Data Engineering
57Blocks rooted in its origins in AdTech’s real-time data streaming, brings a unique competitive edge to data engineering.
Founded by a team with experience at Adobe Advertising Cloud, we have built and optimized large-scale data pipelines that process high-volume, real-time events with low latency—critical for AI-driven applications.
Our deep understanding of data streaming allows us to design robust, scalable infrastructure for AI workloads.
Our Services
LLM-driven Text Processing
Automate data extraction from unstructured content using LLMs
Standardize data transformation for consistent, high-quality datasets
Integrate NLP for semantic search, tagging, and data processing
Accelerate data readiness with ingestion, validation, and refinement
Data Pipeline Engineering
Streams real-time event pipeline and batch data in AI and analytics
ETL/ELT workflows for data extraction, transformation, and integration
Automate pipeline execution with Airflow and Kubernetes scheduling
Validate data integrity with anomaly detection and quality control
Data Annotation and Labeling
Scrape structured & unstructured data from webs, APIs, and documents
Clean data by removing duplicates, missing values, and formatting
Images/text/videos labeling with bounding boxes, segmentation, and etc
Auto annotation for classification, sentiment analysis, and object detection
Business Intelligence
Create analytics systems for trend detection and business insights
Build visualized dashboards with automated updates and reporting
Use anomaly detection to prevent fraud, reduce risk in decision making
Optimize predictive models for market trends, behaviors, and performance
Insights From Building
Which vector database to use? It depends. Based on our experiences, there is no one-size-fits-all "best" database. Instead, a superior vector database is well-matched with its use case, meeting the unique requirements of that scenario. Our comparative analysis offers a promising path to discovering the ideal database for your specific needs.
Today, we are pioneering a new approach to information extraction (IE) from volumes of academic papers. Traditional IE methods, with their reliance on labor-intensive handcrafted rules and patterns, often struggle to generalize across diverse domains and languages. In contrast, we are harnessing the power of Large Language Models (LLMs) from GPT to Claude to complete IE from these documents and compare their performance. We're excited to share our innovative approach in the field of information extraction.
Which vector database to use? It depends. Based on our experiences, there is no one-size-fits-all "best" database. Instead, a superior vector database is well-matched with its use case, meeting the unique requirements of that scenario. Our comparative analysis offers a promising path to discovering the ideal database for your specific needs.
Today, we are pioneering a new approach to information extraction (IE) from volumes of academic papers. Traditional IE methods, with their reliance on labor-intensive handcrafted rules and patterns, often struggle to generalize across diverse domains and languages. In contrast, we are harnessing the power of Large Language Models (LLMs) from GPT to Claude to complete IE from these documents and compare their performance. We're excited to share our innovative approach in the field of information extraction.
