Mapping & Implementation — From Data Sources to a Complete AI Solution

Your effort during the mapping phase is limited and structured. We start with a kickoff workshop of 2–3 hours where we go through your data sources, systems, processes, and the specific problem we're solving. Participants are typically the people who best understand the data and workflows — this could be IT managers, business developers, team leads, or process owners. After the workshop, we need technical access to relevant systems, which typically means API keys, database access, or exported data files. We then handle the entire technical mapping: inventory of data sources, analysis of data quality, architecture design, and integration planning. During the development phase, we have ongoing check-ins, usually once a week, to ensure the solution develops in the right direction. We never ask for more time than necessary — our process is designed to minimize your effort and maximize results.

We have experience integrating with virtually all systems that organizations use. This includes CRM systems like HubSpot, Salesforce, and Lime, ERP systems like Fortnox, Visma, Microsoft Dynamics, and SAP, e-commerce platforms like Shopify, Centra, WooCommerce, Medusa, and Litium, databases like PostgreSQL, MySQL, and SQL Server, communication tools like Slack, Microsoft Teams, and email, project tools like Jira, Asana, and Monday, support platforms like Zendesk and Freshdesk, and document management systems like SharePoint, Google Drive, and Confluence. Beyond structured data in systems, we also handle unstructured data in the form of PDFs, Word documents, Excel files, web pages, and internal knowledge bases. Integration happens via APIs, webhooks, database connections, or file imports depending on the system. If you have custom-built systems or niche industry solutions, we build tailored integrations. We have yet to encounter a system we couldn't connect.

The timeline depends on the solution's complexity and number of integrations. A simpler solution — for example a RAG-based knowledge assistant or a defined agent workflow — typically takes 2–3 weeks from mapping to production. A medium-sized implementation with multiple data sources, system integrations, and agent workflows takes 3–5 weeks. More complex projects with enterprise data volumes, multiple systems, and advanced agent orchestration take 4–6 weeks. We work in iterations, meaning you see progress continuously and can provide feedback throughout the process. Many clients choose to start with a defined scope and then gradually expand with more features based on results. We always provide a realistic time estimate after the mapping phase — before we write a single line of code. If your timeline is tight, we can parallelize work streams to deliver faster. The most important thing to us is that the solution maintains the right quality, not that it's delivered prematurely.

Quality assurance is built into every step of our process. During mapping, we validate data quality and identify potential challenges before we start building. During development, we test continuously with your actual data, not synthetic test data, to ensure the solution works in your reality. We build validation and control steps into all agent workflows and RAG pipelines — if a step produces an unexpected result, it's automatically flagged. Before deployment, we conduct systematic testing covering functionality, performance, security, and edge cases. We test with the data volume you'll actually use in production, not a small sample. After deployment, we monitor the solution during the first weeks and fine-tune based on actual usage. All solutions are delivered with complete logging and audit trails so you can review and verify results. We never deploy anything we're not satisfied with ourselves.

Implementation doesn't end at deployment. During the first 2–4 weeks after launch, we actively monitor the solution, fine-tune based on actual usage, and ensure everything works as expected. We handle any adjustments needed when the solution encounters real data at full scale. You receive complete documentation describing the architecture, integrations, configuration, and how the solution is maintained. We conduct a handover with your team and offer training so relevant personnel understand how the system works and can monitor it. Many clients choose to continue with ongoing collaboration after the initial implementation. This can involve optimization of existing workflows, development of new agent workflows and automations, expansion of data sources and integrations, or entirely new AI applications identified during the work. We have flexible arrangements for ongoing development and support tailored to your needs and ambitions.

No, it's not a requirement. If you already have a clear picture of what you want to build and which data sources are involved, we can go straight to mapping and implementation. Our mapping phase partly serves the same function as a Readiness analysis — we inventory data sources, understand your systems, and define scope before we start building. The difference is that AI Readiness provides a broader organizational review covering technology, processes, competence, and legal aspects across the entire business, while the mapping phase in an implementation focuses specifically on the project to be executed. If you're unsure where to start or want a comprehensive overview, we recommend starting with AI Readiness. If you already know exactly what you want to build, we can go straight to implementation. And if you've done a Readiness with us before, implementation goes even faster since we already know your organization, your systems, and your priorities.