// We Build It
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.
Overview
Most of this work already gets done 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. We 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. The whole exchange is 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 on each project's own data, 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, with retrieval-grounded search across your own knowledge base
- Reading invoices, contracts, forms, and applications, and turning what matters into data your systems can actually use
- Writing the first draft of 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: looking things up and acting in your systems through function-calling, with hard limits on what the assistant is allowed to touch
- Running on whichever model fits the job, from ChatGPT (OpenAI), Claude, and Gemini to open-source models hosted inside your environment when data can't leave
- Staying trustworthy after launch, with 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.
Need one of these, or something close to it?
Talk about this service →//How It Starts
01
Tell us what it has to do
A few lines is enough: what it should do, who will use it, and any date you're working to. It's read by the senior engineer who would build it.
02
We ask before we quote
A call about the problem, not a spec sheet: who it's for, what the first version must do, and what can wait for the second.
03
One price, or an honest hourly
Once you've decided to go ahead, you get one fixed price and one timeline when the scope is predictable, and hourly billing when it genuinely isn't.
//Selected Work
AI Assistants
American Sign Language Interpreter Using Machine Learning
Deaf and hard-of-hearing signers still hit a wall in everyday conversations with people who don't know sign language. Reading American Sign Language automatically is genuinely hard: gestures unfold as motion over time, not as single frozen poses, so a system has to understand movement, not just shapes.
Client Confidential
AI Assistants
Object Detection for Inventory Stocking
Counting stock by hand is tedious and easy to get wrong, and the numbers drift out of date the moment someone forgets to log a change. A two-student capstone team set out to test whether a camera and a detection model could keep a running tally of phones in a storage cabinet with nobody touching a clipboard.
Client Confidential
AI Assistants
Marine Highway Traffic Monitoring System with Trajectory Prediction from AIS Data
On busy marine highways, a vessel wandering off its designated route can turn into a collision or grounding fast. Spotting that drift early, across many ships moving at once, is a hard monitoring problem that human operators can't watch continuously.
Client Confidential
A published sample. Most of our work is under client confidentiality and doesn't appear on this site.
// How We Work With You
In writing, before you commit.
If you've brought in outside engineers before, you know where it goes wrong: the senior on the sales call isn't the one doing the work, the code lives in someone else's account, the price moves, and your data is shared before anything is signed. So those four go in writing first.
The engineer who scopes it stays on it
On the first call, you meet the senior engineer who will lead the work, by name. They stay accountable for all of it to the end, including work by any developer we bring in. No handoff to a stranger once the contract is signed.
Your repository, your IP, from day one
You own the code the moment it's written, not when the final invoice clears. It lives in your repository, under your account, documented so any developer can pick it up. No lock-in, and nothing held hostage.
The price is agreed before the work starts
It can be a fixed price, hourly, or a fixed monthly fee, whichever fits the work. You see the number and how it's billed before you commit, and it doesn't change unless you agree to the change first.
NDA before you send us anything
Ask and we'll sign yours, or send ours, before you share a repository, a database, or a business plan. We'd rather do the paperwork first than ask you to trust us with it.
//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. We don't add 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 that's 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.
Tell us what it has to do.
A few lines is enough. Every brief is read by a senior engineer, the same person who would do the work.