Identify where AI
can make the difference
Map the processes and weak points before touching any tool.
Most AI projects fail because they start from the technology instead of the problem. The Target phase reverses this logic: we step into the company, observe the real workflows, listen to the people who work there every day. The goal isn't to find where to "insert AI", it's to understand where AI can solve a real, measurable and high-priority problem.
In this phase we identify the high-potential processes, assess operational bottlenecks and build the map of opportunities. Only then do we decide where to start.
Explore existing
solutions and use cases
Analyze best practices and tools so you don't reinvent the wheel.
Before building anything, we systematically research what already exists. The AI market evolves every week: there are solutions already working, established frameworks, case studies from comparable industries. Not knowing them means wasting time and resources on problems others have already solved.
The Research phase produces a structured overview of the available options, with advantages, limits and costs (framed in the company's specific context).
Assess requirements,
data and constraints
Understand what it really takes to make AI work in the specific context.
AI works if the data it operates on is good, the processes are clear and the integrations with existing systems are feasible. The Analyze phase answers these questions before writing a line of code or switching on any tool.
We analyze the available data (quality, structure, volume), the legacy systems, the security and compliance constraints, and the expectations of the teams who will use the solution. All of this becomes the project's technical specification.
Launch a fast
validation
Prototype and measure the results before scaling the investment.
You don't build the final system before you have proof it works. The Start phase is when we build a working prototype, complete enough to test the key assumptions, light enough to change quickly.
We define the prototype's KPIs, involve a pilot group of real users, gather usage data and check whether the expected results materialize. Only a validated prototype becomes a production system.
Build improvement
cycles
Listen to the people who use the system every day and refine it based on real data.
An AI system doesn't end at release, it begins at release. The Feedback phase introduces structured cycles for collecting and analyzing user feedback. This isn't about satisfaction surveys: it's about identifying where the system falls short of operational expectations and intervening with precision.
We set up automatic data collection processes, hold periodic review sessions with the teams, monitor usage metrics and prioritize changes by impact.
Integrate AI
into the operational flow
Improve performance and usability. AI becomes a stable part of how the company works.
The Optimize phase turns a system that "works well enough" into one that performs. We work on speed, reliability, output quality and ease of use, with the goal of reducing the friction between the AI system and the people who use it every day.
In this phase we also complete the integrations with the other company systems, automate the data flows and consolidate AI as a natural part of the process, not a separate tool that "you use when you need it".
Replicate the success
across other processes
Transfer the validated solutions to the other teams and workflows.
A successful AI project is the starting point, not the finish line. The Replicate phase leverages what worked, the architecture, the onboarding method, the KPIs, the integrations and applies it systematically to the other processes identified in the Target phase.
We don't start from scratch: we transfer the validated model, adapting it to the new context. The time and cost of each subsequent replication drop significantly because the organization has already built the skills and the confidence in the method.
Manage the risks,
preserve human value
Reduce errors, ensure control and keep people in the lead.
AI introduces new types of risk: systematic errors, technology dependencies, bias in the data, privacy and compliance issues. The Mitigate phase isn't a final phase, it's an ongoing practice that runs through the whole method and becomes more structured as the systems scale.
We define monitoring and alert protocols, establish procedures for human intervention in critical cases, verify regulatory compliance (GDPR, the European AI Act) and train the team to recognize and manage the limits of the AI systems in use.