Engineering Capacity Lost to Triage
Teams spending 40–60% of their time on reactive work instead of building, improving, and innovating.
Aditi Consulting · AI Ops Solution Pack
AI Ops Impact Assessment
See How Our Solution Pack Can Make the Difference for Your Team
AI Ops Can Break the Cycle Holding Your Team Back
Most organizations are carrying the same operational debt:
MTTR That Won't Come Down
Despite tooling investments, resolution times keep climbing as complexity outpaces team capacity.
The Same Incidents, Month After Month
No time for lasting fixes. Root causes resurface because the cycle never breaks.
Senior Talent Burning Out
Your best engineers are responding to incidents that automation could resolve. Attrition follows.
Stage 1 of 4 · AI Ops Impact Assessment
What's the State of Your Current Operations?
Tell us about your environment and we'll show you exactly where Aditi's AI Ops Solution Pack makes the biggest impact.
Question 1 of 6
What is the size of your engineering or IT operations team?
Question 2 of 6
How many monitoring or observability tools does your team operate?
Question 3 of 6
What is your average MTTR for critical (P1/P2) incidents?
Question 4 of 6
Are incident response playbooks documented and consistently followed?
Question 5 of 6
What percentage of engineering time is spent on operational firefighting?
Question 6 of 6
Is there executive urgency around operational reliability improvements?
Stage 2 of 4 · Adoption Path
Select Your Preferred AI Ops Adoption Path
Two routes to AI Ops adoption. Choose the one that fits your organization's readiness and risk appetite.
Stage 3 of 4 · The Solution
Before Your Results Snapshot — Here's What Changes With AI Ops
Aditi's AI Ops Package helps engineering teams move from reactive incident response to automated operational resilience — without replacing existing monitoring investments.
With Aditi's AI Ops Package
Before: The Reactive Loop
Alert Fires
↓
Manual Review
↓
Engineer Investigates
↓
Manual Fix
↓
Repeat ↩
After: Automated Resilience
Signal Detected
↓
AI Correlates
↓
Root Cause Found
↓
Auto-Remediated
↓
Verified ✓
Core Capabilities
- Observability and AI Ops foundation
- AI-assisted incident prediction and root cause analysis
- Automated remediation playbooks
- Integration with cloud operations and ITSM platforms
Key Outcomes
- Reduced MTTR
- Fewer escalations
- Faster root cause identification
- Reclaimed engineering capacity
up to 95%
faster MTTR
40–60%
of incidents auto-resolved on average
8–12 wks
to production-ready pilot, typically
Stage 4 of 4 · Your Results
Your AI Ops Impact Snapshot
Based on your assessment responses, here is your operational profile, estimated impact, and recommended starting path.
Operational Profile
Engineering team size—
Monitoring tools—
Average MTTR—
Firefighting time—
Opportunity score—
Estimated Operational Impact
Potential MTTR Reduction
60–90%
Based on Aditi's AI Ops Package deployments
Engineering Hours Reclaimed
—
Estimated annually from automated triage
Recommended Starting Path
—
Select your adoption path to generate a recommendation
Suggested Pilot Scope
1–2 critical services
5 remediation playbooks
60–90 day pilot engagement
Go/no-go decision with documented metrics
Automation Opportunity
—
—
Complete the assessment to generate your impact snapshot.
Common questions
How quickly will we see results? ▼
Week 1: Initial noise reduction (up to 50%). Weeks 2–4: Correlation accuracy at 75–85%. Month 2–3: Full accuracy above 90%, auto-resolution rate established.
Do we need to replace our monitoring stack? ▼
No. Aditi's AI Ops Package is ecosystem-first. It ingests from Splunk, Datadog, Prometheus, PagerDuty, ServiceNow, and most other tools via API.
What if our playbooks aren't documented? ▼
This is the most common starting condition. The engagement includes a playbook inventory and documentation sprint. Undocumented playbooks are a starting condition, not a blocker.
What does a pilot engagement cost? ▼
A typical pilot ranges from $250K–$500K: one service, five playbooks, 60–90 days. Most organizations identify a positive ROI case before the pilot concludes.
Next Step
Explore Aditi's AI Ops Package
Most organizations begin with a focused pilot deployment to validate automation opportunities and operational impact.