AI Development: Custom Features Built On Your Stack
AI-powered search, a chat interface, a content generator built to your brand voice, custom AI features built into your actual website, not a bolted-on third-party widget.
See sample deliverables →AI Development covers custom AI-powered features built directly into your website: AI-powered search, a chat interface tuned to your content, a content or recommendation generator, or another custom application of an AI model API. We build it natively into your stack, using your own API keys and account, so you own the feature and control the cost, rather than depending on a third-party widget you cannot fully control.
Why a bolted-on AI widget usually disappoints.
Generic AI chat widgets are fast to install and hard to customize. A feature built for your actual content performs differently.
Tuned to your content and brand voice
Token usage monitored, not left unchecked
Built-in guardrails against runaway spend
Your API keys, your data, your control
Scope and cost depend on the complexity of the feature and which underlying AI model API is used.
What ships when you engage an AI feature build.
One-time build per feature. You own the code and API account when we hand it off.
Priced per feature. Complexity varies significantly depending on the model, data, and interface involved.
A feature built into your site, and controlled.
Six concrete deliverables ship on every AI development engagement.
Defining exactly what the AI feature needs to do and which model API best fits the use case.
The actual working feature, built natively into your site, not a third-party embedded widget.
Prompts tuned specifically to your content, brand voice, and the use case, not a generic default.
Rate limiting and usage monitoring built in from the start to prevent runaway token spend.
Testing against real, messy inputs, not just clean demo data, before go-live.
Written documentation covering the build, the API account, and how to maintain it.
We scope the feature. Then we build and cost-control it.
AI features built without cost controls are a common source of surprise bills once real usage kicks in.
What the feature actually needs to do, and what it should cost.
Before building anything, we define the feature scope, select the right model API, and set realistic cost expectations.
- 1Use-case definition: what the feature actually needs to do.
- 2Model selection: which AI API fits the use case and budget.
- 3Cost modeling: expected token usage and monthly cost range.
- 4Rate-limiting strategy defined before any code is written.
- 5Delivered as a written scope document before build starts.
Our team builds, tunes, and tests the working feature.
Once the scope is signed off, we build the feature into your site, tune the prompts, and test against real inputs before launch.
- 1Feature built natively into your site against the signed scope.
- 2Prompts tuned specifically to your content and brand voice.
- 3Rate limiting and cost controls configured before go-live.
- 4Testing run against real, messy inputs, not clean demo data.
- 5Written documentation handed to a named owner on your team.
Bundling multiple services? Request a custom proposal →
Three guarantees on every AI feature build.
If we miss any of them, we fix it at no additional charge.
The code, API account, and credentials are yours at handoff, built on your own accounts, not gated behind an MV3-owned service.
Rate limiting and usage monitoring are built in from the start, not added reactively after a surprise bill.
The feature is tested against real, messy inputs before go-live, not just the clean examples used in a demo.
What a real AI feature build actually looks like.
A sanitized view of the kind of build checklist your team receives at handoff.
AI feature build status
Every feature is tracked through scoping, build, and real-input testing before we consider it complete.
- Use case and model selection confirmed
- Feature built natively into your site
- Prompts tuned to your content and brand voice
- Rate limiting and cost controls configured
- Real-input testing completed before go-live
How we build every custom AI feature.
Five stages. Same protocol every engagement.
Use-case definition and model selection against your budget.
Prompt design and cost modeling, signed off in writing.
The feature built natively into your site.
Real-input testing before the feature goes live.
Written documentation delivered to a named owner on your team.
What teams report after a custom-built AI feature replaces a generic widget.
Illustrative composites built from real engagement patterns, not verified individual clients. Used to make the working style concrete, not to claim a specific outcome for a specific company.
A generic chat widget we tried before had no idea about our actual product. MV3’s version is tuned to our real documentation, and the difference in answer quality is obvious.
We were nervous about an unpredictable AI bill. Rate limiting built in from the start kept costs exactly where we expected.
Owning our own API account instead of depending on a vendor’s black-box service gave our engineering team real confidence in the build.
Illustrative composite personas. Not verified statements from a named, specific client.
One-time implementation fee per feature. No ongoing management fee unless you add a maintenance retainer separately.
- Feature built natively into your site, not a bolted-on widget
- Model API selected against your specific use case and budget
- Prompts tuned to your content and brand voice
- Rate limiting and cost controls built in from the start
- Testing against real, messy inputs before go-live
- You own the code and API account at handoff
Free scoping call. Written implementation quote inside 3 business days. You own the feature at handoff.
This is not for you if…
We hold a fit bar on this service. If any of the below is true, we'll say so on the call.
- An existing plugin or tool already does what you need well. We will tell you that honestly rather than building something custom you do not need.
- You want unlimited usage with no cost controls. We build in rate limiting by design to prevent runaway spend; if you specifically do not want that, we are not a fit.
- The feature requires training a custom model from scratch. This service covers building features on top of existing AI model APIs, not training foundation models.
- You have no budget clarity for ongoing API usage costs. Token costs are billed by the model provider directly; we need a realistic sense of your usage tolerance before scoping.
- You want this done with no one on your team able to maintain it. We document thoroughly, but we still recommend a named technical owner on your side.
If you’re a fit, keep scrolling. Or book the call now →
Questions buyers ask us.
Oversees MV3’s AI development builds. Every feature scope is reviewed by Vance before it reaches a client.
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