Case Studies

Engineering engagements in regulated environments: generative AI in production, multi-tenant data platforms, cloud security architecture, and the delivery tooling underneath them.

Many of our engagements are covered by client confidentiality agreements. Where a client cannot be identified, we publish the engagement in anonymized form and hold the detail back rather than approximating it.

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Retail and commercial banking
Getting Generative AI Approved to Write to a System of Record
A working prototype is not a production system when a risk function has to sign for it. Kainskep built an ongoing customer due diligence system for a bank in which the model is structurally incapable of the things risk, compliance, finance and security each refused to accept.
6 months Requirements to production
Generative AI
Regulated Environments
Systems Integration
Conversational AI
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Regulated banking, cloud security
Securing a Regulated Cloud Estate That Discloses by Design
Most security work protects a system from the outside. This one had to protect customers from what the system was built to do, because its intended behavior is to display a person's address, contact numbers and source of funds on a channel the bank does not own.
6 Trust zones, every crossing mediated
Zero Production access held by the delivery team
Cloud Security
Zero Trust
Regulated Environments
Microsoft Azure
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Healthcare software, regulated SaaS
Isolated Databases Per Customer, Onboarded in Two Weeks
Giving every customer its own database wins deals in regulated markets and then quietly becomes the ceiling on growth. Kainskep industrialized a per-customer estate until a new environment was a deployment rather than a project, without giving up the isolation that was winning the deals.
2 weeks To bring a new customer environment live
Zero Data paths between customer environments
Data Engineering
Multi-Tenant Architecture
Microsoft Azure
Regulated Environments
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Healthcare software, clinical data
Querying Regulated Clinical Data Without Generated Numbers
A language model that is right most of the time is worse than one that is wrong most of the time, because people stop checking. Kainskep built a production system over regulated clinical data in which the model interprets and explains but is never allowed to compute a value.
32 Failure modes permanently closed
Zero Generated arithmetic in a reported figure
Generative AI
Model Context Protocol
Regulated Environments
Data Engineering
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Engineering delivery, regulated SaaS
Grounding AI Coding Assistants in a Legacy Estate
AI coding assistants know your language. They do not know your system, which is why they help least on the twenty year old platform that runs the business. Kainskep grounded its own assistants in live schema, prior work and run telemetry, and halved the engineering time on a regulated multi-customer estate.
Halved Engineering time attributable to the tooling
AI Engineering
Model Context Protocol
Legacy Modernization
Engineering Delivery
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Multi-service AI SaaS platform
Rebuilding a Live AI Platform on a Second Cloud
A live, multi-service AI SaaS platform was rebuilt in full on a second cloud while all three of its services kept serving production traffic. Close to half the estate was managed AI, which cannot be proved correct the way a database can, so the plan separated what could be verified from what could only be judged.
Zero Maintenance windows required
One User-facing change in the whole migration
Cloud Migration
AI Platform Re-architecture
Infrastructure as Code
Zero Window Cutover
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Multi-service AI SaaS platform
Hardening a Live AI Platform After the Migration
A migration had just finished cleanly, and by any steady-state standard the environment was also wrong: static capacity, no lifecycle rules, on-demand pricing, monitoring that had been sufficient to survive a cutover. None of it was a mistake. It was the bill for having done the migration properly.
Zero Permanently exposed administrative servers
Four Workstreams, each with its own definition of done
Cloud Optimization
FinOps
Observability
Cloud Security
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Case Studies

Getting Generative AI Approved to Write to a System of Record

Securing a Regulated Cloud Estate That Discloses by Design

Isolated Databases Per Customer, Onboarded in Two Weeks

Querying Regulated Clinical Data Without Generated Numbers

Grounding AI Coding Assistants in a Legacy Estate

Rebuilding a Live AI Platform on a Second Cloud

Hardening a Live AI Platform After the Migration

Have a Complex System to Build?