Before models can learn, reason, or act — they need clean, accessible, and well-structured data. From messy CSVs to fragmented databases and third-party APIs, we help you wrangle the chaos and design a data pipeline that sets your AI up for success. At CONFLICT, we don’t just tune models — we engineer your data layer for performance, clarity, and compliance.
What We Do
Data Audits & ML Readiness Checks
Assess current data architecture, coverage, and fitness for machine learning.
Data Labeling & Curation
Supervised dataset prep, semi-automated annotation pipelines, and labeling tools.
Feature Engineering & Normalization
Extract features, normalize formats, and ensure consistency across datasets.
Ingestion & Integration Pipelines
Pull structured and unstructured data from APIs, logs, cloud buckets, and more.
Data Cleaning & Deduplication
Fill gaps, remove noise, and fix inconsistencies using smart rules and logic.
Compliance & Privacy-Safe Design
Architect with data regulations in mind — including GDPR, HIPAA, and more.
Vectorization & Embedding Prep
Structure your data for retrieval-augmented generation, vector search, and more.
Metadata, Tagging & Ontology Design
Add semantic layers for smarter data discovery and AI understanding.
Stack & Tools We Work With
Apache Airflow, dbt, Dagster, Prefect
Pandas, Polars, NumPy, Spark
Postgres, BigQuery, Redshift, Snowflake
LangChain, Haystack, LlamaIndex
Label Studio, Scale, Snorkel, and other labeling platforms