AI Agents and Assistants
What is an AI agent? A plain-English guide for business owners
What an AI agent is, how it works, where it helps a small business, the risks, and a 7-point check to see if you are ready for one.
AI Features & Bots
AI Feature Integration
A defined AI feature gets designed, built and shipped inside your existing app, tested and handed over with the code.
Your product roadmap has an AI feature on it, but your engineers are busy with everything else. We design, build and ship that feature inside your existing web, mobile or internal app, as production code in your own repository. It comes with automated tests, documentation and a technical handoff, so your team can maintain it with confidence. No secrets are stored in the code.
- A defined AI feature shipped inside the app you already run
- Production code, tests and documentation in your own repository
- Configuration kept in your environment, with no secrets in code
$4,900
20–30 working days
Optional maintenance $249/mo
What it is
AI Feature Integration is a software build that adds a defined AI-powered feature to your existing web, mobile or internal app, delivered as production code in your own repository with tests and documentation. It ships with automated tests and a recorded technical handoff.
Why businesses buy it
AI features slip down roadmaps because they need prompt design, careful testing and edge-case handling that core teams rarely have time for. Meanwhile competitors ship theirs. Bringing in a fixed-scope build gets the feature live without pulling engineers off planned work, and you keep the code. The written scope sets how many features or screens, which APIs connect and how the work is tested before you order, so engineering leads can approve it with clear edges.
Problems this solves
The team has an AI feature idea but no capacity to build it
The build adds a defined AI feature to your existing web, mobile or internal app. Up to three user-facing features or screens are delivered as production code in your own repository.
AI prototypes never reach production because nobody has tested them properly
The feature ships with automated tests for core flows and thirty manual test scenarios. The test report shows how it behaves, so your engineers can review it before merging and releasing.
Wiring an AI model into existing APIs takes longer than planned
Connections to up to two APIs are built and tested as part of the work. Prompts and guardrails are configured alongside them, so the feature behaves within limits you agree.
Engineers are wary of inheriting AI code they did not write
Code, configuration and documentation all sit in your repository. A recorded 60-minute technical handoff walks your engineers through it, followed by post-launch support for the first month.
A good fit if
You already have a working app or website and want to add one AI-powered feature to it, not build something new from scratch.
You can describe up to three user-facing features or screens the AI should power, such as a smart search, a summary view or a recommendation panel.
You have a repository and a technical contact who can review code, even if nobody in-house has time to build the feature themselves.
You want the finished code delivered into your own codebase, not locked inside a tool you would have to migrate away from later.
Not the right fit if
Your AI idea is still unproven and you are not sure it will work on your data. See AI Proof-of-Concept Sprint, which tests one use case before a full build.
You need a brand-new standalone internal tool, not a feature added to an app you already run. See AI Internal Tool Builder.
You mainly need visitors answered with questions and answers, not a built feature. See AI Website Chatbot.
The feature belongs inside a browser extension, not your own app. See AI Browser Extension Assistant.
The feature is specifically for a mobile app, not a web or internal tool. See AI Mobile App Assistant.
How it works
- Requirements are confirmed. We agree the up to three features or screens, the APIs involved and what counts as done, based on your repository and design references.
- The feature is built against your codebase's own conventions, using the access and API documentation you provide, with secrets kept in environment configuration, never in code.
- It is connected to up to two APIs, whether that is your own backend, a third-party service, or the AI model itself.
- Automated tests are written for the core AI flows, and 30 manual test scenarios are run to catch what automated tests miss.
- Your technical contact reviews the code and the working feature, with two rounds of changes included to close any gaps.
- The feature is deployed to your environment, with a recorded 60-minute technical handoff covering how it works and how to extend it.
What you provide within 72 hours
Your portal lists these the moment you order. Delivery starts the working day after everything is in and your access is checked.
- Access to the repository the feature should be built into.
- A hosting or cloud account for deploying the finished feature.
- Design assets or reference screens showing how the feature should look and behave.
- API documentation for any systems the feature needs to connect to.
- A technical contact who can review pull requests and answer questions about the codebase.
- Admin access and API keys for every connected system, shared by adding our team as a user or creating a key you control, never by email.
- One decision-maker who can approve the finished feature.
- An API key for your own AI model account (for example OpenAI or Google Gemini), created for this build with a spending limit.
The 72-hour rule. Send everything on your checklist, including access, within 72 hours of ordering so your build starts on time.
Timeline
Start
Requirements confirmed: the up to three features, the APIs involved, and what counts as done.
Weeks 1–2
The feature is built against your codebase, with environment-based configuration and no secrets in code.
Week 3
The up to two APIs are integrated and the feature connected end to end.
Weeks 3–4
Automated tests and 30 manual test scenarios are run against the finished feature.
Week 4
Your technical contact reviews the code, with two rounds of changes to close any gaps.
Go-live
Deployed to your environment, recorded technical handoff held, support starts.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $4,900 |
| Express | 15–25 working days | $5,750 |
| Rush | 10–20 working days | $6,600 |
What you receive at handover
- The AI feature, deployed in your app and delivered as production code in your own repository
- Workflow, prompt and guardrail configuration for up to three features or screens
- Working connections to up to two agreed APIs
- A test report covering automated core-flow tests and thirty manual test scenarios
- An admin guide and the recorded 60-minute technical handoff
- 30 days of post-launch support
Expected benefits and how to measure them
- Can put an AI feature in front of users without pausing your roadmap
- Makes the feature easier to maintain, since tests and documentation come with it
- Feature adoption
- Use your product analytics to count weekly active users of the new feature in the months after release.
- Task completion time
- Time the task the feature supports before release. Repeat the timing with the feature live and compare.
- Defects linked to the feature
- Count issues tagged to the feature in your tracker during the first months after release.
Before you start: Record how users complete the task today and how long it takes. Note current usage of the screen where the feature will sit.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
The build itself is a one-time payment, shown before you buy. Running the finished feature typically costs ~$50–300 a month for hosting and AI usage, on your own cloud account, depending on how much traffic the feature sees.
AI usage during build and testing is billed the same way, so your cloud and AI provider accounts stay in your name throughout.
Ask about running costsCustom requirements
Need more than two APIs or more than three features? Add a note at checkout for a written Change Quote, without affecting your original delivery date. Larger or bespoke projects go through the Custom AI request form instead.
Request a custom versionKeep it running
Monthly maintenance for this build covers monitoring, fixes and change hours for small adjustments, dependency updates and prompt tuning as real users try the feature. Bigger changes go through a Change Quote. Maintenance starts after the included support period and can be cancelled any month.
$249/mo (the minimum fee, includes 2 change hours)
Optional. Starts after the post-launch support period. Cancel any month.
How maintenance worksHow this compares
| Do it yourself with a SaaS tool | Hire a custom agency | Upmark Labs | |
|---|---|---|---|
| Price | A no-code AI plugin charges a recurring fee and limits what it can touch | A software agency quotes after a discovery and scoping phase | Ours is one fixed price, shown before you buy |
| Code ownership | A plugin's logic stays inside its own platform, not your codebase | An agency's ownership terms vary by contract | Ours ships as production code in your own repository |
| Delivery | A plugin installs quickly but is limited to what it was built to do | An agency usually takes several weeks, set per project | Ours has a fixed delivery time shown before you buy, with faster options |
| Testing | A plugin is tested by its vendor, not against your specific app | An agency build depends on what was agreed | Ours ships with automated tests plus 30 manual test scenarios |
| Fit | A plugin bends your app to fit its own patterns | An agency build depends on what was agreed | Ours is built to your codebase's own conventions |
Questions about this product
What does the AI Feature Integration price include?
One fixed price covers designing, building and shipping up to three AI-powered features or screens into your existing app, with automated tests, 30 manual test scenarios and a recorded 60-minute technical handoff. The price is shown before you order, with no separate discovery fee and no open-ended billing.
What does the finished feature cost to run each month?
Typically $50 to $300 a month for hosting and AI usage on your own cloud account, depending on the feature's traffic. A feature used by a moderate number of daily users usually sits mid-range, and every charge appears directly on your own cloud bill.
How long until the feature ships to my app?
The delivery time for standard, Express and Rush is shown in the price panel before you order. The schedule runs from a complete intake, so it depends on receiving repository access, design references and API documentation within 72 hours of ordering. Late information adds delivery time.
What access do you need to my codebase?
Repository access, a hosting or cloud account for deployment, and API documentation for any systems the feature connects to. Credentials are shared by adding our team as a user or creating a key you control, never sent by email, and are used only to build and deploy the agreed feature.
Do we own the code once the feature is delivered?
Yes. The feature is delivered as production code inside your own repository, following your codebase's existing conventions, with no logic locked inside a separate platform you would need to migrate away from later. The repository, the deployment and every provider account involved stay in your name throughout.
Can changes be requested after the feature review?
Yes. Two rounds of changes are included as your technical contact reviews the working feature and the code. After launch, small fixes or adjustments fit inside the monthly change hours included with maintenance. That covers dependency updates and prompt tuning as real users try the feature. Bigger changes go through a Change Quote first.
How is the AI feature tested before it ships?
With automated tests covering the core AI flows plus 30 manual test scenarios run against the finished feature in your own environment before deployment. You receive the full test results ahead of launch, and two rounds of changes are included if anything still needs adjusting afterwards.
Guides on this topic
All guides Add the AI feature without rebuilding your app.
Order AI Feature Integration and the finished feature ships to your repository, tested, documented and handed over so your engineers can own it from day one.
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