What custom ML development includes
- Assistants on your own data: chat assistants that answer from your documents, policies or product data, and say when they do not know.
- Reading documents: pulling names, amounts and dates out of invoices, forms and PDFs into your system.
- Predictions: models for demand, churn, pricing or risk, trained on your own history.
- Generative AI in your product: text, image or video generation features inside your software.
- Automating enquiries: AI that handles routine questions on chat, WhatsApp or phone and hands the rest to your team.
Custom or off-the-shelf?
| Question | An off-the-shelf tool fits when | Custom development fits when |
|---|---|---|
| Your data | The task works on generic or public information | The value is in your own documents, history or product data |
| Your workflow | You can adapt your process to the tool | The AI has to sit inside your existing software and steps |
| Privacy | Sending data to a third-party service is acceptable | You need data kept in a region, not used for training, or on your own servers |
| Cost control | Per-seat pricing suits your usage | You need limits, monitoring and predictable running costs |
| Differentiation | AI is a back-office convenience | AI is part of what you sell |
How a custom ML project runs
- Pick the use case: find the task where AI saves the most time or money, and check it is realistic.
- Small proof first: a working prototype on your own data, so you can judge the results before spending more.
- Build it in: connect the feature to your product or tools, with limits, logging and fallbacks.
- Watch and improve: track quality and cost after launch and tune as real usage comes in.
What you need to start
- A clear task with a result you can measure, such as hours saved or errors reduced.
- Some of your own data or documents to work from.
- A person who owns the process and can judge whether the output is good.
Running costs and data privacy
AI features have running costs that grow with use, so they should be estimated before you commit. A good proof stage includes an estimate of the monthly cost to run the feature.
On privacy, use providers and settings that do not train on your data, keep data in the region you need, and consider running open-source models on your own servers when the data is sensitive.
How we work at MOUNTSMY
We compare two or three models on your own data before choosing one, and we start with a small proof so you can judge results first. We build with models from OpenAI, Anthropic and open-source families such as Llama, and with Python tools such as PyTorch, scikit-learn, LangChain and FastAPI. Most AI features are added to software you already have.
Frequently asked questions
How do I know if I need custom machine learning?
If an off-the-shelf tool already solves the task with your data and process, you probably do not. Custom is worth considering when your data, workflow, privacy needs or product make a generic tool a poor fit.
Is our data safe?
We use providers and settings that do not train on your data, keep data in the region you need, and can run open-source models on your own servers when required.
Which AI model do you use?
Whichever fits the job and the budget. We often compare two or three on your own data before choosing.
How much does it cost to run?
It depends on how much it is used. We estimate monthly running costs during the proof stage, before you commit.
Can AI fit into our existing software?
Usually, yes. Most AI features are added to software you already have rather than built as a separate product.