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The Problem

Difficulty accessing information for

streamlined operations

Crucial data for daily micro-decision making across various departments and hierarchy levels was dispersed across numerous sources, including thousands of documents, graph databases, spreadsheets, and websites. As operations expanded, retrieving specific information became increasingly challenging, leading to significant time delays in accessing even small but essential data.

The Solution

Enterprise level Cognitive Search Engine

A highly advanced cognitive search engine designed to intuitively understand query intent, delivering precise and comprehensive results with exceptional accuracy.
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Documents
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Unstructured Data Sources
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Active Users
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Data Synchronisation Cycle

The Transformation

01

Turning complex data into a unified

knowledge base

In the initial phase, we concentrated on integrating diverse data sources, extracting and standardizing data, and organizing it for optimal search performance. This groundwork enabled the search engine to efficiently locate any required information from the diverse dataset.
Understanding diverse data sources

Data was spread across various sources, including documents, graph databases, spreadsheets, websites, and more.

Data extraction and standardization

Standardized data was extracted via OCR, web scraping, and APIs, and structured through form extraction and layout analysis.

Data Indexing

Data was indexed through metadata tagging, hierarchical organization, and search optimization.

02

Optimizing intelligence for contextually

perfect search

Building on the structured data, we trained specialized machine learning models to enhance the search engine's capabilities. These models improved content interpretation, intent recognition, and precise information retrieval, ensuring the system delivers accurate and contextually relevant results.
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Q&A Model

Utilized advanced NLP to parse questions and retrieve precise answers from extensive datasets, optimizing response accuracy and relevance.

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Document Analysis Model

Excels in interpreting and organizing complex content, boosting data retrieval and decision-making efficiency.

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Cognitive Search Model

Enhanced data discovery by understanding context and intent, delivering precise and relevant search results.

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Intent-entity Model

Accurately identifies user intentions and extracts key entities, enhancing understanding and contextual relevance in generated results.

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Prediction Analysis

Employed predictive algorithms to calculate relevancy scores, optimizing content matching and enhancing the precision of search results.

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Paragraph Extraction Model

Specialized in isolating key paragraphs from large text bodies, enabling focused content extraction and improving overall information clarity.

03

Providing up-to-date results with real-time

data synchronization

We developed an automated pipeline that ensures seamless and continuous data synchronization across multiple data sources. This solution is designed to handle real-time data flow, minimizing latency and ensuring data consistency. By automating the synchronization process, the pipeline reduces manual intervention, prevents data discrepancies, and supports scalable data operations.

The Outcome

Delivering exactly what the users need

With a focus on output precision and user experience, the solution combining useful features with a seamless interface for efficient navigation and access to key information.

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Achieving Perfection

Implemented technical features that elevate performance and maximize efficiency.

Auto-complete

Advanced auto complete feature to search  the content.

01

Query Suggestion

Traditional search backed with AI/ML models and logic for enhanced results.

02

Info-panel

Quick access to the information, links, and files related to the queries.

03

Chatbot

Produce most relevant content based on the conversation.

04

Web Crawler

Crawl the websites and extract text, documents and images in the required output format.

05

Metadata

Link metadata to relevant documents, allowing searches using this metadata.

06

Knowledge Graphs

Define relationships to better connect the data for correct interpretation and to solve complex queries.

07

Query Analytics

Representation of interlinked entities, events, its relationships to interpret the data and infer new facts.

08

Image Search

Leveraging the best OCR technologies, we extract the content from the images to suit our user’s needs.

09

Exceeding expectations,
Happy clients!

In retrospective, SmartSense was a good option in critical phases. They have set the right balance between offering implementation flexibility and long-term product vision when it came down to offering solutions. We have seen that they have responded with high speed in critical phases, and we have never lost time due to alignments since they acquired a good understanding about BASF internal structures and processes.
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Dr. Bjoern Schroeder

BASF - Germany

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