--- title: "Build the missing piece" description: "When configuration and integrations are not enough, we build the focused tool or application the workflow needs, then help deploy and maintain it." url: https://lidvizion.ai/services/custom-software/ site: Lid Vizion index: https://lidvizion.ai/llms.txt --- [Services](https://lidvizion.ai/services/) · 04 Build the missing piece # The software you need. Not a rebuild for its own sake. When configuration and integrations are not enough, we build the focused tool or application the workflow needs, then help deploy and maintain it. [Let’s map your workflow](https://lidvizion.ai/contact/) · [All services](https://lidvizion.ai/services/) Illustrative workflow A purpose-built operations interface that brings your data, approved actions, and exception queue together. The exact implementation depends on your tools, access, constraints, and definition of success. What we work on ## Deliverables you can hold us to. 1. Requirements and build-versus-configure decision 2. Custom application or API development 3. AWS deployment and data architecture 4. Tests, documentation and operational handoff A good fit when ## You’ll recognise the problem. - Configuration and connectors have reached a real limit. - The workflow needs its own interface, data model or API. - It has to be deployed, tested and maintained properly. Examples ## Where it shows up. Illustrative workflow patterns, not results from a specific customer. Build the missing piece ### Operations console One interface for your data, approved actions and the exception queue. Build the missing piece ### Internal API A small service that gives AI workflows safe, structured access to a system. Build the missing piece ### Review tool A focused tool for people to check, correct and approve AI-prepared work. How it runs ## Start with the operation. We map the work, establish the boundaries, and choose the smallest useful implementation. 1. 01 / Map The trigger, the context, the handoffs and the owner. 2. 02 / Bound What the workflow may read, change and must escalate. 3. 03 / Implement The smallest configuration, connection or build that works. 4. 04 / Measure Completed work, review effort, exceptions and cost. What you leave with ## Something your team can run. Every engagement ends with a handoff, not a dependency. - A written build-versus-configure decision - The application or API, tested and documented - Deployment on your infrastructure or AWS - An operational handoff and maintenance plan Use cases ## Where teams start. Illustrative workflow patterns for this service. [All use cases](https://lidvizion.ai/use-cases/) - [Invoice and payment matching](https://lidvizion.ai/use-cases/finance-reconciliation/): Finance Match invoices, receipts and payments across systems, and hand a person the short list that does not reconcile. - [Movement and activity analysis from video](https://lidvizion.ai/use-cases/video-movement-analysis/): Sports & field operations Count repetitions, score form or track activity from ordinary video, with results a coach or supervisor can check. - [Parking and space occupancy from cameras](https://lidvizion.ai/use-cases/parking-and-space-occupancy/): Facilities & operations Use existing cameras to count which spaces are free or occupied and feed the count to signage, an app or a dashboard, with people checking anything unusual. Field guides ## Run it yourself first. Free, ungated playbooks for this kind of work. [All guides](https://lidvizion.ai/guides/) - [Run a computer vision proof of concept](https://lidvizion.ai/guides/run-a-computer-vision-proof-of-concept/): A four-step playbook to test whether computer vision can support a real decision, using your own images and a clear pass-or-fail test agreed up front. - [Decide whether to build, buy or configure](https://lidvizion.ai/guides/decide-build-buy-or-configure/): A four-step method to decide whether a need is best met by configuring tools you have, buying a product, or building custom software, before any money is committed. - [Use your existing cameras for computer vision](https://lidvizion.ai/guides/use-existing-cameras-for-computer-vision/): A four-step playbook to test whether the cameras you already have can answer a counting, occupancy or safety question before you buy any new hardware. Direct answers ## Questions buyers ask first. [All answers](https://lidvizion.ai/answers/) - [When should a company build custom AI software instead of using existing tools?](https://lidvizion.ai/answers/when-should-a-company-build-custom-ai-software/): Build custom AI software when a valuable workflow still has a material gap after existing products, connectors, APIs, configuration, and automation options have been evaluated. Custom software should solve a specific operating constraint rather than duplicate capabilities already available in the AI ecosystem. - [What can computer vision do for a business?](https://lidvizion.ai/answers/what-can-computer-vision-do-for-a-business/): Computer vision turns images and video a business already captures into structured signals: counting and detecting objects, checking quality, reading documents, measuring movement or spotting changes. It is most useful when a person currently watches footage or inspects images by eye, and a decision follows from what they see. - [What drives the cost of an AI or automation project?](https://lidvizion.ai/answers/what-drives-the-cost-of-an-ai-project/): The cost of an AI project is driven less by the model and more by the work around it: how many systems it must connect to, how clean and reachable the data is, how much human review and exception handling it needs, the security and compliance requirements, and who will run and maintain it afterwards. Before you build ## Test whether custom software is actually the next step. Work through configuration, connection, automation, and the remaining capability gap before creating another system to own. [Use the build vs. buy assessment](https://lidvizion.ai/tools/ai-build-vs-buy-assessment/) Questions ## Common questions. ### Why not build first? Configuration is faster to change. We build when existing tools reach a real limit, not by default. ### Who owns the code? Ownership and hosting are agreed up front, and everything is documented for handoff. ### Can you maintain it? Maintenance can be part of the engagement. Deliverables include documentation, tests and an operational handoff. Related ## Other ways in. [See all services](https://lidvizion.ai/services/) - [Configure your AI](https://lidvizion.ai/services/ai-setup/): Turn individual AI use into a repeatable way of working. - [Connect your tools](https://lidvizion.ai/services/integrations/): Put the right context within reach of the workflow. - [Automate the repeatable](https://lidvizion.ai/services/automation/): Give recurring work an owner, a trigger, and a stop rule. Bring the messy version ## One workflow. A better way to run it. Show us the tools, the handoffs, and the part that keeps slowing your team down. [Let’s map your workflow](https://lidvizion.ai/contact/) · [Read our approach](https://lidvizion.ai/approach/)