LLM features in your product

Search, summarization, classification, drafting assistants and other features built on large language models and integrated into your web application or platform. We choose the model and provider based on quality, cost, latency and data requirements, and design for the cases where the model gets it wrong.

Claude connectors and MCP integrations

Model Context Protocol (MCP) servers and connectors that let AI assistants such as Claude work with your own systems, data and tools, with scoped permissions so the assistant can only do what it is meant to do.

Headless CMS and content pipelines

AI-assisted content workflows on top of headless CMSs: drafting, enrichment and fact-checking, with editors reviewing and publishing everything in the CMS. Laturikotiin.fi runs on this kind of pipeline, with Claude connected to Sanity over MCP.

AI-assisted development

Our own development runs on a structured Claude Code pipeline. Developers scope the work, write the task specifications and divide it into phases. Claude Code implements each phase, changes are committed between phases, and developers review and test every change before work moves on. Unit tests, end-to-end tests, CI checks and manual QA are used according to what the project needs.

Data protection and GDPR

We agree up front which data may be processed by AI services and on what terms. Personal data and confidential information stay out of prompts unless there is a legal basis and an agreement covering it, AI providers are treated as data processors under GDPR, and we use commercial AI services whose terms exclude training on customer data. If your policy rules out AI tools for a project, we don't use them.

Where we don't use AI

Not every task benefits from it. High-fidelity, detail-heavy work and complex, security-critical code such as authentication, payments and access control are written and reviewed by our developers rather than generated.

AI development and integration

We build AI features into real products: LLM-powered functionality, Claude connectors, MCP integrations and content pipelines on top of headless CMSs. Engineered, tested and maintained like any other production software.

AI is useful where it saves real work or makes a product better, and a liability where it's bolted on for its own sake. We help you find the first kind, then design, build and run it with the same engineering standards as the rest of your software: clear scope, proper testing, and data handling agreed before anything touches a model.

Production AI, not demos

LLM features in your product: Search, summarization, classification, drafting assistants and other features built on large language models and integrated into your web application or platform. We choose the model and provider based on quality, cost, latency and data requirements, and design for the cases where the model gets it wrong.
Claude connectors and MCP integrations: Model Context Protocol (MCP) servers and connectors that let AI assistants such as Claude work with your own systems, data and tools, with scoped permissions so the assistant can only do what it is meant to do.
Headless CMS and content pipelines: AI-assisted content workflows on top of headless CMSs: drafting, enrichment and fact-checking, with editors reviewing and publishing everything in the CMS. Laturikotiin.fi runs on this kind of pipeline, with Claude connected to Sanity over MCP.
AI-assisted development: Our own development runs on a structured Claude Code pipeline. Developers scope the work, write the task specifications and divide it into phases. Claude Code implements each phase, changes are committed between phases, and developers review and test every change before work moves on. Unit tests, end-to-end tests, CI checks and manual QA are used according to what the project needs.
Data protection and GDPR: We agree up front which data may be processed by AI services and on what terms. Personal data and confidential information stay out of prompts unless there is a legal basis and an agreement covering it, AI providers are treated as data processors under GDPR, and we use commercial AI services whose terms exclude training on customer data. If your policy rules out AI tools for a project, we don't use them.
Where we don't use AI: Not every task benefits from it. High-fidelity, detail-heavy work and complex, security-critical code such as authentication, payments and access control are written and reviewed by our developers rather than generated.

Laturikotiin.fi, an EV charger comparison and installation platform, was built by a two-person team in two months with this workflow: brand, full-stack platform, custom backend and the Claude + Sanity content pipeline.

Read the Laturikotiin case study

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