AI agents and features for your product, tested before launch
We build AI agents and features into your product, website, or app. You approve the content they draw on, and we test them on your real questions before anyone relies on them.
AI inside the products your customers and staff use
We build AI into what your customers and staff use every day. It might be an agent that answers questions on your website, an assistant inside your app, or a feature in the product you sell.
People can check what it tells them, because answers drawn from your content show their sources. When a question goes past what it knows, it is built to say so and bring in someone from your team.
- Who it’s for
- You are building a product around AI, and its answers have to hold up before customers rely on them.
- Your customers ask the same questions every day, and the answers are already in your own content.
- You want to add an AI feature to an app or a product you already run.
- You run an agency, and a client needs an AI agent or feature built to the scope you agreed with them.
The agent or feature, and the tests that come with it
- A written scope that says what the agent or feature does and what a person still checks
- A working agent or AI feature, built into your product, website, or app
- A test set made from your real questions and cases, run before launch and after each change
- Answers drawn from content you approve, with sources shown and a handoff to a person
- A written guide and training for the people who will run it
A test set that runs from the first build to each later change
Scope
We agree with you what it will do, which content it may draw on, and where a person still checks its work. You approve the written scope and its fixed price before the build starts.
Gather the content
We collect what it needs to know, such as your help pages, documents, and product details, and you sign off on each source.
Write the test set
We turn your real questions and cases into a test set. It includes cases built to trip it up, such as a push for a price or a message that tries to change its instructions.
Build
We build it in short rounds, running the test set as we go. You try each working version as it comes together.
Launch and train
We read the test results with you before it goes live, and we train the people who will run it.
Test every change
After launch, the test set runs again after each change we make, so an update is checked against the same cases before it ships.
How to start and what it costs
After a free call, AI work starts with a Workflow Review: about two weeks for $2,500, credited toward the first project if you go ahead within 60 days. It ends with a map of how the job runs today and a fixed‑price scope for your first project.
AI automation or agent project: From $7,500. Typically 3 to 6 weeks.
Example: a 25‑person heating and cooling company
A website assistant that answers after‑hours questions and passes quote requests to the office.
Typically $7,500 to $12,000, about 4 weeks.
Your data and what you own
- Is our data used to train AI models?
- No. We don’t use it to train AI models, and we use AI providers only under business or API terms, or account settings, that exclude training on it.
- Where is our data stored?
- In your own accounts and tools where possible, otherwise in cloud services we set up for your project and name for you before work starts.
- What happens to our data when the work ends?
- We hand over or delete what we hold, as you choose, and remove our access to your systems.
- Who owns the code you write for us?
- You own what we write for you; we keep our own pre‑existing components. What you own at the end, such as code, accounts, and content, is agreed in writing for each engagement before work starts.
See it in practice
Website knowledge agent
Read the case study on Website knowledge agentA chat‑first version of a company website. An AI agent answers questions about the firm from approved content and broader Salesforce questions from its own knowledge, gauges whether the visitor is a good fit, and offers a booking calendar.
- Includes
- Firm answers from approved content; Guardrail test battery; Fit scoring; Booking hand‑off
Website rebuild engine
Read the case study on Website rebuild engineA web app that runs a complete website for a local business on its own domain. Our team builds each site from the owner’s answers and the business’s details, and after launch Claude, Anthropic’s AI model, turns the owner’s plain‑English change requests into small content changes, with anything risky checked by a person first.
- Includes
- Sites built from a business’s own details; Plain‑English change requests; A person checks risky changes; Hosting on the business’s own domain
- Tools on these builds
- Anthropic Claude, TypeScript, Next.js, React, Zod, Supabase (Postgres), Stripe, Slack, Vitest
Questions about AI Product Development
Have you built AI agents before?
Stratyx Ventures has built AI agents and features for clients and for its own products, including both builds shown on this page. The website knowledge agent answers questions about a firm from content the firm approved. It was checked against scripted test conversations, including attempts to pry out prices, client names, and its hidden instructions.
How do you keep an agent from making things up?
Stratyx Ventures builds agents that take facts about your business only from content you approve, and that show the source of each of those facts. Before launch, we test each one on questions it should not answer.
Can you add AI to a product we already have?
Yes. Stratyx Ventures builds agents and AI features into products, websites, and apps you already run, as well as new ones. We look at how your product is built before we scope the work.
Which AI models do you use?
Stratyx Ventures chooses the AI provider for each project from Anthropic, OpenAI, Google (Gemini), xAI (Grok), and ElevenLabs. Both builds on this page use Claude, Anthropic’s AI model.
Share the questions your customers ask most
Those questions become the first cases in the test set, and we tell you what content an agent would need to answer them.