Case
Energy Response
Technology Stack:
Infrastructure:
On-premise
Backend:
Node.js, Express.js, RabbitMQ
Databases:
PostgreSQL, MongoDB, Redis
Frontend:
React.js, Material UI, TailwindCSS
Project Overview:
The energy company faced inefficient business processes: duplicated operations, lengthy approval cycles, extended equipment downtime, and heavy loads on L1-L3 support. The goal was to apply lean methodology and automate all support levels.
01.
Solution Overview
Two core modules were introduced:
Lean Process Analyst: maps every process, detects bottlenecks, and recommends optimisations.
LLM Support Agent: automates multi-level support: L1 - answers FAQs. L2 - analyses ERP/SCADA data. L3 - prepares expert reports.
02.
How the System Operates
Process Mining Engine
The Process Mining Engine analyses all workflows and suggests lean improvements.
L1
On L1 the agent handles FAQs, resolving up to 70 % of requests instantly.
L2
On L2 it inspects ERP/SCADA data and resolves another 20 % without human input.
L3
On L3 for complex cases the agent generates a report, saving experts 40 % of their time.
03.
Automation & Optimisation Results
Before
A maintenance request passed through six departments and took 5 days.
After
Automated approvals cut this to 1 day; downtime fell by 35 % and service costs by 15 %.
04.
Real‑World Scenario
User submits: “Power is out in district N.”
L1
Agent checks the FAQ; if common, it delivers the instruction.
L2
If data are insufficient, it analyses SCADA logs, detects faults, and issues recommendations.
L3
For a large‑scale outage, it compiles an engineer’s report (event log, repair history).
05.
Outcomes
70 % of requests solved instantly at L1
20 % more resolved automatically at L2
Experts spent 40 % less time on critical incidents at L3
Lean optimisation reduced overall costs by 15-20 %
Other Cases
The energy company faced inefficient business processes: duplicated operations, lengthy approval cycles, extended equipment downtime, and heavy loads on L1-L3 support. The goal was to apply lean methodology and automate all support levels.
Country:
Ukraine
Project:
Energy Response
Team size:
5
Duration:
10 months
Budget:
$68000
Industry:
Energy
Technologies:
NodeJS, OpenAI API, React.js, MongoDB, PostgreSQL, Redis, RabbitMQ
Country:
Cyprus
Project:
OpenRTB Advertising Platform
Team size:
4
Duration:
14 months
Budget:
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Industry:
AdTech
Technologies:
Node.js, Express.js, nginx, MongoDB, Clickhouse, RabbitMQ, React, Material UI, TailwindCSS
Lumiveo is a fast-growing e-commerce platform specializing in home decor, furniture, and lifestyle accessories. The brand offers a curated selection of items from top manufacturers, combining quality with style. With a growing customer base across Europe, Lumiveo was ready to invest in its UX/UI, branding, and digital strategy to scale.
Country:
Lithuania
Project:
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Team size:
5
Duration:
8 months
Budget:
NDA
Industry:
E-commerce / Home Decor
Technologies:
UI/UX
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Country:
United States
Project:
Content & Behavior Analytics for Media
Team size:
7
Duration:
22 months
Budget:
$420 000
Industry:
Media
Technologies:
Node.js, PHP, Clickhouse, Redshift, Redis, RabbitMQ, React, TailwindCSS
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Country:
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Project:
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Team size:
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Duration:
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Budget:
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Industry:
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Technologies:
OpenAI API, Node.js, PHP, Express.js, RabbitMQ, PostgreSQL, MongoDB, Clickhouse, Redis, React.js, Material UI, TailwindCSS
At the beginning, we needed an alerting system to notify us when our critical metrics went beyond normal thresholds. However, throughout our collaboration with Yieldy, we received much, much more. Now, what they have built is the command center for our business, which our team uses 24/7.
Country:
Poland
Project:
iGaming Affiliate Network
Team size:
3
Duration:
17 months
Budget:
$94 000
Industry:
Performance Marketing
Technologies:
Node.js, Express.js, MySQL, Redis, Angular
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Svitlana Rakova
Head of Sales
Business acceleration starts today.
Leveraging AI to make things done
Leveraging AI to make things done