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AI features inside your product
AI where it actually helps: one clear job inside your product, such as answering from your own documents or searching the way people talk, built to say when it doesn't know.
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The AI features worth building do one clear job inside your product, for people who already use it. ShipFastDev adds them to web platforms and apps: answers drawn from your own documents, search that understands what people mean, assistants that look things up in your data, and forms or messages turned into structured records. Each one starts small, is tested on real examples, and is built to say when it doesn't know.
Examples of AI features
- Answer from your own documents: policies, manuals, product sheets. The answer points to where it came from, and says so when the documents don't cover the question.
- Search that understands: customers type the way they talk ("a quiet laptop for travel") and still find the right products, even when the words don't match.
- An assistant on your data: questions about orders, bookings or stock, answered by running real queries rather than guessing.
- Turn text into records: an e-mail, a form or a message becomes a draft order, a ticket or a quote for someone on your team to check and send.
An example: JARVIS in SkyEye
SkyEye, ShipFastDev's in-house 3D data platform, has JARVIS, a chat and voice assistant. You ask how busy an airport is right now, or to follow the highest plane over a city, and it answers in a sentence or two while flying the camera to what it names. Its tools are generated from each data layer's field schema, so a new dataset becomes searchable by the assistant without new tool code. The model asks for a tool, the server runs the query on live data, and the result goes back to the model. SkyEye isn't public; a demo is available on a call.
How it's built
- One job, measured. Pick the task, collect real examples of questions or inputs, and agree what a good answer looks like before anything is built.
- Grounded in your data. The model works from your documents or your database through tools, not from memory, so answers can be checked.
- A person stays in charge of anything that changes data or reaches a customer: the AI drafts, your team approves.
- Costs and privacy up front. Which provider sees which data, what each request costs, and what's stored, decided before launch.
- Released weekly like any other feature, with notes, so you see it improve on real use.
The article on adding AI features to your product covers the questions to ask first, and the one on the security risks of AI-generated code matters if AI tools also wrote part of your code.
Always included
- You own the code from day one, in your own repository.
- Release notes in plain language after every release.
- You work directly with Adil; trusted specialists join when a project needs them.
Frequently asked questions
Will it make things up?
Language models can be wrong, so the design limits it: answers come from your documents or data, show their source, and the system is told to say it doesn't know rather than guess. It's tested on your real examples before launch.
Which AI model do you use?
The one that fits the job, its cost and your privacy needs. The choice is made with you and written down.
Can AI be added to the product I already have?
Usually yes. Most features plug into your existing backend through an API, without rebuilding the product around them.
What does it cost to run?
Each request to a model has a price, so the running cost is estimated from your expected usage before launch, alongside the cost of building it.