# Datansh

> Data analytics and applied AI for quality-critical manufacturers

Improve yield and reduce quality loss with advanced analytics, AI and workflow automation built around the production and quality checks you already make.
Three capability families, used where they fit the problem: Advanced Analytics to find the likely contributors to quality loss, Predictive AI to test whether earlier warnings are reliable, and Intelligent Automation to route findings to a named owner and record the response.
Two alternative starting points, each fixed in scope and fee: Investigate (understand the loss, 2 to 4 weeks) or Validate (test an approach on plant data, 6 to 10 weeks). Engineers validate causes and corrective actions; nothing writes back to a machine.

## Pages

- [Home](https://datansh.com): Improve yield and reduce quality loss with advanced analytics, AI and workflow automation built around the production and quality checks you already make.
- [Quality analytics, AI and automation | Datansh](https://datansh.com/solutions): Find likely defect drivers, test earlier quality warnings and connect findings to action. Practical analytics, AI and automation for component manufacturing.
- [How we work | Datansh Quality & Yield Intelligence](https://datansh.com/how-we-work): Investigate a quality loss or validate an approach using plant data. Two fixed-scope starting options, followed by implementation only where it makes sense.
- [Manufacturing quality use cases | Datansh](https://datansh.com/use-cases): Quality investigations, earlier-warning tests and response workflows across injection molding, die casting, electronics and semiconductor manufacturing.
- [Manufacturing quality and data case studies | Datansh](https://datansh.com/case-studies): Datansh's manufacturing work in production quality, wafer-lot analysis, reporting and data delivery. Delivered scope and reported figures are separated.
- [Your data, systems and security | Datansh](https://datansh.com/your-data): Review how Datansh plans access, integration and quality decision support around your existing systems and approved environment.
- [Book a Call | Datansh](https://datansh.com/book-a-call): A 45-minute conversation about your quality priority, the KPI that matters and the right starting point. Bring one quality problem.
- [About Datansh | Quality & Yield Intelligence](https://datansh.com/about): Founded in Jaipur in 2022, Datansh brings business understanding, data expertise and delivery experience to quality and yield work in component manufacturing.
- [Careers | Datansh](https://datansh.com/careers): Explore open positions at Datansh and apply to work on data analytics and applied AI for manufacturers.
- [Manufacturing quality insights | Datansh](https://datansh.com/insights): Practical notes on quality investigations, production evidence and making analytics useful to the people responsible for quality and yield.
- [Meet Datansh in Austria | November 15 to 20, 2026](https://datansh.com/austria): Datansh co-founder Nitish Kumar will be in Vienna and Carinthia for ALPS 22, with an interest in practical quality and yield work in component manufacturing.
- [Privacy | Datansh](https://datansh.com/privacy): What Datansh's contact form collects, how the information is used and how to contact us about your information.
- [Imprint | Datansh](https://datansh.com/imprint): The company operating the Datansh website, its contact details and the people responsible for its content.
- [Website terms | Datansh](https://datansh.com/terms): Terms for using the Datansh website, including how to read case-study figures and distinguish them from project commitments.

## Case studies

Projects are anonymized. Client-specific figures or quotes are published only with written permission.

- [Production and quality, visible on the plant floor](https://datansh.com/case-studies/production-quality-and-yield): Automotive lighting components. Shared screens and historical reports brought production, rejects, yield and defect details to plant teams while production was running.
- [Follow wafer losses from lot to process](https://datansh.com/case-studies/foundry-kpi-visibility): Semiconductor foundry operations. Connected lot movements, process readings, quality information and claims gave teams more context on wafer losses.
- [Consistent reporting across a distributed manufacturer](https://datansh.com/case-studies/governed-reporting-center-of-excellence): Semiconductor reporting. Common data models, usage monitoring and controlled releases replaced fragmented development practices.
- [Less manual work behind the reports](https://datansh.com/case-studies/legacy-etl-modernization): Semiconductor data delivery. A costly transformation layer was rebuilt around configuration-driven pipelines that handle schema changes more consistently.
- [From static reports to useful questions](https://datansh.com/case-studies/static-erp-reports-to-interactive): Manufacturing reporting. A connected reporting foundation let users filter, explore and trace manufacturing figures instead of working through static PDFs.
- [A common platform for enterprise reporting](https://datansh.com/case-studies/tire-manufacturer-data-platform): Tire manufacturing. Batch and streaming data, automated releases and monitored pipelines gave business reporting a reusable foundation.

## Facts

- Data residency: Production and quality data can remain inside the customer's approved on-premise, German/EU cloud or existing Azure environment. Datansh does not require production datasets to be relocated to India.
- Security and quality requirements are discussed per engagement: TISAX, GDPR, ISO 27001 and ISO 9001 as they apply. Certificate details on request.
- Your engineers validate findings and approve rules or actions before anything runs. They keep control of critical production decisions.
- Capabilities: Advanced Analytics (see losses as they happen); Predictive AI (catch problems earlier); Intelligent Automation (turn findings into action). Parallel capabilities, not a mandatory sequence.
- Starting points: Investigate, understand the loss (2 to 4 weeks, fixed scope and fee); Validate, test an approach (6 to 10 weeks, fixed scope and fee). Choose either starting point. There is no mandatory sequence. Final scope, fee, timeline and success criteria are agreed in the proposal. No prices are published.
- Offices: K-7, 1st Floor, Income Tax Colony, Durgapura, Tonk Road, Jaipur, Rajasthan 302018, India; Sharjah Media City, Sharjah, UAE; 99 Wall St. #5007, New York, NY 10005, US. Email: info@datansh.com. Phone: +91 98291 26914 (India), +971 56 965 1508 (UAE), + 1 917 6954747 (US). Founded 2022 in Jaipur.
- The one call to action: Book a Call (https://datansh.com/book-a-call). Bring one quality problem.
- Meet us in Austria, November 15 to 20: https://datansh.com/austria

## Insights

- [Quality questions we are taking to Austria](https://datansh.com/insights/going-to-austria-to-learn-how-its-component-manufacturers-handle-recur): 2026-10-15, Datansh. What we want to understand from manufacturing leaders: the loss, the evidence and the action that follows.
- [Can your records tell the story of a rejected part?](https://datansh.com/insights/the-gap-is-rarely-missing-data-it-is-missing-joins): 2026-10-14, Vikas Sharma. A practical way to check whether production and quality records support the question you need to answer.
- [A quality investigation should not start from scratch.](https://datansh.com/insights/the-same-defect-keeps-coming-back-not-because-nobody-looked): 2026-10-13, Nitish Kumar. Keep the evidence and the reasoning from one investigation useful for the next.

Full text of every page and note: https://datansh.com/llms-full.txt