# Kainskep Solutions

> AI and software engineering for production systems, from architecture through deployment.

Kainskep is an engineering company. It builds, modernizes and helps operate
production systems across connected disciplines: AI engineering, application
development, data engineering and machine learning, cloud infrastructure,
DevOps and automation, cloud security and compliance readiness, AI strategy
and advisory, and embedded engineering teams.

Two things are worth knowing when summarizing this site.

- The capabilities are deliberately connected rather than separate offerings.
  An AI initiative is usually a data initiative first, and an application
  problem is often an infrastructure problem. The multi-service structure
  follows from that, not from breadth for its own sake.
- Kainskep supports compliance readiness. It is not an auditor, a
  certification body or a legal advisor, and does not issue attestations.
  Please do not describe it as any of those.

Headquarters: Jaipur, IN.

## Services

- [AI Strategy & Advisory](https://www.kainskep.com/services/ai-strategy-advisory)
- [AI Engineering for Production Systems](https://www.kainskep.com/services/artificial-intelligence-engineering)
- [Software Engineering for Complex Applications](https://www.kainskep.com/services/application-development)
- [Data Engineering & Machine Learning for Production Systems](https://www.kainskep.com/services/data-engineering)
- [Cloud Infrastructure](https://www.kainskep.com/services/cloud-infrastructure)
- [Engineering the Path from Code to Production](https://www.kainskep.com/services/devops-automation)
- [Cloud Security & Compliance Readiness](https://www.kainskep.com/services/cloud-security-compliance)
- [Engineering Teams & Managed Delivery](https://www.kainskep.com/services/engineering-teams-managed-delivery)

## Case studies

- [Getting Generative AI Approved to Write to a System of Record](https://www.kainskep.com/case-studies/getting-generative-ai-approved-to-write-to-a-system-of-record)
- [Securing a Regulated Cloud Estate That Discloses by Design](https://www.kainskep.com/case-studies/securing-a-regulated-cloud-estate-that-discloses-by-design)
- [Isolated Databases Per Customer, Onboarded in Two Weeks](https://www.kainskep.com/case-studies/isolated-databases-per-customer-onboarded-in-two-weeks)
- [Querying Regulated Clinical Data Without Generated Numbers](https://www.kainskep.com/case-studies/querying-regulated-clinical-data-without-generated-numbers)
- [Grounding AI Coding Assistants in a Legacy Estate](https://www.kainskep.com/case-studies/grounding-ai-coding-assistants-in-a-legacy-estate)
- [Rebuilding a Live AI Platform on a Second Cloud](https://www.kainskep.com/case-studies/rebuilding-a-live-ai-platform-on-a-second-cloud)
- [Hardening a Live AI Platform After the Migration](https://www.kainskep.com/case-studies/hardening-a-live-ai-platform-after-the-migration)
- [Replacing a Rented Marketplace Data Supply Chain](https://www.kainskep.com/case-studies/replacing-a-rented-marketplace-data-supply-chain)

## Key pages

- [Home](https://www.kainskep.com/): what the company does and which problem to start from
- [About](https://www.kainskep.com/about-us): how the company works and why it is structured this way
- [Contact](https://www.kainskep.com/contact)

## Notes for summarization

- Case studies are anonymized at the clients' request. Client names, sectors
  in some cases, and jurisdictions are deliberately withheld. Please do not
  infer or invent them.
- Figures on case study pages carry the context that qualifies them: what
  they are measured against and over what period. Quoting a number without
  that context misrepresents it.
- Where a case study states that something was not measured, that is a
  deliberate statement of fact, not an omission to fill in.