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BunnyDevs

Python, AI & ML

Smart IT solutions powered by Python, Data Science and AI.

Data pipelinesPredictive modelsAI features & agentsAutomation

What is included

We build data pipelines, predictive models and AI features that automate workflows and unlock insight — pragmatic AI that ships and delivers ROI.

Pragmatic is doing a lot of work in that sentence. Most AI projects fail because they start from the technology rather than from a decision somebody needs to make faster or better. We start from the decision.

How we work

First, is there a real dataset and a real question? A short discovery answers both, and sometimes ends with us recommending you do not build the thing.

If it goes ahead: the pipeline first, because unreliable data is the actual bottleneck in almost every project. Then a baseline — often something embarrassingly simple — so there is a number to beat. Then the model, evaluated honestly against that baseline. Then integration, monitoring, and a plan for what happens when the data drifts.

Why BunnyDevs

We ship the surrounding product too, so the model does not arrive as a notebook nobody can deploy. It arrives as an endpoint with latency budgets, error handling and a dashboard.

And we will tell you when the answer is a database query rather than a neural network. That honesty is worth more than the engagement.

Common questions

Do we need machine learning, or just good software?

Usually the second one. A rules engine you can read beats a model you cannot explain for most business problems, and it is far cheaper to run. We will tell you when the answer is that you do not need AI — that conversation is free.

Can you use an existing model rather than training one?

Almost always, and it is the right call. Calling a strong general-purpose model with good prompting and retrieval gets most products where they need to be in weeks. Custom training is for when you have proprietary data and a specific, measurable gap.

How do you stop it producing nonsense?

Evaluation before deployment, on your data, with a number attached. Then guardrails in production: constrained outputs, retrieval so answers are grounded in your documents, and logging so you can see what it actually said to real users.

What does it cost to run?

We benchmark inference cost and latency before you commit, so you know what a thousand users a day costs. AI features that quietly become the largest line on your infrastructure bill are a common and avoidable failure.

Python, AI & ML in practice

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2025

FinTech · Trading