AI Solutions
Test AI Before You Commit
A proof of concept answers one question: does this work well enough on our real data to justify building it properly? We keep it small, measured and honest.
Overview
AI Proof of Concept Development
An AI proof of concept is an experiment with a decision attached. Before writing code we agree the exact task, the sample data, and what result would count as success or failure. Codemotives then builds the thinnest possible version, often without a polished interface, to test the hardest assumption first, which is usually whether the model and your data can do the job.
The output is not just a demo. You receive measured results on real examples, a list of failure patterns, a view of likely production effort and running cost, and a clear recommendation to proceed, change direction or stop. Stopping is a legitimate outcome, and a cheap negative result is far better than a costly one discovered after launch.
Context
Problems we solve
- 01
Demos that mislead
A handful of cherry-picked examples can make anything look impressive. Results must be measured on representative, unseen data.
- 02
Vague success criteria
Without an agreed target, every outcome can be argued as a win. We fix the pass mark before the work starts.
- 03
Hidden data problems
Missing, inconsistent or restricted data often surfaces only when someone tries to use it. A pilot finds this early.
- 04
Scope creep into a product
Pilots grow features until they stop being cheap. We hold the line on the one question being tested.
Capabilities
What we deliver
- 01
Hypothesis and metric definition
A one-page brief naming the task, the baseline today, the success measure and the decision the result will inform.
- 02
Data sampling and review
Selection of representative samples and a candid review of quality, gaps and access constraints.
- 03
Rapid prototyping
A minimal working build using hosted models or classical methods, whichever suits the problem better.
- 04
Structured evaluation
Scoring against a labeled test set, with attention to failure modes rather than only average results.
- 05
Effort and cost projection
A reasoned view of what a production version would involve, including integration, monitoring and running cost.
- 06
Go, adjust or stop memo
A plain recommendation with the evidence behind it, written for decision makers.
Where it fits
- Testing whether support tickets can be auto-categorized
- Checking extraction accuracy on your real documents
- Trialling a forecast against historical figures
- Comparing model options on one narrow task
- Validating an idea before budget approval
Technologies we use
- Python
- Jupyter
- Streamlit
- OpenAI API
- scikit-learn
- Hugging Face
- FastAPI
Approach
How we deliver
- 1
Discover
We learn your goals, users, constraints and existing systems before proposing anything.
- 2
Plan
Scope, priorities, architecture and milestones are agreed so everyone knows what ships when.
- 3
Design
User flows, interfaces and technical design are shaped and reviewed with you early.
- 4
Build
Work is delivered in short iterations with regular demos, so progress is always visible.
- 5
Test
Functional, integration and performance checks run throughout, not only at the end.
- 6
Launch & support
We deploy, monitor and keep improving the product after it goes live.
FAQs
Common questions
Have a question that is not covered here? Get in touch.
How long does a proof of concept take?
It depends on how clear the question is, how accessible the data is and how many approaches are compared. Keeping the scope tight is the main lever on duration. We agree a timebox up front.
What happens if the proof of concept fails?
You still get something valuable: evidence about what does not work and why. That may point to better data, a simpler non-AI approach or a different use case, and it protects you from a larger failed build.
Can the pilot become the real product?
Sometimes parts can be reused, but pilots are built for speed and learning, not durability. We usually plan a proper production build informed by what the pilot taught us.
Let's build what's next
Tell us about your idea, product or legacy system. Send us a message and we will help you scope the right next step.