Data & AI

Generative AI, new regulatory requirements and shifting competitive pressures are forcing companies to make concrete decisions: where is it worth investing in data and AI, and where is it not? Which use cases are viable, which organisational structure is appropriate, and which tools should be allocated to whom? We help you answer these questions on a sound basis – with a clear view of the effort involved, the benefits and the feasibility within your organisation.

• AI Strategy & Roadmap: Not knee-jerk action, but a clear direction
  We work with you to develop an AI strategy and roadmap with a clear organisational and business focus – realistic, actionable and tailored to your organisation. We incorporate ‘Agentic AI’ from the very start: wherever agentic approaches can drive processes, products or development, this is embedded in the vision, areas of action and prioritisation of the roadmap. There is no one-size-fits-all solution here.
• Data-driven business models & use cases: Where does the real value lie?
  Together, we identify use cases that really pay off – and prioritise what should be tackled first. We incorporate Agentic Coding right from the start: this allows data science and AI projects to be transformed from ideas into productive models and applications much more quickly, which has a direct impact on the business case and prioritisation.
• AI Governance & Compliance:
  Deploying AI in compliance with regulations – before the regulator becomes a problem.
• EU AI Act, data protection, internal policies:
  We ensure that your AI initiatives are set up securely and in compliance.
• Organisational Design & Data/AI Operating Model: Structure trumps strategy.
  Even the best concept will fail if the organisation is wrong. We design the operating model that supports your data and AI ambitions – including determining which AI tools belong in which roles (from sales and HR to senior management) and how AI agents, as part of teams and processes, reshape responsibilities, interfaces and governance.
• Change Management & AI Enablement: Technology alone is not enough.
  AI transformation can only succeed if people are on board – across all roles, topics and disciplines. We support your organisation from senior management right down to the specialist departments, train staff in the productive use of AI tools and AI agents in their day-to-day work, and establish the routines that ensure reliable collaboration between humans and agents.

To bring data to life and use it to shape your business, you need a solid yet scalable foundation on which to build.

We consider architecture, security, governance and operations together, not one after the other. We utilise generative AI and agent-based approaches not only for your use cases, but right from the outset when building the platform itself – faster, more consistently and with greater traceability.

• Architecture & Technology Selection: Vision first, then the stack
  We design the platform architecture around your business objectives – not around the vendor with the most high-profile roadmap. Build vs. buy, cloud strategy, data flow, integration into your IT landscape: we make the structural decisions that will underpin the platform for years to come.
• Platform Development with Agent-Based Support: From Blueprint to Operation
  We build your platform using agent-based processes – agents under our control handle provisioning, configuration and recurring tasks. This significantly reduces the time-to-platform and ensures the platform remains reproducible, documented and operational.
• Data management with agents in the engine room: Quality isn’t achieved by hoping for the best
  Data quality, lineage, cataloguing and access are not by-products – they are the prerequisites for AI and analytics to function reliably. We establish a data management system in which agents handle routine checks, classifications and deployments, whilst your teams focus on business decisions.
• Migration & Modernisation: Legacy is not a foregone conclusion
  Letting go of old data environments rarely fails because of the technology, but rather because of the effort and risk involved. We migrate with agent-based support – analysis of legacy systems, mapping, and code and pipeline transformation are partially automated. The result: a realistic migration path instead of yet another ‘someday’ programme.
• SODA – Sovereign Data Platform: Control over data, technology and operations
  SODA is our open-source-based data platform for businesses that want to actively avoid vendor lock-in. It runs in any environment – on-premises, private cloud, hyperscalers – and can be ported between them without major reconfiguration. Reliable operation is ensured by adesso’s managed service.

In most companies, the problem isn’t a lack of data, but the fact that everyone has their own. Three departments, three versions of the truth, an hour-long meeting just to decide which figure is correct. Business Analytics from adesso provides a single, reliable view – so that discussions focus on decisions, not definitions.

In practical terms, this means: consistent key performance indicators instead of data silos, modern dashboards instead of ad-hoc Excel spreadsheets, and self-service instead of queuing for reports. We build analytics solutions that are used in day-to-day operations – not ones that merely impress once a quarter.

• BI Architecture & Dashboard Development: An End to Siloed Reporting
  We consolidate the ad-hoc BI landscape into a robust architecture: a shared database, uniform calculation logic, and a consistent authorisation concept. We develop dashboards based on the decisions they are intended to support – not on what the respective data source happens to provide at any given moment.
• Semantic Layer & Data Modelling: A Common Language for Your Figures
  Turnover, margin, active customer – if every department defines these terms differently, even the best dashboard won’t help. We build a semantic layer that clearly defines business terms once and for all and makes them consistently available across all tools. Business units can find the right data themselves – without needing specialists to act as interpreters.
• Self-Service Analytics & Data Democratisation: IT Is Not Your Reporting Department
  If you have to wait two weeks for every analysis, you end up making decisions based on gut feeling. We establish self-service analytics with clear guidelines: verified data products, training for business departments, and governance in the background. On request, we can provide analytics agents who accept specialist queries in natural language and answer them against the validated semantic layer – so that even staff without BI tool knowledge can find the answers they need. This takes the strain off IT and speeds up decision-making – without uncontrolled proliferation.
• Reporting Modernisation & KPI Frameworks: From a graveyard of figures to a management tool
  Reports that have evolved over time often measure everything – but steer nothing. We modernise your reporting using a KPI framework that links strategy with day-to-day operations: fewer, but more meaningful metrics, complete with context, thresholds and accountability. Instead of interpreting figures, you can take action.
• Planning, Budgeting & Forecasting (FP&A): Planning as a process, not an Excel marathon
  We replace the annual planning battle with integrated FP&A solutions: rolling forecasts, scenarios at the click of a button, and driver-based logic instead of top-down targets. Automated processes handle the legwork – data collection, plausibility checks, forecast runs and scenario comparisons – so that your FP&A teams can focus on assumptions and steering. Finance, Sales and Operations plan using the same data set and can make adjustments during the year, rather than waiting until the next planning cycle.

AI projects rarely fail because of technical issues, but rather because the solution is not utilised in day-to-day operations. We build AI applications that have a clear purpose and deliver a measurable impact on lead times, quality or costs – and that is precisely why they become part of the way we work after just a few weeks.

In doing so, we combine technical depth with industry expertise: from the initial use-case definition through model selection and integration to live operation, we support AI projects in such a way that management, IT and line organisations are equally committed to the end result.

• Agentic AI & Automation: From assisted recommendations to autonomous action
  Agentic systems take on entire task chains – research, decision-making, execution – rather than merely reacting to inputs. We design and implement agents for clearly defined tasks, integrate them into your processes and systems, and set the parameters that allow you to maintain control, traceability and accountability.
• Data Science, Computer Vision & Machine Learning: Turning data into forecasts, classifications and decisions
  We develop ML models for specific use cases: demand forecasting, predictive maintenance, image and document analysis, and anomaly detection. From data preparation through model selection and training to production deployment – including monitoring and retraining to ensure models do not quietly lose their accuracy.
• Generative AI: From chatbot experiments to production-ready applications
  Generative AI only realises its full value when integrated into real-world processes and data sources. We build applications based on LLMs and RAG – from internal knowledge assistants through automated document generation to customer-facing chat interfaces – with the safeguards, evaluation and cost mechanisms that ensure reliable operation.
• AI Infrastructure: A PoC is not a product
  The journey from a working prototype to a robust production environment is the real hurdle. We build the underlying infrastructure: model serving, MLOps and LLMOps pipelines, monitoring, cost control and security. This ensures your AI applications scale reliably – and continue to deliver what they demonstrated in the PoC even after going live.

Platforms, pipelines and models are not projects with a fixed end date, but ongoing operations. We take care of them – with clear SLAs, monitoring throughout the entire data and model lifecycle, incident response and continuous fine-tuning. Routine tasks such as pipeline monitoring, anomaly detection in data streams or model drift detection are handled by automated processes – so that our specialists can step in where it really matters. Technology-agnostic, from a small Power BI setup to an enterprise-wide Data & AI Platform.

• Managed Data & AI Platform Services: Outsource operations, retain control
  We operate your Data & AI Platform – whether in the cloud, on-premises or in a hybrid environment – with clearly defined SLAs, 24/7 monitoring and proactive patch and release management. You retain control over the architecture and data; we ensure the platform runs reliably, securely and cost-effectively. Scaling up and down is included – you pay for what you use, not for what is theoretically possible.
• Data Quality & Pipeline Operations: Pipelines that not only run, but are accurate
  A pipeline that runs through is not the same as a pipeline that delivers the right data. We monitor data streams end-to-end – for completeness, timeliness and plausibility – and respond to disruptions before they reach the dashboard. Agent-based processes detect anomalies in the data stream and suggest corrections; our specialists then make the final decision and intervene. This ensures that data quality remains a managed state, not a random outcome.
• MLOps & LLMOps: Models age – we keep them up to date
  A model that was good six months ago is often worse today, without anyone realising it. We operate ML and LLM applications with everything that goes with them: drift detection, performance monitoring, retraining pipelines, versioning, rollback and cost control for token-intensive LLM workloads. With generative AI, we also carry out response quality evaluation and guardrail monitoring – to ensure that productive operation does not lead to a creeping loss of quality.