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Apps and Engineering AI
Upmark Labs builds AI for engineering and product teams: AI features and assistants inside your apps and chat tools, agents that review code, generate tests and triage incidents, and tools that turn feedback into themes and specs. Each build has a fixed scope and price, written before you buy. Order online, or talk to an expert first.
What problems does Apps and Engineering AI solve?
Engineering and product teams lose time to work that repeats: answering the same questions, reviewing routine changes, writing tests and docs after the fact, and sorting feedback by hand. The products in this area take on that work and leave decisions with your engineers and product managers.
If any of these sound familiar, this is the right area to start in:
- Customers or colleagues ask the same questions on your website, in Slack or in Teams, and someone answers each one by hand.
- You want an AI feature inside your existing app, but your team has no time to design and ship it.
- Pull requests wait for review, and bugs, security issues and breaks from your coding standards still get through.
- Test coverage trails the code, and dependency upgrades pile up because nobody has time to test them.
- When a pipeline fails or alerts fire, the on-call engineer digs through logs and runbooks before anyone can act.
- Feedback, feature requests and interview notes sit in different tools, and docs and release notes fall behind what has shipped.
What does Upmark Labs build for engineering and product teams?
The work falls into three groups. App builds add AI to your website, your product, your internal tools or your team chat, with production-grade code delivered to your repository and no secrets in the code. Developer and DevOps builds run inside your existing Git and CI workflow and post their findings as comments or tickets your team reviews. Product management builds group feedback and usage data into clear themes and write in your own document and ticket formats, with a product manager reviewing before anything reaches the roadmap.
Typical connections include GitHub, GitLab, Bitbucket, Jira, Linear, Slack, Microsoft Teams, Notion, Productboard, Intercom, Zendesk, your own REST or GraphQL APIs, and cloud accounts on AWS, GCP or Azure. Each product page lists exactly which systems its scope covers.
- AI Feature Integration: designs and ships an AI-powered feature inside your existing app.
- AI Slack Bot and AI Microsoft Teams Bot: answer questions and run actions for your team where it already works.
- AI Code Review Agent: reviews pull requests for bugs, security issues and standards, and comments inline.
- AI Test Generation: generates unit and integration tests for your existing code.
- AI Incident Triage: groups alerts, finds likely causes and drafts incident updates.
- AI Feedback Synthesis: groups product feedback from your channels into ranked themes.
How does an apps and engineering project run?
The scope is written before you pay. Each product page lists the application, repository or pipeline we work in, the systems we connect, the features or workflows we build, the testing we run, the rounds of changes you get and the support that follows. The price and the fixed delivery time are shown on each product, so both are known before you order.
Running costs sit in your own accounts: AI model usage, hosting and CI minutes, plus the Git host, tracker or chat plans you already pay for. You keep every provider relationship, and nothing runs through us.
- Intake: for developer tools you share scoped repository access through an app or token, your CI/CD details, coding standards or runbooks, and access to your tracker or chat. App builds also need your hosting or cloud account, design references and API documentation.
- Build: we write the code or configure the workflow, prompts and guardrails, and connect the agreed systems.
- Testing: developer tools run a trial on pull requests or pipeline runs, and app builds get automated tests for core flows plus manual test scenarios. You receive the test report.
- Review: you check the results and request changes within the agreed rounds.
- Launch and support: we deploy to your environment and hand over an admin guide and a recorded handoff session, followed by a period of post-launch support.
How do you choose the right apps and engineering product?
Start with where the help is needed. For answers in a channel, look at the AI Website Chatbot, the AI Custom Team Assistant, or the Slack and Teams bots. For AI inside software you own, choose AI Feature Integration for an existing app, the AI Internal Tool Builder for a new internal web tool, the AI Mobile App Assistant for an iOS or Android app, or the AI Browser Extension Assistant for help inside the pages your team already uses.
For engineering work, match the product to the step that slows you down. Code quality: the AI Code Review Agent, AI Test Generation and the AI QA Agent. Upkeep: the AI Dependency Update Agent. Operations: the AI DevOps Agent, AI Incident Triage, AI Log Analysis and the AI On-Call Assistant. Documentation: the AI Documentation Agent, AI API Documentation and the AI Changelog Generator. For product work, AI Feature Request Triage and AI User Research Synthesis sort requests and interviews, the AI PRD Generator drafts requirement documents, the AI Roadmap Assistant explains trade-offs to stakeholders, and the AI Product Manager Agent drafts specs and maintains the backlog.
If your tools are already chosen, check for an edition built for them. The AI Code Review Agent and AI DevOps Agent come in editions for GitHub, GitLab and Bitbucket, the AI QA Agent for GitHub and GitLab, AI Incident Triage for Jira, Slack and GitHub, and the AI Slack Bot for Jira, Notion and Google Workspace. The AI Website Chatbot has industry editions for sectors such as SaaS, real estate and legal operations. If nothing fits, book a discovery call and an expert will scope it with you.
Apps and Engineering AI in the Store
AI Browser Extension Assistant
Builds a browser extension that brings AI help into the pages your team uses.
AI QA Agent
Runs structured test plans against your application and reports defects with evidence.
AI Mobile App Assistant
Adds a conversational AI assistant to your iOS or Android app.
AI Dependency Update Agent
Proposes and tests dependency upgrades as reviewable pull requests.
AI Documentation Agent
Generates and maintains technical documentation from code and specs.
Browse the Store by category: Development Developer & DevOps Product Management
Questions buyers ask
Will an engineering agent change our code or production systems on its own?
No. Developer and DevOps products post their findings as comments or tickets for your team to review, and they make no production changes without human approval. The AI Dependency Update Agent proposes and tests upgrades as pull requests that your engineers review and merge. The AI DevOps Agent diagnoses failures and proposes fixes, and a person decides whether to apply them.
Where does the code live, and who controls it?
App builds such as AI Feature Integration and the AI Internal Tool Builder deliver production-grade code to your repository and deploy it to your environment, on your hosting or cloud account. Configuration is environment-based, so no secrets sit in the code. AI usage is billed to your own provider accounts, so the code, the hosting and the provider relationships are yours to run after handoff.
Which Git hosts, trackers and tools can these builds connect to?
Developer tools typically connect to GitHub, GitLab or Bitbucket, a tracker such as Jira or Linear, Slack, and cloud accounts on AWS, GCP or Azure. Product management builds work with Jira, Linear, Productboard, Notion, Intercom, Zendesk and Slack. App builds connect to your web app or site and your REST or GraphQL APIs. Each product page lists the systems included in its scope.
What do we need to provide before the build starts?
For developer tools: scoped repository access through an app or token, CI/CD configuration details, coding standards or runbooks, and access to your tracker or chat. App builds also need your hosting or cloud account, design references, API documentation and a technical contact for code review. Feedback and roadmap builds need read-only access to each data source and your metric definitions. Late intake items move the delivery date.
What does an apps and engineering build cost to run after launch?
The build price is paid once. After launch, running costs for developer tools are mainly AI model usage and CI minutes, and app builds add hosting on your own cloud account. Providers bill these to you directly, on top of the Git host, tracker or chat plans you already have. Each product page shows a running cost estimate, and an optional maintenance plan bills monthly and can be cancelled any month.
What if we need more repositories, systems or languages than the scope covers?
Code review, testing and operations tools are scoped to one repository or pipeline and one language or stack, and app and chat builds to the features, channels and systems listed on each product. Extra repositories, systems or workflows are available as add-ons. For anything else, add a custom requirements note at checkout and you receive a written Change Quote; that work starts only after you accept it. Larger projects go through the Custom AI enquiry form.
Not sure whether to start in the app, the pipeline or the backlog?
Read the full scope of any engineering or product build in the Store before you order, or book a discovery call and decide with an expert where to begin.