AI Consultancy & Strategy
Applied ML models
Not every problem needs a chatbot. We build classic machine-learning models for forecasting, scoring and classification, trained on your data and deployed where decisions are made, with monitoring so they stay accurate as things change.
Model in production
Forecast · Score · ClassifyIs this for you?
You’ll get value from this if…
- Forecasts are built in spreadsheets and are often wrong
- You need to score leads, risk or churn consistently
- Items must be sorted or tagged at a volume people can't keep up with
- A data-science prototype never made it into production
Benefits
What changes for your business
Consistent decisions
The same inputs get the same score, every time, at any volume.
Predictions where they're used
Models run inside your systems, not in a notebook.
Accuracy that holds
Monitoring catches drift before it quietly degrades results.
What you get
What we deliver
Trained model
Built and validated on your historical data, with the trade-offs explained plainly.
Production deployment
An API or batch job wired into the system where the prediction is used.
Monitoring
Tracking of accuracy and data drift, with alerts when the model needs retraining.
Retraining pipeline
A repeatable way to refresh the model as new data arrives.
How it works
From first call to running in production
- 01
Frame
Agree the decision the model supports and how success is measured.
- 02
Data
Assess, clean and prepare the data, and be honest if it isn't enough.
- 03
Model
Train and compare approaches, starting simple.
- 04
Deploy
Put it into production with monitoring and a retraining plan.
Example applications
What this looks like in practice
Typical applications of this service. Illustrative, not client case studies.
- 01Demand and inventory forecasting
- 02Lead, churn or credit-risk scoring
- 03Classifying tickets, documents or products
- 04Detecting anomalies in transactions or sensor data
Technology
Tools we work with
- Python
- scikit-learn
- PyTorch
- SQL
- Cloud ML services
- Docker
Why Bitfumes
Built by engineers who ship
Senior team, no hand-offs
The engineers on your first call are the ones who build your product.
10+ years of engineering leadership
Led by Sarthak Shrivastava, Docker Captain, AWS Certified Solutions Architect, AWS Certified Developer.
We teach this for a living
156K+ developers learn from our founder on YouTube, and 100K+ on Udemy.
Production, not prototypes
Tests, monitoring and handover are part of every build, not extras.
How to start
From first conversation to production
- 1
Talk to us
Tell us the problem. We come back with a straight view on whether it is worth building.
Get in touch - 2
Build
A senior team embeds with yours and ships in short cycles, with a demo every week.
- 3
Run and improve
We hand over cleanly, or stay on to monitor, support and extend what we built.
FAQs
Common questions
When is classic machine learning better than an LLM?
When the task is a prediction from structured data, such as a forecast, a score or a category. Classic models are usually cheaper, faster and easier to validate for these.
How much data do we need?
It depends on the problem. Assessing whether your data is sufficient is the first step, and we will say if it isn't.
Who maintains the model afterwards?
We hand over monitoring and a retraining pipeline your team can run, or stay on to maintain it.
Can you explain why the model made a prediction?
Where it matters, we choose interpretable models or add explanations so people can see which factors drove a prediction.
Where does the model run?
In your cloud or infrastructure, as an API or scheduled job, depending on where predictions are needed.
Insights
Related reading
- EngineeringAI in production: the checklist most teams skip.Getting a demo working is the easy 20%. Here's what separates a prototype from something you can trust running unattended in front of customers.Read
- EngineeringBatch your AI calls, halve the bill.A for loop calling the model once per ticket pays full price and competes with live traffic for the same rate limit. The Batch API processes up to 100,000 requests at once, at half the cost — for exactly the work that was never going to need an answer in three seconds.Read
Next step
Want this built into your business, not just explained?
Tell us the problem and we'll come back within one business day with a straight view on whether AI is worth it for you, and what it would take to build.
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