AI-Powered Apps & Automation
AI app development and workflow automation
We build AI features and automation that do a specific job inside your product or operations, such as recommending the right match, reading documents or removing a manual step. We start by checking that AI is the right tool, then ship it at a fixed price with testing and cost control built in.
Book a free discovery callFree 30-minute call · Fixed-price proposal within 3 days · NDA on request
When teams come to us
- Rules and database queries no longer give good enough results, and you want a model that learns from your own data.
- Your team repeats the same manual steps every day, such as copying data between systems or reading documents.
- You want to add an AI assistant or smart feature to a product, but don't want surprise bills or unreliable answers.
What we build
Machine learning and recommendation features
Models trained on your historical data, for example a neural network that recommends jobs to the candidates most likely to succeed.
LLM-powered features and assistants
Assistants and text features built into your product, with guardrails so answers stay inside what they should.
Workflow and document automation
Scripts, scrapers and pipelines that replace repetitive manual work, from collecting readings to extracting data from pages.
Data pipelines and internal tools
Moving, cleaning and storing data reliably, including lower-cost storage for older records, with dashboards on top.
Evaluation, safety and cost control
Ways to measure whether the feature works, limits on what it can do, and monitoring so running costs stay predictable.
How an AI project runs
- The first call is about the problem, not the model. Sometimes AI isn't the right answer, and we'll say so.
- The proposal states what the system will do and how we'll measure whether it works.
- We test on your own data from the first sprints, so you judge real output instead of a slide deck.
- Once it is live, we can keep tuning accuracy and cost on monthly support.
Technology we use
- Python
- TensorFlow and Keras
- NumPy and Pandas
- OpenCV
- Flask
- Node.js
- AWS Lambda and S3
- Docker
Related work
AI development questions
Can you add AI to our existing product?
Yes. We look at where AI would actually help, build it as a feature of your existing product, and avoid rewriting what already works.
How do you keep AI features accurate and affordable?
We agree how success is measured before building, test against your own data, put limits around what the feature can do, and monitor usage so costs don't drift.
Do we need a lot of data to start?
Not always. Some automation needs no training data at all, and for machine learning we tell you honestly on the call whether your data is enough.
Can you automate something that's done by hand today?
Usually, yes. If a task is repetitive and follows clear rules, it's a good candidate. Bring an example to the discovery call and we'll tell you what is realistic.