solutions · 03/08
AI product development and AI integration
AI can be added to any product, but it should not be added everywhere. Together we decide which tasks suit AI, which model will run them at what cost and how to measure the result, and we start with a small prototype.
Who it is for
- Software companies that want to add AI features to their product
- Businesses that want to speed up repetitive processes with AI
- Founders with an idea for a new AI-based product
- Teams that did not get the results they expected from an AI solution
What problem it solves
The most common problems in AI projects:
- a.Features are added because everyone else has one, not to solve a user problem.
- b.Model and usage costs are not calculated upfront, so the bill surprises everyone at scale.
- c.Output quality is not measured and wrong answers reach users.
- d.Data privacy and transparency towards users are an afterthought.
What you get
- A prioritised list of use cases that suit AI
- A comparison of models, infrastructure and cost
- A working prototype, or a technical specification for one
- An evaluation set to measure output quality
- A checklist for privacy, failure cases and user disclosure
Process
- 1
Choosing use cases
We review your workflow and find the steps where AI really adds value.
- 2
Technical decision
We compare model, infrastructure and cost options.
- 3
Prototype
We build a small, measurable prototype for the chosen use case.
- 4
Evaluation
We test output quality and cost with real examples.
- 5
Rollout plan
We define the steps to bring it safely into the product or process.
Where the experience comes from
I am building Entrobase as an AI product from scratch: site and app generation, image generation, automated blogging and ad analysis. I deal with the balance between model choice, cost and quality inside a product every day.
Frequently asked questions
Should we train our own model?
For most business cases, using existing models with the right instructions and data is enough and much faster. If you do need your own model, we show it with measurements.
Will our data stay safe?
Which data goes to a model, where it is stored and who can access it is the first item in the plan. Infrastructure options for sensitive data are evaluated separately.
How will we measure the result?
At the start we build an evaluation set from real examples. After every change, quality and cost are compared on the same set.
other solutions
- 01
Building and leading a software team
A small, fast software team that ships, with the right roles from day one.
- 02
Software project and product management
Planning the road from idea to MVP, and from MVP to a product people use.
- 04
SEO and visibility in AI search (GEO)
Being findable on Google and in AI answers from ChatGPT, Gemini and others.
- 05
Starting and growing a digital agency
An agency structure that delivers, from service packages to team setup.
contact
What do you need help with?
Describe your situation in a few sentences and we will pick the right topic and a time to talk.