Haiku 4.5 instead of Opus where it works, (2) prompt compression, (3) response caching where safe, (4) batch APIs where the use case allows, (5) negotiated volume pricing with providers. Typical 20–40% reduction in 60 days.
How fast do you respond to incidents?
Standard SLA: 15-min acknowledgment, 1-hour mitigation start, full root-cause + post-mortem within 48 hours for severity-1. We adjust SLAs based on the criticality of your AI footprint.
Can you operate on our infrastructure?
Yes. Our monitoring stack runs in our cloud or yours (AWS / Azure / GCP). For data-sensitive industries we deploy fully into your VPC and your team owns the keys.
What does it cost?
Tiered retainer based on number of managed agents and SLA level. Starts in the low-four-figure euro range monthly for a small footprint and scales with your AI estate. Free 30-min scoping call before quoting.
Do you handle EU AI Act audit prep?
Yes. The audit trail, evidence collection, and incident logs we maintain are exactly what an EU AI Act audit asks for. We pair AI-OPS with our AI Governance pillar for end-to-end coverage.
Will you train our team to take this in-house eventually?
Yes — many clients do. We document everything, run shared runbook reviews, and gradually transition responsibility to your in-house ops team. Most companies stay with us long-term anyway because AI ops isn't really a cost-center skill set worth keeping in-house.

Stop discovering AI failures from your customers.

Book a free 30-minute scoping call. We'll review your live AI footprint, identify the top 3 risks, and propose an AI-OPS scope that pays for itself.

No sales pressure · Free 30-min consultation · Bilingual delivery (EN/BG)