What happens when it doesn't know the answer?

It says so, and hands the conversation to a person with everything it already gathered attached, so your customer never repeats themselves. We'd rather it admit a gap than fill it with something plausible, and we set that threshold with you.

How do you stop it from making things up?

It answers from your own material rather than from memory. We ground it in your documents, limit what it's allowed to say, and test it against real questions you've already been asked before it goes anywhere near a customer.

How long before it's actually useful?

The first build is scoped narrow on purpose, so you're usually testing something real in weeks rather than months. We prove it against your own past cases first, then widen what it handles once it's earning its keep.

Does my team have to change how they work?

As little as possible. We put the assistant where the work already happens — your website, your helpdesk, Teams, Slack, the system they're already in — instead of adding one more place to log into. Adoption fails when it's an extra step.

Will our data be used to train someone else's model?

No. We use API tiers and configurations that keep your data out of training, and when it can't leave your environment at all, we deploy open-source models you host yourself.

Which AI models do you use?

Whichever fits the job — usually ChatGPT (OpenAI), Claude, or Gemini where answer quality matters most, and open-source models we self-host when privacy or running cost is the priority. We're not tied to one vendor, and we'll tell you what each choice costs you.

// Problems We Solve

AI Assistants

Your team answers the same questions, retypes the same documents, and drafts the same emails every week. We build AI assistants that do that work instead.

Talk about this service →← All services

Overview

Most of this work is already getting done — just by people, at a cost you've stopped noticing because it's spread thin across everyone's week. An assistant is worth building when a task repeats, has a right answer somewhere in your own material, and eats hours that should go somewhere better. When a task fails that test, we'll say so. A bad fit doesn't get cheaper by adding AI to it.

So we start with your process, not with a demo. We look at how the work actually runs — who touches it, where it stalls, what gets re-done — and pick the one task where an assistant carries real weight and you'd notice if it stopped. That first build is deliberately narrow. Narrow is what makes it provable, and proof is what makes the second one an easy decision.

Then we make it accountable. It answers from your documents instead of guessing, it says it doesn't know rather than inventing something, and it hands off to a person the moment a question stops being routine — with the whole exchange logged, so you can see exactly what it told your customer. Your team stops being the first line and becomes the exception handler, which is the part of the job you hired them for.

We were building this the hard way before you could buy it off a shelf — computer vision, detection, and forecasting models trained from scratch, which is the work you'll see below. That's the difference between wiring up an API and knowing what a model will do when your business hands it something it has never seen.

What We Build

Answering the routine questions your customers and staff ask every day — on your site, in your helpdesk, or inside Teams and Slack
Finding the answer buried in your files so nobody has to remember where it lives — retrieval-grounded search across your own knowledge base
Reading documents and pulling out what matters — invoices, contracts, forms, and applications turned into data your systems can actually use
Writing the first draft — quotes, reports, summaries, and follow-ups built from your own templates and past work, for a person to approve
Taking the next step, not just talking about it — assistants that look things up and act in your systems through function-calling, with hard limits on what they're allowed to touch
Running on whichever model fits the job — ChatGPT (OpenAI), Claude, and Gemini, or open-source models hosted inside your environment when data can't leave
Staying trustworthy after launch — monitoring, logging, and review, so you can see what it answered and correct it

Ideal For

Your team answers the same questions over and over, and every one of them interrupts someone whose time is worth more.
The answer lives in one person's head — when they're out the work waits, and when they leave it's gone for good.
Someone spends their day reading documents and retyping what's inside them into another system.
Every quote, report, and follow-up starts from a blank page, even when most of them say nearly the same thing.

//FAQ

What happens when it doesn't know the answer?: It says so, and hands the conversation to a person with everything it already gathered attached, so your customer never repeats themselves. We'd rather it admit a gap than fill it with something plausible, and we set that threshold with you.
How do you stop it from making things up?: It answers from your own material rather than from memory. We ground it in your documents, limit what it's allowed to say, and test it against real questions you've already been asked before it goes anywhere near a customer.
How long before it's actually useful?: The first build is scoped narrow on purpose, so you're usually testing something real in weeks rather than months. We prove it against your own past cases first, then widen what it handles once it's earning its keep.
Does my team have to change how they work?: As little as possible. We put the assistant where the work already happens — your website, your helpdesk, Teams, Slack, the system they're already in — instead of adding one more place to log into. Adoption fails when it's an extra step.
Will our data be used to train someone else's model?: No. We use API tiers and configurations that keep your data out of training, and when it can't leave your environment at all, we deploy open-source models you host yourself.
Which AI models do you use?: Whichever fits the job — usually ChatGPT (OpenAI), Claude, or Gemini where answer quality matters most, and open-source models we self-host when privacy or running cost is the priority. We're not tied to one vendor, and we'll tell you what each choice costs you.