Technological compatibility

Broad technology expertise, backed by the commitment of experienced professionals

Luoto Company brings together committed and experienced professionals in the industry

Alongside software development and architecture, AI and data have been part of our core expertise since 2017.

When generative AI shifted from research into practice, we were already building in this space — and in 2022 we made it a strategic priority. Today most of our our professionals either build AI solutions or rely heavily on AI in their daily software development work, and we have more than 30 AI implementations running in production.

Luoto AI Lab is how we stay genuinely current in a field that changes fast: continuously tracking the AI landscape, testing and validating new approaches before recommending them, and maintaining the shared codebase, templates and testbeds that feed our teams and clients.

GenAI Solutions

We have delivered solutions across energy, finance, telecom, healthcare, retail, transport and media — knowledge platforms, AI assistants, content generation, and BI solutions — each built to run and be maintained, not just demonstrated.

Building GenAI solutions that work reliably in production requires more than capable models. It requires proper integrations, governance, and a codebase that can evolve as the technology does.

Agentic software development — AI agents integrated into customer-facing products and internal systems
Knowledge platforms, AI assistants, content generation and BI solutions
Customer-facing and enterprise GenAI applications across multiple sectors

AI Architecture

AI architecture provides the technical framework that lets different AI systems work together coherently — with shared foundations for data access, identity management, prompt governance, guardrails and model lifecycle management.

As organization’s adopt AI across multiple systems and use cases, the risk of fragmentation grows with it. AI solutions built in isolation tend to accumulate inconsistent governance, duplicated effort and security gaps.

We have been developing and refining AI architecture patterns across enterprise environments since 2023. In our experience, architecture is not a setup step — it is a continuous discipline as models, agents and use cases evolve.

Read our thinking on AI architecture →

Prompt management and corporate system prompt — consistent AI behaviour across all systems
Unified data access and IAM — structured, secure data access shared across AI solutions
Guardrails and LLM governance — version management, fallback solutions, output control
Agentic frameworks — MCP, A2A, Semantic Kernel and others, enabling agent-to-agent interaction
Retrieval and grounding patterns — RAG and similar approaches for connecting AI to organisational data
CI/CD for AI — model switching and upgrade pipelines that keep transitions manageable

AI Software development

Software development is changing in a fundamental way. AI agents can now participate meaningfully across the full development pipeline — from requirements and architecture through implementation, code review, quality assurance and operations.

We have built and refined this model in our own delivery: every Luoto professional uses AI tooling as standard, with over 90% reporting measurable benefit to their work.

What we validate internally, we bring to clients — through direct delivery or through the coaching and advisory that helps client teams develop the same capability.

End-to-end agentic development pipeline — agents across requirements, architecture, implementation, review, QA and DevOps
AI tooling, workflows and best practices for development teams
Agentic development coaching and sparring for client teams

AI Strategy

Our work in this area spans executive-level engagements across energy, finance, telecom, healthcare, retail, transport and media.

As AI moves from isolated experiments toward a core part of how organisations operate, the questions become harder: where to invest, how to govern, how to build capability that compounds rather than fragments. We work with leadership teams on these questions — developing strategies that connect directly to architecture decisions and delivery, rather than remaining at the level of principles.

AI strategy and governance frameworks
Executive-level engagement across multiple sectors
AI readiness assessment and roadmap development

Data & ML

AI solutions are only as good as the data beneath them. Our data and ML work covers structuring and enriching large-scale product data, building natural language interfaces for business intelligence, and migrating critical data platforms.

These are production implementations, not prototypes.

GenAI data enrichment — structuring and enriching product and operational data at scale
Natural language BI and data access — Text-to-SQL and AI-driven reporting
Data platform migration and modernisation

From Idea to Production

Our AI Lab process covers this journey: from initial sparring and use case assessment, through rapid POC on our own infrastructure, to production delivery and ongoing DevOps.

We find most organisations have more AI ideas than capacity to evaluate and deliver them well. Moving from a concept to something that runs reliably in production requires a structured process, a credible validation step, and the architectural thinking to make the result maintainable.

Business sparring — use case identification, feasibility assessment and approach selection
Rapid POC and validation — own AI Lab infrastructure for quick, credible experimentation
Production delivery and DevOps — full lifecycle from proof to live system

Legacy modernisation

Our work covers exotic and underdocumented technology stacks, COBOL systems and large-scale business platforms — combining AI-assisted analysis and documentation with a structured conversion pipeline that keeps humans in the loop where it matters.

Legacy systems have long represented one of the most stubborn problems in enterprise IT — too costly and risky to replace, too limiting to build on. AI has meaningfully changed the economics: automated code analysis, AI-generated test coverage and agentic conversion pipelines now make modernisation feasible in cases where it previously was not.

AI-assisted codebase analysis and documentation — including rare and exotic technology stacks
AI-generated test coverage and output verification
Agentic code conversion with human-in-the-loop oversight

Customer experience in figures

94

Latest NPS

100%

Latest CSAT

4.9

Latest CES

Want to hear more about our projects?

It goes without saying that our customers take the credit for their digital successes – and we want to keep it that way. At the same time, references are also more closely linked to the professionals than the company, so with the permission of our customers we are happy to tell you in our meeting about the latest, team-specific references that might interest you.

So far in its history, Luoto Company has served more than 35 satisfied customers in various industries. In addition, all our professionals have an average of 15 years of wide-ranging expertise in creating high-quality digital solutions in various fields and for a diverse range of customers.