The Seven Sky Case: What Happened Before Deployment
At ISP Seven Sky, technical tickets often accumulated 50-100+ comments. Engineers had to spend 15-30 minutes reading through the thread before they could act, and even then the core signal was often buried under clarifications, side notes, and repetition.
This happened dozens of times a day across the duty shift. For the business, it meant direct waste: expensive engineering time went into reading instead of solving, incident response slowed down, and the risk of mistakes increased because critical context was easy to miss.
- Long comment chains instead of a structured problem view.
- Repeated context loading for every participant joining the ticket.
- Slower reaction at exactly the point where SLA pressure is already high.
What Sensei Does Inside the Task
Sensei, tuned specifically for telecom workflows, analyses the entire task discussion and produces a structured summary directly inside the ticket: what the issue is, what has already been done, what the current status is, and which next step makes sense.
The agent also proposes the open questions that should be closed to move the incident forward faster. The engineer opens the task and immediately sees an interpreted picture instead of a raw stream of comments.
The solution plugs into the existing IT landscape: the task tracker, internal communication channels, and corporate knowledge bases. No process reset and no separate standalone platform are required.
What Sensei connects to
How to Calculate ROI Without a Complex Financial Model
In the documented Sensei case, the saving is about 28 minutes for every entry into a complex task. That number is easy to convert into money even with a conservative model and very few assumptions.
If the shift includes around 20 specialists and each of them needs deep context on roughly 3 complex tasks a day, the saving becomes about 84 minutes per person. Across the department, that is roughly 28 hours per day, or around 3.5 FTE of engineering capacity without adding headcount.
For an operator with strict SLAs, those hours do not disappear. They turn into proactive work, faster outage analysis, network audits, and less downtime.
| Specialists on shift | ~20 |
|---|---|
| Complex tasks per person per day | ~3 |
| Time saved per task | ~28 minutes |
| Time saved per specialist per day | ~84 minutes |
| Freed engineering capacity | ~3.5 FTE |
From Task Analyst to Virtual Network Engineer
Task analysis is the first function that has already proven itself. From there, Sensei scales into a virtual engineer model that helps not only with reading tickets, but with network operations themselves.
The agent can detect vulnerabilities, incomplete configurations, and access-list mistakes, walk through network chains, assess link utilization, and analyse available capacity using the data the company already stores.
Audit and security
Identify vulnerabilities, configuration gaps, and policy errors before they turn into outages or reputational damage.
Shift unloading
Reduce repetitive work on expensive duty teams and free time for actions that actually affect SLA performance.
Current-data operations
Sensei does not replace the stack. It works on top of the monitoring and inventory systems already in place.
Deployment Economics and Control at the Network Core
The two biggest objections to enterprise AI are compute cost and loss of control. Sensei addresses both at the architectural level: the agent works asynchronously through a task manager and does not depend on heavy real-time processing.
Deployment can start on hardware with roughly 60 GB of RAM, comparable to around RUB 350,000 at entry level. In practice, that configuration is sufficient for networks with about 25,000 switches.
At the same time, data never leaves the company perimeter, the final action stays with the engineer, and every conclusion is visible inside the task itself. That makes it possible to lock risks and economics before the pilot is paid for.
Data stays inside the company perimeter.
A human remains in the decision loop.
Risks, data scope, and integrations are fixed during the Vision stage.
In network operations, Sensei is not decorative AI. It is a practical way to return engineering hours to the team, accelerate incident response, and reduce operational risk on accessible infrastructure.
Want to model a similar scenario for your network?
We can design a pilot around your current stack, show where the savings are, and lock the metrics before implementation starts.
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