GraphStream AI

Transforming data complexity into competitive intelligence

Knowledge Graphs (KG): Bridging structured and unstructured data landscapes.
Data-driven technology fed with HR data such as ESCO (European Skills Competences and Occupations)

Save time, hire better

Unique value proposition

Comprehensive Data Integration
- Seamlessly connect fragmented information sources
- Break down data silos across organizations
- Create a unified, interconnected knowledge network
Advanced Insight Generation
- Uncover hidden relationships and patterns
- Enable sophisticated predictive analytics
- Support intelligent decision-making
Adaptive Intelligence
- Dynamic knowledge representation
- Real-time learning and evolution
- Continuous contextual understanding

How do Knowledge Graphs transform unstructured data from a challenge into a strategic AI asset?

GraphRAG: Enabling agentic systems to navigate and reason through complex knowledge landscapes

Key differentiators

Our advanced platform transforms complex data landscapes, delivering 40-60% faster data processing and 3x deeper contextual understanding. By enabling rapid knowledge discovery and flexible data adaptation, we convert computational complexity into strategic clarity, empowering organizations to navigate increasingly dynamic information environments with precision and agility.

Competitive advantages

Our intelligent platform revolutionizes data interpretation by mapping intricate relationships and revealing non-obvious insights through sophisticated reasoning. The system supports continuous knowledge refinement through self-updating networks, creating adaptive learning mechanisms that generate predictive intelligence. By transcending traditional data analysis, we provide contextual and nuanced interpretations that offer a multi-dimensional perspective, transforming raw information into strategic understanding that connects complex organizational knowledge in unprecedented ways.

ROI advantages

ROI advantages
Fribl’s GenAI platform transforms talent acquisition by dramatically reducing search time, enhancing decision-making accuracy, and lowering integration costs. By enabling precise talent matching and increasing organizational agility, we convert recruitment from an administrative task into a strategic competitive advantage, delivering measurable value across critical business dimensions.

Quantifiable Benefits

Quantifiable Benefits Our solution delivers transformative performance improvements across critical business dimensions, enhancing data accessibility by 70% while accelerating decision-making processes to generate insights 2-3 times faster. By implementing advanced predictive models, we’ve increased analytical accuracy by 40-50%, enabling organizations to make more informed, timely, and strategic decisions with unprecedented confidence.

The Data challenge

When most of the applications in any industry are focusing on structured data, 80-90% of global data is unstructured!

Unstructured data includes text documents, social media posts, email communications, audio transcripts, video content, images, handwritten notes and more…

The traditional approach (Structured Data) is limited to rigid table-based storage, predefined schemas, limited contextual understanding and minimal semantic interpretation.

The KG is set to handle flexible network of interconnected entities, dynamic relationship mapping, contextual semantic understanding and the ability to integrate multiple data types

Knowledge Graphs definition

KG have rapidly emerged as an important area in AI over the last ten years. A KG may be simply defined as a directed, labeled, multi-relational graph with some form of semantics. It is typically stored in a graph database, which natively stores the relationships between data entities. Entities in a knowledge graph can represent objects, events, situations, or concepts. The relationships between these entities capture the context and meaning of how they are connected.

Ontologies & Knowledge Graphs for a multimodal analysis

Ontologies & Knowledge Graphs for a multimodal analysis Ontologies and knowledge graphs are advanced data structures that are revolutionizing the representation and organization of information. An ontology is a formal framework that defines concepts, relationships, and attributes within a specific domain, thereby creating a common vocabulary for communication and collaboration. Knowledge graphs, in turn, use these ontologies as a foundation to represent data as a network of interconnected entities. Unlike traditional data structures such as relational databases, knowledge graphs offer superior flexibility and adaptability, enabling more efficient integration and analysis of complex and heterogeneous data.
The main components of a knowledge graph include nodes (representing entities), edges (illustrating relationships), and properties (describing attributes). This structure enables a rich and interconnected representation of information, facilitating the discovery of new knowledge and the analysis of complex data. Ontologies, as a conceptual framework, define the possible types of entities and relationships, thus ensuring consistency and structure in knowledge representation. This structured approach allows not only efficient data organization but also advanced reasoning and inference capabilities, which are essential for artificial intelligence and machine learning applications.

Data acquisition & modelling

In this phase, data is gathered from various sources such as resumes but also the ESCO taxonomy. This data is then used to construct an ontology, which represents the knowledge extracted from the collected information. Ontology development involves organizing the data into a structured hierarchy, enhancing its meaning and interpretability. This process ensures that the data is modeled in a way that reflects its relationships and significance within the domain, laying the groundwork for more advanced knowledge representation and processing.

KG construction phase

The data is fed into the KG construction phase, the second research area. In this stage, advanced proprietary natural language processing (NLP) techniques are utilized to extract meaningful information and link entities. Information extraction involves identifying and retrieving relevant pieces of information from unstructured data, such as text from reviews. This process includes recognizing key concepts, relationships, and attributes within the text. Entity linking, on the other hand, involves matching and associating these identified concepts with known entities in a knowledge base or ontology. This step ensures that entities mentioned in the data are accurately connected to their corresponding entries in the KG, thereby enhancing the coherence and usability of the knowledge graph. Our contextual reasoning understand nuanced relationships, inferring hidden connections to provide deeper insights beyond literal interpretation

Multilingual translation

In our increasingly interconnected and globalized world, the ability to communicate effectively across multiple languages has become essential. Multilingual translation models are critical in breaking down language barriers, facilitating seamless communication, and ensuring that content can be delivered accurately across diverse linguistic contexts. Amazon Translate is a robust neural machine translation service designed to deliver fast, high-quality translations that are both cost-effective and customizable. It leverages advanced machine learning techniques to provide accurate translations across a wide range of languages, making it a versatile tool for businesses and individuals seeking to overcome language barriers efficiently.

How it works

Boost your productivity and attract the best talents

They talk about it
better than we do

Straight from our clients

The integration of Fribl has transformed our approach to talent acquisition, introducing a level of efficiency and cost-effectiveness that was previously unattainable.

What used to be a time-intensive process of candidate selection now unfolds within mere minutes. This streamlined efficiency allows us to redirect our efforts towards cultivating meaningful connections with our chosen candidates.

The rapidity and precision afforded by GenAI have significantly enhanced our recruitment strategy, reaching unprecedented levels of seamlessness and satisfaction. It stands as a pivotal advancement in the realm of talent acquisition, serving as a true game-changer for our organization.

François Pichon

Co-Founder Synchroteam

At Yees!, where we are deeply committed to mental health and holistic employee well-being, finding the right psychological support professionals is crucial. Fribl has been instrumental in helping us navigate the complex landscape of recruiting top-tier mental health experts. Their platform allows us to streamline our recruitment process, ensuring we can quickly identify professionals who not only meet our rigorous standards but also align with our comprehensive approach to employee support across professional, personal, and psychological dimensions.

Christophe Launay

Co-founder Yees!

Revolutionizing the fresh product ecosystem, we found that talent acquisition mirrors our core mission—precision, efficiency, and the perfect match. Fribl’s GenAI platform has been instrumental for Consentio, swiftly connecting us with the right professionals in an effective and timely manner. Just as we use technology to preserve food quality, Fribl helps us maintain the quality of our team—making hiring as fresh and reliable as our products.

Vincent Rosso

Co-founder Consentio

Fribl’s GenAI solution has dramatically streamlined our talent acquisition at Gimar&Co. We’ve cut our hiring timeline in half while finding candidates who are both technically qualified and culturally aligned. The ROI has been immediate, both in recruitment costs and team performance.

Stéphane Olmi

Managing Partner & President, Gimar&Co

Fribl’s GenAI technology has transformed how we deliver value to our clients. We now analyze, score, and match top sales talent with unprecedented precision and speed. This allows us to provide our clients with better-qualified sales professionals who drive immediate results. The efficiency gains have also strengthened our marketing services, as we can now connect businesses with talent that truly understands their market positioning. With Fribl, we’re not just filling positions – we’re building revenue-generating teams for our clients.

Dennis O’ Hagan

Managing Partner TheRainMakers

Our most frequently
asked questions

The essentials

What is Fribl Intelligent Matching API?

Fribl is an AI-powered Intelligent Matching API built for Staffing Agencies, HR Platforms and Talent Marketplaces. It tackles critical recruitment challenges through a single API connection, delivering 4 modules: Talent Search (candidate sourcing), In-depth Profile Analysis, Job Description Optimization, and Job Matching (scoring & ranking), with full analytics included.

How do Fribl's modules work together?

Each of the 4 modules can run independently or in combination, driven by our workflow agent, adapting to your use case and specific requirements. The automated workflow handles everything: indexing profiles from your Talent Pool into our Knowledge Graph (capturing both structured and unstructured data), analyzing new candidates sourced via Fribl and enriching your Talent Pool with their insights, optimizing Job Descriptions on the fly or in advance, and running Job Matching against selected candidates or your entire Talent Pool, all powered by our KG structure.

What is Fribl Talent Search?

Fribl Talent Search is our AI-powered sourcing module, giving Staffing Agencies, HR Platforms and Talent Marketplaces access to 800M+ candidate profiles through a single API. Recruiters can search in natural language directly from their ATS, build their own sourcing workflow, and retrieve rich profiles including work history, education, skills and contact information, that can be automatically sinced within their Talent Pool. Unlike keyword-based searches on LinkedIn, Fribl’s semantic search delivers significantly higher match rates, making it the most accurate candidate search engine on the market.

How does the token-based pricing work?

Tokens are shared across all 4 modules and consumed as follows:

-Talent Search: 1 Token

-Profile/CV Analysis (shared in the ATS/HR Platform): 1 Token

-Job Desc Optimization & Enrichment: 3 Tokens

-Job-Matching (scoring and ranking with all explanations shared in the ATS/Platform): 5 Tokens

How have companies traditionally pre-qualified their best candidates until now?

Companies have traditionally identified candidates through manual review, keyword-based systems, and more recently AI parsers, each with significant limitations in capturing candidate potential:

Manual review: Time-consuming, costly, with potential biases
Keywords based systems (in databases and ATS): Miss candidates using different terminology
AI Parsers: Extract information, looks for keywords too and miss context and transferable skills. No AI Act compatibility, as uniquely based on generic LLMs: creating hallucinations, lack of transparency and no results explanations.