Power the digital transformation of your company by including a near-human level of cognition in your applications. By using advanced mathematics and machine learning models you can further improve the efficiency and profitability of your organization.
Cognitive algorithms can understand unstructured data such as images, text, and even sound taking the load off your employees enabling them to focus on more complex tasks.
The next evolution of enterprise intelligence hides in effective AI strategy and cognitive applications.
You don’t need a strong in-house data science team to bring cognitive abilities to your apps, websites, and bots. Grant your business a new perspective, ensure a higher level of customer orientation and give yourself a better chance to take a leading market position.
Business analysis — Defining your pain point, collecting information, and setting expectations.
Data consolidation and understanding — Exchanging information with the in-house expert, obtaining data from data sources, scrubbing data, setting data strategy.
Development — Creating magic. Developing a robust AI solution that meets your business’ needs, e.g. exploratory analysis, interactive dashboards, predictive modeling.
Testing and review — Validating created solution on historical data. Analyzing the implemented approach and returning to data understanding until a developed solution meets the requirements.
Solution — Letting you enjoy the benefits enabled by our solution delivered through Cloud or on-prem with the bonus of customer support or periodical model retraining on request.
Cognitive algorithms are the first AI services to achieve human parity in computer vision, speech, language, and decision-making process.
Bringing intelligence to your decision-making process makes it more informed and efficient, creating a unique experience for every person. The speech and vision can be customized for specific acoustic environments or recognizing particular objects, while the language cognitive ability can be used to analyze, understand and translate text.
Identifying events that deviate from a dataset’s normal behavior (great for detecting and preventing frauds)
Detecting and dealing with unwanted content (could be used for flagging potentially offensive content)
*depending on supported languages
Suggesting the most relevant items to a particular customer (simultaneous upselling and increased customer loyalty aren’t a myth)
Collecting information from images and video, and analyzing extracted content (giving computers the ability to see)
Analyzing faces from images and perceiving facial features and attributes (able to detect and identify people or to recognize human emotion)
Analyzing documents, specifying the form, and extracting data from it (great for processing invoices and receipts)
Using NLP (natural language processing) to understand users’ intents (allowing computers to understand human language)
Modifying knowledge base without any development knowledge (frequent QnA updates contribute to continuing increase of resolution rates)
Extracting entities, relations, and key phrases from unstructured text (a way to get insights from images and sounds)
Real-time translation across more than 100 languages (enables instant text and document translation)
Transcribing audible speech into readable, searchable text (great for call centers and later conversation analysis)
Converting written text to its corresponding audio version (giving computers more natural features)
Translating audio from more than 30 languages in real-time (could be customized for terms specific to your company)
Tools and languages
Keras
Pytorch
ML Ops
Pre-built model foundation (Facebook, Google, Microsoft)
Deployment options
on-prem
Microsoft Azure
AWS
Google Cloud
Every project based on Artificial Intelligence consists of four following steps: business analysis, KPI alignment, dataset preparation, modeling, and deployment.
After training data is organized, tagged, and labeled, it is suitable for modeling and evaluation. When a machine learning model is created, the process continues with deployment to cloud service or on-premise resources and periodical retraining. If you have some necessity for customer support after, we are there to help you.
The time needed for developing a solution based on machine learning and artificial intelligence depends on the specific client’s needs and wishes. A simpler solution like a text classification model can be done in weeks, while complex machine learning algorithms require few months for proper development.
Part of our promise to you is to give you the right value for the price paid. With that in mind, we are open to give you a sneak peek at the power of our solutions. A proof of concept will demonstrate to you how to best use cognitive algorithms to upgrade your existing applications or develop new ones with the ability to read, listen or understand text and images.
There are five recommended steps of the AI initiative. Start by building a portfolio of impactful, measurable, and quickly solvable use cases. Then continue with assembling a set of talents pertinent to these use cases and carry on with gathering the appropriate data relevant to them. After selecting the AI techniques linked to the mentioned use cases, skills, and data, the only step left is to structure the expertise and accumulate AI know-how.
We are ready to help you with any of the previous steps. Contact us at sales@bonsai.tech to discuss use cases, skills, data, technology, or know-how.
