Daily error analysis and processing, by hand
330 min
Several people, five to six hours, on more than 300 tickets a day.
Case Study
Intelligent automation with actionable AI Agents for the US telecommunication company
Vstorm started with two of more than 80 candidate use cases: field device activation and error-ticket monitoring. The first two were meant to prove that agentic automation could pay for itself — then scale.
The outcome
Two processes, then a blueprint for the rest of the 80
Every field install used to mean a call to a desk where three people drove multiple systems in real time. Error tickets — more than 300 a day — still needed handwritten notes decoded by hand. The agents had to close the job in the field and steer the ticket, without a monolith that hallucinates its way through both.
The figures are from the two production workflows, not a lab wrapper. A 98% activation rate is device activation, not error tickets. The 10× cut is daily error analysis, 330 minutes down to 30.
98%
Automation of device activation workflows
Field install: orchestrator plus five domain sub-agents. Call-center staff moved to higher-value work.
About the client
A US telecommunications provider with more than 45 years in the industry. It delivers fiber-powered internet and video to 150,000+ households in 500+ master-planned communities across two southern states. The company is not named here.
They came to Vstorm as a strategic consulting and engineering partner: more than 80 candidate use cases across the organization, and a need to pick the first two that would show real ROI rather than a slide deck.
Vstorm's impact
Vstorm's impact, the TL;DR
- 98% automation of device activation — routine call-center work dropped, staff redeployed
- Orchestrator plus five sub-agents (devices, network, accounts, troubleshooting, documentation)
- Error analysis cut from 330 minutes to 30 minutes a day — 10× on the same ticket load
- A foundation aimed at tenfold capacity for multi-state operations, without office-hour limits on installs
The challenge
A call for every install, and 300 tickets a day of handwriting
Field installation was a desk process wearing a van. Each job required the technician to call a support center. Three agents then activated the device in real time across multiple systems. That meant labour on every install, no jobs outside office hours, and a bottleneck on expanding beyond the two states they already served.
A second platform already funnelled every customer error — hardware, slow internet, intermittent signal, router or modem, dropped phone — into one stream. Helpful, and still manual: each ticket had to be read from handwritten notes, matched to equipment, and routed. More than 300 requests a day occupied several people for five to six hours and slowed the response.
How we delivered
TriStorm on two of eighty use cases
Think big, start with the two processes that would prove the agents in the field and on the ticket queue — then reuse the pattern.
Pick the first two from eighty
Workshops ranked the 80-plus candidates. Device activation and error monitoring were the ones that would show ROI immediately and leave a path to grow, rather than a proof that lived in a demo.
- Use-case shortlist
- Field-install brief
- Error-ticket brief
Proof of Value on live work orders
A main orchestration agent plus five specialists for the install chat. A three-tier decision engine on the ticket stream: business rules, LLM reading of unstructured notes, then live device telemetry. Narrow roles, so a monolith could not invent a step.
- 1 + 5 field graph
- Three-tier ticket engine
- Mixed-size LLMs by task
Redeploy the desk, keep the van moving
Technicians open a chat with tech ID and work-order ID; the job closes when the system confirms. Tickets get a single recommendation in minutes. Call-center staff move to higher-value work. Installs are no longer gated by office hours.
- 98% activation automation
- 330 min → 30 min tickets
- Staff redeployed
How it works
An install chat in the field, a three-layer engine on the ticket
The field system is a main orchestration agent and five sub-agents: device management, network, accounts, troubleshooting, and documentation. A monolith was rejected so each specialist stays narrow (fewer hallucinations), so a new sub-agent can join without rewriting the core, and so model size can follow the task: a stronger model for orchestration, lighter ones for routine steps.
Technician opens chat Enters tech ID and work-order ID
↓
Main orchestration agent Interprets the request, routes to the right sub-agent
↓
Device management
Network
Accounts
Troubleshooting
Documentation
↓
Job closed System confirms — technician closes the job
Vstorm × undisclosed US telecom — field installation agents
Error monitoring on the existing service platform
The agent sits in the platform the client already used. It reads the ticket (IDs, account, request type, problem codes, timestamps, technician notes), hardware (device IDs, models, health, performance), and context (account status, local outages, earlier tickets). Those streams go through three layers:
- Business-rule analysis — rapid checks against predefined conditions
- AI analysis — models read unstructured notes and catch patterns the rules miss
- Technical analysis — live validation of device health and operating parameters
The three layers become one recommendation for the fastest accurate resolution.
Results
What the first two processes changed
Error analysis and processing time (daily)
10× faster
By hand
330 min
With the monitoring agent
30 min
Device activation is 98% automated. Routine desk work on installs dropped; those people moved to higher-value activity. The same two workflows are the blueprint for the rest of the transformation: start small, keep quality, do not overinvest the remaining 80 use cases before the first two pay.
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