Data pipeline engineering and AI research execution
No data, no AI
AI data collection and analysis (Data & Research) is a comprehensive R&D support service covering the data collection, preprocessing, and pipeline automation that AI/ML projects require, along with technical research execution, PoC validation, and professional report writing. TreeSoop’s KAIST- and POSTECH-trained engineers work with modern data-engineering tools such as Apache Airflow, Prefect, DVC, and Label Studio — combining academic-grade rigor, GDPR-compliant handling of personal data, and a track record in government-funded R&D programs (Korea). Rated 4.92/5 on Wishket, Korea’s largest dev-outsourcing platform.
Years of accumulated data sit scattered across systems, riddled with missing values, duplicates, and inconsistent formats — nowhere near ready for AI model training.
Government-funded or corporate R&D projects demand technical reviews, experiment design, and publication-grade reports — but there are no research specialists on staff.
You want to run a PoC before adopting a new technology, but without proper experiment design and objective evaluation criteria, the results carry no credibility.
We build pipelines that automatically collect data from every source — web crawling, API integrations, database migrations, and IoT sensor streams.
We implement preprocessing pipelines optimized for ML training: missing-value handling, outlier detection, deduplication, normalization, and labeling.
We deliver technology PoCs and R&D projects with rigorous research methodology — experiment design, baseline comparisons, and statistical significance testing.
We turn exploratory data analysis (EDA), visualization, and statistical findings into reports written for an executive audience.
We map your data types, sources, quality levels, and collection feasibility, then run a data gap analysis.
We architect a data pipeline that automates the full journey — collection, preprocessing, storage, and delivery.
We collect data through the designed pipeline and preprocess it until it meets the agreed quality bar.
For PoCs and research projects, we run the experiments and measure and validate the results quantitatively.
We document the entire engagement and complete the final handover with a maintenance guide.
MLOps data pipeline that automatically collects, preprocesses, and augments training data from multiple sources
Analytics system that collects and analyzes booth visitor data to surface insights on traffic flow, engagement, and dwell time
Multimodal emotion data collection across facial expressions, voice, and text, with annotation guideline design and automated quality review
10TB+
Data processed
Cumulative volume across a wide range of industries
95%+
Data quality achieved
Measured by post-preprocessing missing and error rates
4 weeks
Average pipeline build time
From design to production deployment
100%
Documentation delivered
Including data dictionary and pipeline specs
Our experts will recommend the best approach for your project.
