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Data quality: Editorially Reviewed
Language (LLM / NLP)
Process automation
Agent & prompt engineering
Databricks
Data engineering & preparation
Managed AI operations
Insurance
Implementation & integration
Mid-market
Banking
Predictive / classical ML
Business intelligence
Healthcare & life sciences
Pharmaceuticals
ML lifecycle & data
Manufacturing
Logistics & supply chain
Snowflake
Automotive
Staff augmentation (per FTE)
Azure
Data strategy & engineering
Retail & e-commerce
Agentic / autonomous
Forecasting
Enterprise
Fixed-price project
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Kanerika

Data engineering & preparation

Offered by Kanerika Inc

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Overview

Kanerika provides data analytics consulting covering scalable analytics infrastructure, business intelligence and AI-powered modeling, and migration accelerators such as Informatica to Databricks and Tableau to Power BI. It also ships FLIP, a workflow automation platform with AI agents, and Karl, a data insights agent. Partners include Microsoft Fabric and Power BI, Databricks and Snowflake, and it serves banking, healthcare, insurance, manufacturing, pharma, retail, automotive and logistics. Engagement options range from 4-8 week Quick-Start projects to 6-12 month analytics programs and embedded experts. Kanerika Inc is headquartered in Austin, Texas.

Pricing model
On request
Regions served
North America, Europe, Middle East, Africa, Asia-Pacific (APAC), India, Singapore
Categories
Language (LLM / NLP), Process automation, Agent & prompt engineering, Databricks, Data engineering & preparation, Managed AI operations, Insurance, Implementation & integration, Mid-market, Banking, Predictive / classical ML, Business intelligence, Healthcare & life sciences, Pharmaceuticals, ML lifecycle & data, Manufacturing, Logistics & supply chain, Snowflake, Automotive, Staff augmentation (per FTE), Azure, Data strategy & engineering, Retail & e-commerce, Agentic / autonomous, Forecasting, Enterprise, Fixed-price project
Last updated
7 October 2026
Published
7 October 2026

Use cases

Business intelligence
Forecasting
Process automation
ML lifecycle & data

Data sources & provenance

Where this information came from, how it was checked and when it was last looked at. “Last updated” records a change to the record — it does not mean the record was verified.

Data quality
Data quality: Editorially Reviewed
Primary source
kanerika.com
Sources cited
2
Last reviewed
6 October 2026
Last updated
7 October 2026

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