Entity Recognition: Key Insights with Data Engineering

Entity Recognition

The online marketplace for cars wanted to improve search for users by adding full-text and voice search, as well as advanced search with specific options. We built a system application using Machine Learning and NLP methods to process text queries, and the Google Cloud Speech API to process audio queries. This helped greatly improve the user experience by providing a more intuitive and efficient search option for them.

faster service

CX boost

About the client

The leading online car marketplace serving private sellers and over 10,000 dealers across the United States, connecting buyers with all makes and models of vehicles for sale in close proximity.

Tech stack

Pandas

Pyspark

TensorFlow

PostqreSQL

Hadoop

The client's needs

Challenges & solutions

Challenge

To add a full-text and voice search for users across the entire platform.

Solution

Server application as an optimal solution for the marketplace.

Challenge

To transform the search into an advanced search with a specific structured view according to specific parameters: condition, mileage, price, body style, make, model, seller type, transmission, exterior color, etc.

Solution

Tool which in the process of receiving user queries automatically divides them into two corresponding groups:
- Text Queries - natural language processing #NLP using Machine Learning models. The text is cleaned up, the key elements of the query are identified, and the final result is rendered. The average processing speed is less than 100 ms;
- Audio Queries - natural language processing NLP using the Google Cloud Speech API application.

Challenge

Solution

Challenge

Solution

Challenge

Solution

Challenge

Solution

Challenge

Solution

Challenge

Solution

Luxury Retailer AI Demand Forecasting Implementation

Results

Technically proficient and solution-oriented.

Brian Bowman

President Carsoup, automotive online marketplace

The Way We Deal with Your Task and Help Achieve Results

Step 1 of 5

Web development discovery

It's a good time to get info about each other, share values, and discuss your project in detail. We will advise you on a solution and help you understand if we are a perfect match for you.

Step 2 of 5

Discovering And Feasibility Analysis

One of our core values is flexibility. Hence, we work with either one-page high-level requirements or a whole pack of tech docs. In AI demand forecasting case studies, there are numerous models and approaches, so at this stage, we perform a set of interviews to define project objectives. We elaborate and discuss a set of hypotheses and assumptions. We create a solution architecture, a project plan, and a list of insights or features to achieve.

Step 3 of 5

Solution Development

The work starts with data gathering, data cleaning, and analysis. Feature engineering helps to determine your target variable and build several models for the initial review. Further modeling requires validating results and selecting models for further development. Ultimately, we interpret the results. Nevertheless, demand forecasting solution modeling is a process requiring many back-and-forth iterations. We are result-focused, as it's also one of our core values.

Step 4 of 5

Solution Delivery

AI demand forecasting solutions can be a list of insights or models that consume data and return results. Though we have over 15 years of expertise in data engineering, we expect the client's participation in the project. While modeling, we provide midterm results so you can always see where we are and provide us with feedback. By the way, a high level of communication is also our core value.

Step 5 of 5

Support And Continuous Improvement

We understand how crucial the solutions that we code for our clients are! We aim to build long-term relations, providing guarantees and supporting agreements. Moreover, we are always happy to assist with further developments, and statistics show that 97% of our clients return to us with new projects.

Success stories

Check out a few case studies that show why DATAFOREST will meet your business needs.

All Success Stories

Client Identification

The client wanted to provide the highest quality service to its customers. To achieve this, they needed to find the best way to collect information about customer preferences and build an optimal tracking system for customer behavior. To solve this challenge, we built a recommendation and customer behavior tracking system using advanced analytics, Face Recognition, Computer Vision, and AI technologies. This system helped the club staff to build customer loyalty and create a top-notch experience for their customers.

customer retention boost

25%

profit growth

Christopher Loss

CEO Dayrize Co, Restaurant chain

View case study

The team has met all requirements. DATAFOREST produces high-quality deliverables on time and at excellent value.

Data-driven marketing

We created a solution that helped optimize the customer base to get the most out of the customer data. This solution notifies the client about the services/goods, which they would likely buy, according to the gathered information.

20%

sales growth

200%

traffic boost

Jeremy Groves

CEO ThinkDigital, Digital and Marketing Agency

View case study

They developed solutions that brought value to our business.

Employee Tracker

The large Retail company was facing a significant challenge in managing and tracking our employees' working hours and needed a solution that would automate the process and ensure accuracy. We developed a system for counting employees' working hours. Employees simply approach the device upon arrival and the system automatically identifies them and records their check-in time.

100h+

manual work reduced

13%

work experience boost

Bernd Herzmann

CTO Retail company

View case study

DATAFOREST has an excellent workflow and provide constant and close communication. The team brings in a range of technical talent to address issues as they arise.

DevOps Experience

The ML startup faced high costs during its growth for a data-driven platform infrastructure that processes around 30 TB per month and stores raw data for 12 months on AWS. We reduced the monthly cost from $75,000 to $22,000 and achieved 30% performance over SLA.

2k+

QPS performance

70%