Numberskills: How Strategic Power BI Staff Augmentation Helped Numberskills Achieve SEK 29.6M in Revenue
Client
Numberskills
- Industry
Data Analytics and Business Intelligence (BI)
- Region
Sweden (Europe)
Overview
NumberSkills, established in Europe in 2017, is a data analytics consultancy focused on delivering Power BI solutions that help organizations convert complex data into meaningful business insights. As the company expanded its client base and project portfolio across Europe, the growing demand for its services created challenges in scaling operations and maintaining delivery capacity.
A shortage of experienced technical talent in Sweden made it increasingly difficult to manage workloads, support business growth, and meet project timelines. This resource gap limited the company’s ability to scale efficiently while continuing to provide high quality analytics solutions to its clients.
To address these challenges, NumberSkills partnered with Ryoku Systems to strengthen its development capabilities through strategic staff augmentation. The engagement began with a pilot collaboration involving a single developer, which quickly demonstrated strong results. Following the successful initial phase, the partnership expanded to include a team of more than 15 skilled developers, with three engineers working closely alongside the client’s internal team.
This collaboration enabled NumberSkills to accelerate project delivery, expand its service capacity, and reinforce its position as a trusted provider of Power BI and business intelligence solutions across Europe.
- Tools
Technologies Used
Azure
Azure-Devops
Azure Pipeline
Vue JS
Sql Server
Java Script
Type Script
.Net
Asp .Net
Docker
Syncfusion
Nuxt
Bootstrap
Visual Studio
Vs-Code
- Challenges
As NumberSkills expanded its operations, finding qualified technical talent in Sweden became increasingly difficult, creating resource constraints that impacted business growth.
The shortage of skilled professionals reduced delivery capacity, making it challenging to manage multiple client engagements and growing project demands.
Limited development resources affected the company’s ability to expand services, pursue new opportunities, and continue innovating for clients.
- Solutions
A detailed consultation was conducted to identify operational bottlenecks, revealing a critical need for additional technical expertise to support growth.
The partnership began with a trial engagement involving a dedicated developer who quickly integrated with the existing team and demonstrated strong technical capabilities.
Following the success of the pilot phase, the team was expanded to include more than 15 experienced developers, increasing delivery capacity and supporting multiple projects simultaneously.
Three developers worked closely alongside the client’s core team, enabling seamless collaboration, stronger operational alignment, and improved project execution across ongoing initiatives.
Measurable Results
- Achieved SEK 29.6 million in revenue through expanded delivery capacity and growing client demand.
- Successfully onboarded major organizations, including Advania and Marbit, strengthening market presence across Europe.
- Supported 45 active enterprise customers with business intelligence and data analytics solutions.
- Completed 212 projects across multiple industries while maintaining high quality Power BI solution delivery.
Project Team Composition
Role
Offshore Developers
Dedicated Developers
Team Members
15
3
Core Features of the Software
Hybrid RAG Query Engine
Combines Weaviate's semantic vector search with BM25 keyword scoring using a tunable alpha parameter. This hybrid approach ensures highly accurate retrieval for both conceptual queries and telecom-specific terminology.
Automated Document Ingestion Pipeline
Processes PDFs and mixed-format documents through intelligent chunking and OpenAl-powered embeddings. Automatically syncs content to the Weaviate vector database with built-in deduplication and version control.
Context-Aware Query Rewriting
Enhances user queries by incorporating recent conversation context. Rewrites ambiguous or follow-up questions to improve retrieval accuracy without requiring users to restate queries.
Multi-Layer Fallback System
Implements a three-tier response logic: vector database → conversation history → predefined fallback.This layered approach minimizes hallucinations while maintaining consistent conversational flow.
Persistent Conversation Management
Stores threaded conversations in MongoDB with per-user session tracking. Automatically generates conversation titles and summarizes context to support seamless multi-turn interactions.
LLM Self-Evaluation (Judge Layer)
Integrates an intemal evaluation mechanism that scores each response using structured prompts. Enables continuous quality monitoring and data-driven optimization over time.
Timeline
Project Start Time
2018
1
Project End Time
Ongoing
2
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