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Babelfish AI: Real-Time Personalized BI with Accurate Business Insights

Rajesh Nair,CEO

Business owners are increasingly searching for the most secure solution that averts the risk of data breach and losses. Babelfish Tech, headquartered in Bengaluru, facilitates data democratization in the areas of Business Intelligence, Conversational Interfaces and Machine Human Conversations. The company’s core expertise lies in the field of NLP (Natural Language Programming) and Knowledge Graphs.

That’s why; Babelfish Business Intelligence Automation Platform makes it easy for businesses to provide real-time personalized insights to its users. The CEO of the company will let us know how natural language allows every user in the world to manage machines easily.

In conversation with Rajesh Nair, CEO, Babelfish Tech Pvt Ltd

Organizations with comparatively fewer head count than their competing giants are opting for business Intelligence solutions seeking some solace. Given their hunt, what advantages does Babelfish Business intelligence provide to small businesses and enterprises which help them to stand at par with the competition?
The automation features provided by Babelfish can benefit both SME as well as Enterprise users. Further, NLP allows any user type, trained or untrained, to just query and find insights that will help

them make better decisions instantly. Currently, analytics is a manual haul and is out of reach for many SME as BI tools and resources can drain businesses of its budgets. Babelfish BI Automation can take the load out of both resources and tools. Likewise, our SaaS offerings allow SMEs to take their existing solutions to the next level, without risking any investments they have already made.

Babelfish NLP solves these NLP limitations by using a proprietary algorithm for semantic role labelling which allows in extracting meaning from queries

Businesses intelligence system uses NLP to make it easier to read and learn data. What kind of features and system process is used in Babelfish’s Advanced BI system?
Babelfish proprietary NLP engine, which works on the metadata of the knowledge graph, makes it easier to learn and be contextual. The current techniques used in NLP use-cases are limited because the query dimensions are limited; it works purely on keyword match and the queries are pre-populated with fixed keys. This creates a limitation with the kind of answers you can expect. Most NLP queries are descriptive and are not building for predictive or cause-based queries. Babelfish NLP solves these NLP limitations by using a proprietary algorithm for semantic role labelling which allows in extracting meaning from queries. This algorithm is integrated to the knowledge graph to achieve multidimensional analysis covering predictive, prescriptive and causal analytics.

Additionally, Babelfish learns user behavior which extracts keywords and events from user queries to learn the topic of interest of a particular user.

Based on these topics, the machine can discover anomalies, or personalize predictions or insightful recommendations, making the experience completely contextual for a given user.

Data discovery comes as one of the current hottest trends in business intelligence but it is becoming more and more complicated by nature. In what better ways does Babelfish BI system converge, simplify and filter raw data for predictive analysis for businesses?
Babelfish BI uses an explicit data model (Knowledge Graph) to unify available data sources of an enterprise. It is more of a designed experiment than a random observational study. During the data preparation phase, we ensure that all the physical data sources are mapped to the nodes of the model. This data organization uses streaming pipelines to run analytical workflows to compute and update the aggregate nodes, so it is insight ready for real-time insight delivery. Data stored sequentially by time (time series) allows in extracting temporal patterns from past data, which is used to predict the next probable event or value. These values are further compared globally and prioritized using a collaborative filtering algorithm. As we can expect more predictions either as a sales forecast or a probable product recommendation, Babelfish can configure these analytical workflows within the system so we can answer NLP questions that require to be predicted.

Further, tell us about your future innovation goals.
Babelfish’s primary goal is Data Democratization for all type of business. We are in the process of offering NLP wrappers that work seamlessly with point applications like CRM, ECom, Martech and ERP allowing businesses to reverse integrate all their data sources to a single knowledge graph. This will help provide unified insights from data federated from multiple data sources that are collected from point applications.

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