AI that earns its place in your stack.
Most agencies talk about AI. We build products with it and run our own shop on them. That is the only reason we are willing to point it at yours.
Pick the one that sounds like you.
What we actually build with AI.
Finding the repeated manual steps in your operation and removing them. This is where AI pays for itself first, and where we look before anything else.
Chat trained on your own content and documentation, answering from what you actually published rather than inventing an answer.
from your docs
source shown
Search that returns answers instead of a list of blue links, built on your catalog or your knowledge base.
Drafting articles, product copy and translations with a person editing before anything publishes. The draft is the time saved; a person still edits and approves it.
Recognizing, describing and classifying images at a volume no team could review by hand. This is what we built for FraudSniffr.
Supervised learning on your own data where the pattern is specific to your business and no off-the-shelf model knows it.
patched 12
scanned daily
WAF on
Checking documents, submissions or communications against your own rules, at a volume and consistency a review team cannot hold all day. A person still signs off.
Pulling the numbers together and writing the summary, so the monthly report stops eating a day and starts arriving on time.
Straight answers.
What do you build it on?
Python and AWS for the most part, against whichever model suits the job. We are not tied to one vendor, which matters because the price and the capability of these models change every few months.
We already have automation. Do we need AI as well?
Often not. Automation handles the steps you can write down as rules, and it is cheaper and more predictable when it fits. AI earns its place where the input varies too much for a rule, such as reading documents that never arrive in the same format.
Will it slow down our site or systems?
Not if it is built properly. The work happens away from the page render, and where a visitor is waiting on a response we cache and measure it. Slow AI features are almost always an architecture decision rather than a limit of the model.
How do you stop it inventing answers?
It answers from your own content, with the source shown, and it says it does not know rather than guessing. Anything customer-facing gets human review before it goes live.
What does it cost to run?
We measure cost per run before scaling anything. Some use cases are worth it at a hundred a day and not at ten thousand, and you should see that number before you commit rather than after.
Where do we start?
An assessment. We look at where the hours actually go, rank the candidates by return, and tell you which ones are not worth doing. That is a short engagement, and it is the cheapest way to avoid an expensive mistake.
Main Street to the World Health Organization.
Where it pays. Where it does not.
We will tell you when AI is the wrong tool, and we say it often. That is the most valuable sentence in an assessment, and it is the reason ours is worth paying for.
- Repetitive work between two systems that nobody enjoys.
- Thousands of product records that need cleaning and describing.
- Questions your documentation already answers, asked at 2am.
- Decisions nobody on your team can explain the rule for.
- Anything that reaches a customer with no person reviewing it.
- Work so infrequent the setup costs more than the hours saved.
The handover is the whole point.
Full source code, the database, the credentials and the documentation, handed over at the end. No license, no lock-in, and nothing switches off if we part ways.
Build, grow and run under one roof.
Tell us the goal.
We will map the shortest path to it, and give you a real number in two business days.
A real number in two business days, from an engineer. No sales deck, and no call just to book another call.