AI features that do real work.
Estimating and quoting tools trained on your numbers. Document processing. Chat over your own files with citations. Agents with a human in the loop. Built into software your team already opens.
| Delivers | Use-case scoping and a data audit · designed interface · evaluation set from your real cases · human-in-the-loop review step · monitoring and a monthly accuracy report |
|---|---|
| Timeline | 3–10 weeks |
| From | $8,000 |
| Data | Stays in your accounts; not used to train models |
| Models | Claude for most work; chosen per task; we tell you what each feature runs on |
Vector UX builds AI features into the software small companies run on: chat over documents with citations, document-processing pipelines, and estimating engines trained on historical job costs. Every feature ships with an evaluation set, shows its sources, and routes low-confidence cases to a person. AI features start at $8,000 and take 3–10 weeks.
AI inside your software
| Scenario | Example | Range | Timeline |
|---|---|---|---|
| Chat over your documents with citations | Your manuals, contracts, and past jobs answer questions and show where the answer came from. | $8,000–$18,000 | 3–5 weeks |
| Document processing pipeline | Invoices, applications, or specs read, extracted, checked, and routed, with a person approving exceptions. | $10,000–$25,000 | 4–8 weeks |
| Estimating engine trained on your numbers | Historical job costs become a model that prices the next job in minutes, with the math shown. | $15,000–$35,000 | 6–10 weeks |
What moves the number: integration with a system you already use (+$2,500) · accuracy evaluation set (+$1,500) · review queue for a person (+$2,500). Try the estimator →
How we build AI-native
The other half of the story is how we work. We build with Claude Code, which means the typing is fast and the weeks go to design and testing. Every AI feature gets an evaluation set before launch, so the accuracy is a number you can see, not a promise.
- An evaluation set from your real cases, run before every release
- Sources shown on every answer
- Low-confidence outputs routed to a person
- A monthly accuracy report
Live proof on this site
The scoping assistant in the estimator turns a sentence into a scenario and a price range. The website audit reads a page and writes a plain-English readout. Both run the way we would build them for you: constrained to real data, sources shown, and a person at the end.
How it runs
Asked before
How much does an AI feature cost?
How accurate is it?
What happens when the model is wrong?
Is our data used to train models?
Which models do you use?
Tell us what you're building.
30 minutes, no deck. You'll leave with a scenario and a price range.