Document search, invoice processing, customer support and software development. We choose a task, test the benefits and put the solution into production. We adapt deployment and data handling to your requirements.
On a specific task, we compare time, costs and quality before and after introducing AI. We include running costs, output review and corrections, and estimate payback. We recommend next steps based on the results. When AI does not make economic sense, we say so.
Sometimes letting the user select a document is enough. We add automatic search when its benefits justify the cost. We build the more complex version after testing the simpler one.
We design assistants to link to the documents and passages used to prepare an answer. Users can then compare the output with the original source.
We design cloud, on-premises or hybrid deployment based on requirements for data location and processing. The design covers model selection, data usage terms and integration with your identities and permissions (Entra, Google, OneLogin).
We establish access permissions, review of generated code and other outputs, and audit records. We address AI governance requirements from the start. Our subsidiary comma0 provides auditing and oversight.
Search across policies, contracts, manuals and historical document versions. Answers linked to their sources. We build on our own DOC BRO platform, adapted to your environment, as at the Road and Motorway Directorate.
Draft replies for the helpdesk, integration with Salesforce, CRM and ERP, and automation from quote to invoice. Deployed, for example, at Linet.
Extraction, classification, verification and matching of documents; OCR of historical records; integration with records management and accounting systems.
Reports, visualisations, predictions and detection of unusual changes in data. With BI BRO, users can ask questions about data in plain language. We test the benefits of the chosen approach on your data.
A programme for introducing AI into a development team’s work: tool selection, assigning tasks to agents, output review, company instructions and measurement of benefits. Details below.
Assessment of EU AI Act requirements, rules for recording queries and responses, access permissions and measures against prompt injection and data leakage. Cloudfield handles design and implementation; comma0 provides auditing and operational monitoring.
GPU servers in your data centre, model deployment subject to technical and licensing conditions, an AI gateway and cost management for model calls. For organisations that require data processing in their own environment.
Analysis of business goals and the IT environment, task selection and estimated return on investment, and a project plan covering costs, risks and measurement. User training, documentation and support for the deployed solution.
Data warehouses, ETL/ELT, batch and real-time processing, and integration of source systems. Databricks, cloud storage and storage in your own data centre.
Automation of routine business processes and detection of unusual behaviour patterns in data to support the assessment of possible fraud.
We determine deployment, supported models, access permissions and data processing terms based on the specific solution and your organisation’s requirements.
Tools are the starting point. We work with your team to adapt how tasks are assigned, code is reviewed and knowledge is shared. We test the approach on your project and measure its impact. We adjust the pace to what the team can put into daily practice.
Our developers, project managers and marketing team use AI. It helps with code, specification preparation and technical writing. We work with GitHub Copilot and Claude Code, as well as our own AI infrastructure. We draw on experience from these projects when introducing AI to customers.
Buying licences alone does not determine how to work with AI. With the team, we select suitable tasks and establish how to assign them and review outputs. Alongside assisted coding, we test assigning whole tasks to agents, such as modifying a component or preparing tests. A developer remains responsible for evaluating and accepting changes.
We compare tools on tasks from your project. Benefits depend on the type of work, the quality of instructions and the review process; we assess them against the team’s starting point.
An agent needs information about your project. With Superpowers, we prepare sets of instructions describing:
We create the instructions with your team and update them as the product changes and feedback comes in. We test whether they produce usable outputs and reduce manual corrections on specific tasks. Agent outputs continue to be reviewed.
We measure the starting point before introducing tools. After 30, 60 and 90 days, we compare results on comparable tasks. We track four areas:
We select specific measures with the team, such as code review time, defects detected, model costs and time to complete tasks. Our management report compares programme costs with measured benefits. The outcome may also be a recommendation not to expand the use of certain tools or practices.
We adapt the annual programme to team size, existing practices and pilot results. Individual services can also be used separately.
In a full-day workshop, we review the team’s tools and practices and identify what slows development down. We measure the starting point and test working with agents on real tasks in a practical exercise.
Output: a readiness assessment, prioritised proposals for AI use with estimated benefits and a 12-month plan.
We establish rules for access, output review and audit records. We deploy selected tools on a pilot project. Through MCP, we connect agents to agreed systems, such as issue tracking, repositories and CI/CD. We prepare the first set of Superpowers.
Output: a pilot deployment, an AI gateway, company instructions, documented rules and initial measurements.
We connect agents to code and company documentation, such as Confluence, Notion or an internal wiki, using source retrieval to prepare answers (RAG). We first check that the material is up to date. We flag or remove outdated documents and agree how to maintain them. We expand integrations and update instructions based on feedback.
Output: connections to code and documentation, updated instructions and an evaluation of results after 90 days.
We integrate initial AI code review and security scanning into agreed workflows. We use dependency analysis to plan work on technical debt. We prepare material for onboarding new colleagues.
Output: checks integrated into development, an evaluation of return on investment and recommendations for the next year.
We determine scope and price based on the selected areas. We adjust the pace to results and team feedback. We move to the next phase when the team is ready.
We use tools from several providers. We adapt the choice to your environment, data requirements and costs. We assess support for specific features and deployment conditions during design.
Requirements for data location, access and provider use of data inform tool selection. Subject to technical and licensing options, we propose a cloud, on-premises or hybrid deployment.
Cloudfield handles technical design and implementation; see Security. We work with our subsidiary comma0 on audits and monitoring:
For companies with in-house development teams of 10 or more people that:
| Service | What you get | Typical scope |
|---|---|---|
| AI readiness assessment | Assessment of the current state and proposed next steps | 1–2 days |
| AI tool selection | Practical comparison on your code | 1 day |
| Practical training | Developers working with agentic tools | 1–2 days per group |
| Pilot project | Project deployment, metrics and initial Superpowers | 4–6 weeks |
| Annual programme | Four phases with ongoing team support | Approximately 150 person-days per year |
| AI security audit (comma0) | Assessment of security and relevant requirements | 1–2 weeks |
You can start with an initial assessment. We review the current state and propose next steps. The scopes listed are indicative; we agree specific requirements and dates based on your project.
We use AI in our own work and introduce it to customers in logistics, finance and the public sector. We share practices we have tested and evaluate their benefits in your team’s environment.
DOC BRO helps users find information in company documents. Users ask questions in plain language and receive a draft answer linked to source passages. The platform supports document versions and text recognition in scans. Integration with identity management serves to preserve user permissions.
BI BRO lets users ask questions about business data without writing database queries. Agents prepare queries, process results and create visualisations. Outputs support further analysis and decision-making.
We adapt both platforms to the customer’s data, systems and workflows. The design covers deployment, access permissions and output verification.
When designing an AI application, we establish where data is processed, who can access it and how system activity can be traced. Access to documents follows the signed-in user’s permissions. The solution includes rules for recording queries and responses and controlling access to models.
We choose cloud, on-premises or hybrid deployment based on the customer’s requirements. The design specifies the terms of data processing and model use.
Language models can extract information from text for further analysis, such as a message’s topic or sentiment. This information can be combined with structured data to help detect unusual behaviour or prepare recommendations.
For example, an online retailer can supplement purchase records with information from customer communications when developing product recommendations. Historical data should be used to test whether this approach improves results over the existing solution.
We prepare documents for search and connect them to a language model. A user asks a question in the application; the system retrieves relevant passages and uses them to draft an answer.
The answer links to source documents so the user can verify it. Newly uploaded material can also be included in searches. The design covers access permissions and document currency.
Tell us what costs you the most time, money or operational effort. Together, we will choose a first step and a way to measure its benefits.
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