Studenac
Retail operations · Case study
Store growth, without the support queue.
Studenac gave store teams a digital employee for everyday procedures—so expansion could add locations and people without sending every question back to managers and head office.
780+
active store employees
17,700+
operational questions
68%
active in the last 90 days
83%
positive ratings in 2026
Growth outran the onboarding.
Studenac's 1,400+ location network was expanding faster than operational knowledge could travel. A store employee blocked on goods receipt, stocktakes, or a return still needed a person somewhere else to stop and explain it.
- Company
- Studenac
- Industry
- Grocery retail
- Footprint
- 1,400+ stores
- Team
- Master Data & Automation
- Pilot
- Jun–Dec 2025 · 60 staff
- Production
- 2026
The support path before Studenko
One routine question interrupted three layers of the organisation.
Store employee
A procedure blocks the shift
Manager
Context is repeated
Central team
Routine work becomes support
Not a chatbot. A digital employee for store operations.
Studenko is not a generic chatbot layered over documents. It is a digital employee connected to the knowledge, systems, permissions, and escalation path behind store operations.
Connected internal knowledge
Studenko connects to Studenac's knowledge base, operational documentation, back-office systems, and Jira.
Procedures, not generic answers
Employees receive clear steps for inventory, coupons, price checks, returns, and goods receipt.
Role-aware access
Each employee only sees the guidance they are allowed to access, keeping answers relevant and controlled.
A closed improvement loop
When an answer is missing, Studenko can open a Jira ticket so the gap is reviewed once and fixed for every store.
17,700+
Operational questions answered where the work happens.
Adoption moved from a 60-person pilot to daily use across the store network. The strongest signal is not total volume—it is when people choose to use it.
780+
active employees
Scaled from a 60-person pilot in 2025.
83%
positive ratings
Up from 44% during the pilot.
68%
90-day activity
Ongoing use, not launch curiosity.
Pressure hours
Share of conversations across the retail day
75%
Three quarters of conversations happen between 7:00 and 14:00—the stretch when stores are busiest and waiting for head-office support is least practical.
Recurring work
The operating model changed—not just the answer time.
The value appears in what no longer needs to happen: repeated calls, repeated explanations, and a different procedure in every location.
Routine support becomes self-service
17,700+ questions handled; the top question alone was answered 290 times for 252 employees.
Central specialists keep their time for genuine exceptions.
New employees become independent sooner
780+ active employees, averaging 21.4 messages each.
Growth no longer depends on a manager's availability.
Every store receives the same procedure
Standardised guidance is now rated positively by 83% of users.
Execution stays consistent as the store network grows.
Missing knowledge becomes an input
Positive ratings rose from 44% in pilot to 83% in 2026 as documentation gaps were closed.
A gap fixed once improves the answer for every store.
In their words
“Working with Algorise has been outstanding. Studenko is a brilliant implementation of AI that keeps growing and finding new uses day after day.”
Vid Žanić
Master Data & Automation · Studenac