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# Engineering · VANDAM > Build custom applications, automations and AI agents with the data, testing and operating controls they need. We develop the software, check its behavior and prepare your team to run it. URL: https://vandam.ai/approach/engineering Contact: hendrik@vibelab.be [Strategy](https://vandam.ai/approach/strategy) [Implementation](https://vandam.ai/approach/implementation) [Engineering](https://vandam.ai/approach/engineering) [Education](https://vandam.ai/approach/education) # Software built around your business. Build custom applications, automations and AI agents with the data, testing and operating controls they need. We develop the software, check its behavior and prepare your team to run it. [Talk about what you need to build](mailto:hendrik@vibelab.be?subject=VANDAM%20/%20Engineering) ## Software you can operate and improve. - **Working software** The interface, logic and system connections needed for the agreed task. - **A quality record** Data preparation decisions, repeatable behavior checks and the results used to assess release readiness. - **An operating handover** Code, configuration and documentation, with access, hosting, maintenance and support responsibilities agreed. ## We build for the process you want to run. At [Anthropic](https://www.anthropic.com/research/employment-at-anthropic), agents worked **six times** as many hours as their human handlers by August 2026. An agent is an AI assistant allowed to use connected tools to carry out tasks. That much output only helps a business when the software around the model fits the work: your information, the connections to your systems, the access rules and a clear point where a person checks the result. > The best way to get real efficiency gains is you have to actually reengineer the process to take advantage of what agents are actually good at and can do differently and faster than people. > > Aaron Levie, CEO, Box, September 2026, [Source](https://x.com/levie/status/2096279756925026733) So we agree the process first, then build for it. We define acceptable behavior with your team and check it throughout development: the information the system relies on, the actions it may take and what it does when it cannot finish a task reliably. ## How we build it. 1. Define what good means: Your team brings the requirements, source systems and records of actual work. We agree the expected results, permitted actions and acceptance criteria before building. 2. Build and review: We develop working versions and review them with your team. You try the task, inspect the output and help resolve the business decisions that emerge during development. 3. Release and maintain: We review the checks before release and prepare the operating handover. Ongoing maintenance has an agreed scope, including who responds to failures and approves changes. ## How we check quality. The checks become part of the system, so quality can be reassessed when its data, instructions or software change. ### Data preparation and access We identify the authoritative sources, organize the agreed data and check for missing, conflicting or outdated records. We define how information is retrieved and refreshed, while preserving who is allowed to see it. Data owners help resolve ambiguities before those ambiguities reach users. ### Evaluations An evaluation is a repeatable check of whether the system behaves as intended. We assemble a test set from actual tasks, with expected results or review criteria agreed by your team. It covers output accuracy, supporting sources, permissions and incomplete inputs. Disagreements help identify what needs changing. ### Quality after release For systems we maintain, we rerun the checks when models, instructions, data or code change. This catches updates that weaken earlier results. We monitor failures, response times and usage costs. When your team operates the system, we hand over these checks with the recovery procedures. ## Services for this step ### MCP Retrofit Connect your product to Claude, ChatGPT and other MCP clients **$12,800** two weeks · plus hosting ### Adversarial App Audit Verified security findings with code fixes **$4,800** one week ### Local Law 144 Audit Bias audits for hiring tools covered by NYC Local Law 144 **$8,000** annual · repeats every year ### Agent Attack Audit Red-team your agents and their connections **$6,400** two weeks ### Eval Suite Test every new model on your own work **$12,800** three weeks ### Private AI Run sensitive steps on a model you control **$16,000** three weeks · plus hardware or hosting at cost ### Source Cleanup Clean the data your agents will read **Start with a conversation** One email to Hendrik, then a price for the service that fits, agreed before we start.