Go forward with agentic transformation.
AI is transforming every industry. The question isn't whether to adopt it — it's how.
Most companies know they need AI. Few know where to start, what to prioritize, or how to avoid costly missteps — especially now that agents can act inside workflows and systems. We bridge the gap between AI potential and business reality — with senior experts who've done it before.
Four phases of transformation
Assess
Understand where you are
We evaluate your workflows, data maturity, technology stack, and organizational readiness to identify where AI agents can create measurable value. Through workshops, interviews, and technical audits, we assess which tasks agents could take on and where human judgment is essential.
- AI readiness assessment
- Opportunity mapping
- Data landscape audit
- Stakeholder workshops
Strategize
Chart the path forward
A concrete roadmap tailored to your business — prioritizing quick wins alongside long-term transformation goals. We define the role of agents in your workflows, the permissions and oversight they need, and how to measure their impact. Technology choices and investment decisions follow your business goals.
- AI transformation roadmap
- Use case prioritization
- Technology recommendations
- Investment & ROI framework
Implement
Build with precision
From proof-of-concept to production. We build new applications and systems, extend the ones you already run with AI capabilities, and deploy agents as standalone services where that fits best. AI specialists and senior engineers work together, so what we design is what we deliver — with evaluation, access controls, and human review built in.
- Proof-of-concept (PoC) development
- Production AI systems
- System integration
- Team enablement & training
Evolve
Grow continuously
We monitor how your agents perform in practice — the quality of their work, their operating costs, and when people need to step in. As models, data, and business needs change, we improve reliability and expand the workflows that deliver measurable value.
- Performance monitoring
- Model optimization
- Capability expansion
- Ongoing advisory
Agentic AI in production
See how we've brought AI into existing customer experiences for Moi Mobiili and Silmäasema.
How Moi Mobiili brought AI into the customer experience
Codemate built the Moi AI assistant for Moi Mobiili — a conversational AI layer integrated into the Mun Moi app so customers can manage subscriptions and billing simply by having a conversation.
Read the case study
Silmäasema leverages AI to enhance the customer experience
Codemate built Silmäbotti for Silmäasema – an AI assistant that serves customers around the clock and quickly guides them to the right service or appointment booking. The solution is built on Codemate's Paimen AI platform.
Read the case studyOur principles
Bring AI to what you already run
AI doesn't replace your existing stack — it augments it. Unlock data, accelerate workflows, and bring intelligence to features your customers already use.
Augment existing products
Add intelligence to features your customers already love — smart search, autocomplete, recommendations, summarization — built into your existing UX. Or go a step further: GenUI composes the interface itself from your own components in real time, powered by Paimen.
Unlock your internal data
RAG pipelines that turn documentation, knowledge bases, and operational data into answers your team can act on.
Ship agentic workflows
Agentic AI that operates across your tools — automating steps in support, sales ops, and internal processes that used to need a human in the loop.
Rewrite with AI
When augmenting isn't enough — or isn't possible. Rebuild legacy systems as AI-native products — and ship them in months, not years, with AI-accelerated engineering.
Build your EU AI Act compliance
The EU AI Act's high-risk obligations bite on 2 December 2027 for standalone systems and 2 August 2028 for AI embedded in regulated products — and its transparency rules apply already. If you're accountable for an AI rollout, the regulatory requirements have expanded. Shipping a working feature isn't enough — you also have to show how the system works, who can do what, and how risks are managed when it goes wrong. The Act assigns dozens of obligations across the lifecycle of a high-risk system — risk classification, data governance, technical documentation, transparency, human oversight, quality management, post-market monitoring, incident reporting, conformity assessment, and more. We deliver that work alongside the technology, so the records and evidence the regulation expects are built as the system is built — not assembled afterward.
Risk classification & conformity
Every candidate use case is classified against the Act's risk tiers — minimal, limited, high-risk, prohibited. Decisions are documented, conformity-assessment artifacts prepared, and the work is traceable, so you know which obligations apply before any code ships.
Transparency, documentation & traceability
The records the Act expects high-risk systems to produce: system architecture, data governance, training and validation data, per-interaction logs, risk-management measures. Plus the transparency the Act requires toward deployers and end users — clear information about what the AI does and how, and clear signals when users are interacting with it. Maintained as the system evolves, not assembled retroactively.
Human oversight & quality management
Allow-lists, role-based controls, and review workflows designed into the product from day one. Versioning, testing, and rollback — the quality-management practices the Act expects — become part of the build, not a separate project.
Post-market monitoring & incident reporting
Performance monitoring, incident response, and reporting set up at handover, with the cadence the Act expects. The same loop that keeps your AI sharp keeps your compliance evidence current.
Train your team for the AI era
AI fluency is becoming a baseline skill. We run targeted programs for the audiences that need to move fastest — and embed our experts into your development teams so your people absorb that fluency by building alongside us.
Executives & decision-makers
Strategic primers that connect AI capability to business outcomes. Separate what's ready from what's next, and make confident decisions.
Product & design teams
Workshops on AI-native UX patterns, prompt design, and shipping AI features that actually work for users.
Engineers & builders
Hands-on training in Agentic SDLC practices — planning work with agents, reviewing their output, and validating changes through testing. We also cover LLM application architecture, RAG, agent evaluation, and production AI engineering.
Embed & upskill
Bring our experts onto your development team. You get a product shipped — and your people absorb AI fluency by working shoulder-to-shoulder. Knowledge transfer built into delivery, not bolted on after.
Strategy and execution — from one partner
Most consultancies hand you a strategy deck and fade away. Most dev shops build what you ask without questioning if it's right. We combine strategic thinking with hands-on engineering — and stay with you after launch. Your AI initiatives move from idea to production to continuous growth — one partner, not a handoff between vendors.
What is agentic transformation?
Agentic transformation means redesigning workflows so AI agents can carry out tasks across your systems, with clear responsibilities, permissions, and human oversight. It combines changes to technology, processes, and team skills so agents can contribute reliably to everyday work. We start with a focused workflow, measure the results, and expand from there.
Where do we start if we don't have a clear AI strategy yet?
Most engagements start with Assess — a focused discovery period where we map your current capabilities, data maturity, and the highest-impact AI opportunities. The duration varies with scope: a single use case can be assessed in about a week, a broader organizational baseline in a few weeks, and complex environments longer. You don't need a finished strategy to engage us — producing one is part of what we deliver.
How long does a typical AI transformation take?
It depends — heavily on scope. A focused Paimen integration into an app you already run can ship in a couple of weeks. A broader transformation roadmap with several production AI features takes months, sometimes longer. We size each engagement to what you're actually trying to do, and split bigger transformations into shippable phases so something useful is live at an early stage.
Do you bring AI engineers, or just strategy consultants?
Both — in the same teams. Our consultants and engineers work side by side, so the strategy we recommend is the strategy we build. There's no "throw the deck over the wall" handoff between a consulting team and a separate development team.
Can we start small before committing to a full transformation?
Yes — and we recommend it. Most engagements begin with a focused proof of concept (PoC) or pilot: narrow scope, real production data, a measurable outcome. From there we expand into broader transformation work only if the pilot earns its keep.
How do you handle EU AI Act compliance?
It's part of every engagement, not a separate workstream. We classify each AI use case against the Act's risk tiers, design transparency and human-oversight controls into systems as they're built, prepare the documentation the regulation expects, and set up post-market monitoring at handover. See Build your EU AI Act compliance above for the four delivery areas.
Where does our data live during and after the engagement?
Inside your own infrastructure unless you specifically ask otherwise. We design AI systems to run on your cloud, your data warehouse, and your authentication boundary. Personal data and proprietary training data don't have to leave your stack.
Are we locked to specific AI models or cloud providers after the engagement?
No. We're model-agnostic and cloud-agnostic — Google Gemini, OpenAI, and Anthropic models, plus open-source options; Google Cloud, UpCloud, AWS, Azure, on-prem. Switching providers later doesn't require rebuilding the system; that flexibility is part of the architecture.
Can our internal teams operate the AI capability after handover?
Yes — that's part of how we deliver. Team enablement, runbooks, and operational training are built into the Implement and Evolve pillars. By handover your internal teams own the day-to-day; we stay available for major changes or capability expansion, on a cadence that suits you.
How does Paimen fit into your transformation work?
We bring Paimen in when the work calls for customer-facing AI — for example, intent-driven search inside your app, augmented support, or self-service flows that need to compose the interface on the fly. When the transformation is more about agentic workflows behind the scenes, data pipelines, or backend AI, Paimen might not be the right tool — or at least not the only one needed. Read more about Paimen
Ready to go forward?
Let's explore how AI can create real value for your business.