How We Work.
AI-assisted development is not a feature. It is how we operate.
The way software and data systems are built is changing fundamentally. AI is no longer a tool you occasionally reach for — it is present at every stage of how we work: in how we define requirements, how we write and review code, how we test, and how we maintain systems in production.
At Forge, AI-assisted development is our standard operating model across all engagements. This is not about speed for its own sake. It is about applying the right level of automation and intelligence at each stage of delivery — while keeping human expertise, judgment, and accountability exactly where they belong.
FORGE INDUSTRIAL DATA INTEGRATOR.
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We help you to achieve competitive edge by exceeding industry standards on digitalization.
Principal Technology Consultant
Mikko Viskari
050 465 1322
mikko.viskari@forgedigital.fi
From specification to production
Good outcomes start before a single line of code is written. We invest heavily in specification and requirements work, because the quality of what AI can produce is directly bounded by the clarity of what it is asked to produce. Vague goals produce unreliable systems. Precise specifications produce reliable ones.
From there, we move through rapid prototyping to functional systems to production-grade delivery — each stage with its own cadence and quality gates. AI accelerates every phase, but humans own every decision.
FIDI KEY ADVANTAGES
Four levels of AI involvement
Not every system warrants the same degree of AI involvement in its development. We work across the full spectrum, and help clients choose the right level deliberately — based on the nature of the system, the sensitivity of the data, team maturity, and regulatory environment.
Assisted coding — AI handles inline completion, boilerplate, and syntax. Humans write the logic and own all architectural decisions. Typical productivity gain: 1.2×.
AI-augmented — AI acts as a pair programmer: drafting features, generating tests, suggesting refactoring. Humans define the approach and review all output. Typical productivity gain: 1.75×.
AI-first — Specifications drive full AI-generated implementations. Humans primarily review, approve, and own quality, security, and production. Typical productivity gain: 2–3×.
Autonomous agents — Agents handle tickets, perform refactoring, and raise pull requests independently. Humans set goals and guardrails, and handle exceptions. Typical productivity gain: 5× and above.
MODULES & ADAPTERS
Three deployment environments
AI-assisted development introduces real risks alongside its benefits. Code generated at speed requires rigorous review. Models deployed in production can degrade, behave unexpectedly, or expose sensitive data if not properly governed.
We treat security and deployment architecture as foundational — not as final steps. We work across three deployment environments, and the choice of environment shapes how we work from the very start of an engagement.
SaaS / public cloud — Tools such as Claude Code, Cursor, and GitHub Copilot running on public infrastructure. Fastest to adopt and most cost-efficient. Suited to projects where data is not sensitive or regulated.
Self-hosted / private cloud — AI models running in the client's own cloud or on-premise environment. Code and data never leave the client's control. The standard choice for regulated industries and the public sector.
Local / air-gapped — AI models running fully disconnected from any network. The highest security classification, suited to operationally critical systems, defence, and government environments where no external connectivity is acceptable.
ARCHITECTURE
Technical depth as a differentiator
Our team includes people with deep theoretical backgrounds in mathematics and the formal study of how machine learning models behave and fail. This is not incidental. Understanding the actual limitations of AI systems — not just their capabilities — is what allows us to build solutions that hold up in production, in regulated environments, and under real operational pressure.
We apply this depth across software engineering, data platform work, analytics, and AI system design.
DATA SINKS & EDGE INTELLIGENCE
Change, not just delivery
Technology that does not change how people work does not create value. We engage with the organizational and process dimensions of every engagement alongside the technical ones — helping teams adopt new ways of working, defining governance models, and ensuring that what we build gets used.
Delivery is the beginning, not the end.
TECHNICAL DETAILS
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