AI that earns its place
Models trained on your data to solve a problem you can name. If the problem does not need machine learning, we will say so before you pay for any.
- Built with
- Python, scikit-learn
- You email
- A developer
Fit
Who this is for
A good fit
- You understand AI automation isn't magic and want realistic expectations
- You have good data and want to extract actionable insights from it
- You want to improve decision-making with predictive analytics
- You need to process large amounts of documents or data efficiently
- You can say what success would look like before we start
Not a good fit
- You want AI just because it sounds cool or trendy
- You expect AI to solve problems you haven't clearly defined
- You think AI will replace all your employees overnight
- You want to build the next ChatGPT with a small budget
- You have poor data quality but expect perfect predictions
The build
What you get
Six things that exist when the work is done. Not all six on every project, and we will tell you which ones your problem needs.
- 01 Models trained on your data Built for your problem rather than pulled off a shelf, measured on metrics that mean something to the business, and retrained as the data moves.
- 02 Data processing that runs itself Documents read and extracted, data cleaned and validated, and errors caught and reported rather than silently swallowed.
- 03 Forecasts with their uncertainty attached Predictions that come with confidence intervals, so a decision gets made knowing how much the number can move.
- 04 Recommendations worth acting on Personalisation that surfaces relevant items rather than popular ones, handles a new user with no history, and is A/B tested rather than assumed.
- 05 Text turned into something countable Reviews, emails and documents read at a scale no team can, with the language detected rather than assumed.
- 06 A deployment that survives contact It runs in production with monitoring, and a fallback for the day the model is wrong, because a model that fails silently is worse than no model.
Order of work
How we build something that works
Four steps, in this order. Two of them can end the project early, which is the point of running them first.
Define the problem
We start with what you are trying to achieve and what success would look like. Sometimes the answer is not AI at all, and that is a finding, not a failure.
Assess the data
Good models need good data. If yours is too small, too biased or too dirty, we tell you what has to be fixed before anything is trained.
Prototype and validate
We build a simple version first and prove it works on your data, so you see a result in weeks rather than after months of development.
Deploy and watch
Models drift as the world moves. We track performance in production and retrain when the numbers say it is time.
Before you commit
The truth about AI
Worth hearing before you spend, rather than after.
- 01 It is not magic It is statistics with good marketing. It finds patterns in data and cannot create data that does not exist, so it needs quality training data, it makes mistakes, it carries the biases of what it was shown, and it needs maintaining.
- 02 Most problems do not need it Simple rules, a better process or basic analytics often solve the thing faster and cheaper, and cost nothing to keep running. We reach for a model when the patterns are genuinely too complex for that.
- 03 When it does help, it helps a lot Large datasets, personalisation, repetitive decisions and forecasting are where it earns its price, because those are the tasks people are slow and inconsistent at.
Money
What it costs, and what moves it
No surprise charges and no hidden fees. AI projects vary more than most, so these are the ranges we work in and the things that decide where inside them you land.
- Insights pulled from data you already hold $25K to $40K
- A model built for one specific problem $50K to $120K
- A system with several models in it Let's talk
What moves it
- How much data there is, and how messy it arrives.
- How sophisticated the model has to be to beat a simple rule.
- How many of your systems it has to plug into.
- How accurate it has to be, because the last few points cost the most.
Stack
What we build with
Battle-tested tools rather than the newest ones. Python because the ecosystem is mature, scikit-learn for most problems because it is reliable, and deep learning only when the problem actually needs it. No experimental framework that might be abandoned next year.
- What models are written in
- Python, scikit-learn, pandas, NumPy
- When it needs deep learning
- TensorFlow, PyTorch
- How it gets served
- Docker, FastAPI, PostgreSQL
- How versions are tracked
- MLflow
Boundaries
What we won't build
Six of them, stated before you sign rather than discovered at the demo.
- We won't build AI looking for a problem. If a rule-based system solves it better, that is what we will recommend.
- We won't hand you a black box. Where an interpretable model will do, you get one, because you have to be able to say why a decision was made.
- We won't train on data that cannot support it. Too small, too biased or too dirty means we tell you what to fix first.
- We won't promise general intelligence. We build narrow models for specific tasks, and they do not replace human judgement.
- We won't deploy without monitoring. Models drift and fail quietly, and quiet failure is the expensive kind.
- We won't wave away bias. If the training data is skewed, the model is skewed, and you will hear that from us first.
Questions
AI questions
How do I know if my problem needs AI?
Most problems don't. If you can solve it with simple rules, better processes, or basic analytics, start there. AI makes sense when you have complex patterns in large datasets that humans can't easily identify. We'll tell you honestly if AI is overkill for your situation.
What kind of data do I need for a successful AI project?
Quality matters more than quantity, but you need both. Generally, thousands of examples for simple problems, tens of thousands for complex ones. The data should be representative, accurate, and relevant to what you want to predict. We'll assess your data quality before starting any project.
How accurate will my AI model be?
It depends on your data, problem complexity, and requirements. We'll give you realistic accuracy expectations upfront, not promises of perfection. Most business problems don't need 99% accuracy. 80% might be perfectly useful if it saves time or money.
How long does it take to build an AI solution?
Pulling insights from data you already hold runs six to ten weeks. A model built for one specific problem runs three to six months. Preparing the data usually takes longer than training on it, and a system with several models depends on what they have to integrate with.
What happens when the model stops working well?
AI models degrade over time as data patterns change. We build monitoring systems to detect this and retrain models when needed. This is normal and expected, and it's why ongoing maintenance is part of any AI project.
Do you build web interfaces and handle deployment for AI models?
Yes. Our web development team builds dashboards that make complex data simple, and our DevOps team handles ML infrastructure that scales. We can deliver complete AI solutions, not just models.
Tell us what you're trying to build
On the call you get a price range, a rough timeline, or the reason we're not the right shop. No deck, no second call with a salesperson.