Predictive Machine Learning
A Machine Learning Development Company That Ships Real Models
Most machine learning work dies between the notebook and production. We start with the decision the model is meant to improve, prove a baseline before we train anything, and build the serving, monitoring and retraining path in the same engagement.
Triple Protection Guarantee
- US-Registered Entity
- Signed IP Assignment
- Senior Engineers Only
Triple Protection Guarantee:
- US-Registered Entity
- Signed IP Assignment
- Senior Engineers Only
Years In Business
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Engineers On Staff
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Avg. Engineer Exp.
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Where ML Stalls Out
Data Readiness
Proven First
Senior Engineers
Vetted Only
Timezone Overlap
Live Hours
Baseline First
Then A Model
Delaware LLC
US Entity
Drift Watched
Not Assumed
Evaluation Rig
Rerun Always
Serving Latency Cap
Set Early
Model Handover
Documented
IP Assignment
Signed
Delivery Overlap
Fixed
Hours
Model And Data
You
Trusted By Startups
What Our Machine Learning Company Actually Does
A machine learning development company earns its fee in the unglamorous parts. Deciding whether the problem needs a model at all. Finding out that the label you plan to predict is recorded after the decision you want to influence. Beating a simple baseline honestly. Getting a trained model behind an endpoint that answers inside a latency budget, then noticing months later when the world moves and the model quietly stops being right.
Stallyons is registered in Delaware as a US company, and our engineers work a time-zone window agreed before the project starts, with four-plus hours of daily overlap. You sign a US contract, run diligence on a US entity and pay one US invoice. Behind the work sits twelve-plus years of delivery across six continents and around thirty-five engineers, averaging four-plus years of production experience. Teams come to us when a model has to survive contact with live data rather than a held-out test set.
What An ML Model Build Covers
Problem framing first: the decision the model informs, who acts on the output, what it costs to be wrong in each direction, and the simple rule or heuristic the model has to beat before it earns a place in production.
Data readiness as its own phase: where the records come from, how much is missing, whether labels are consistent, and whether any feature encodes the answer the model is supposed to work out for itself.
Training and evaluation you can inspect: a versioned dataset split, an evaluation harness rerun on every change, metrics reported per segment instead of one flattering average, and the failure cases written down.
Deployment as engineering: the model behind a real interface, batch or real-time to match the decision, a latency and cost budget agreed in advance, and a rollback that does not need us.
Life after launch: input and prediction drift watched, a retraining trigger defined rather than improvised, and the offline metric checked against what the model actually did in production.
One accountable vendor: one contract, one invoice and one entity for legal and finance to run diligence on, instead of a data scientist, an ML platform and a backend team who never speak.
Why Buyers Screen A Machine Learning Company So Hard
- Ask what the baseline is. A model that beats nothing is not a result, and a team that cannot tell you what the current rule or human decision achieves has not measured the problem.
- Ask how they check for leakage. Most implausibly strong results come from a feature that was recorded after the outcome, and it only shows up once the model reaches live data.
- Ask where the labels come from. If nobody can describe how the training labels were produced and who checked them, the model is learning somebody's guesswork.
- Ask what happens on day ninety. Data shifts, behaviour changes and a model that was accurate at launch decays quietly, so drift monitoring belongs in the original scope.
- Check the exit before you need it. The code, the trained weights, the feature definitions and the training data sit in your own accounts, so changing supplier is administration.
- Ask when a model is the wrong answer. A vendor who has never talked a client out of a model, or toward a simpler rule, is selling capacity rather than judgement.
How A Machine Learning Project Starts
Every project starts with a free 45-minute scoping session. No slide deck, no sales script. You bring the decision you want to improve and what you already record about it; you leave with a build plan and a timeline.
We are selective about new projects and cap how many we run at once, because the framing is the product. If your problem is better solved by a query, a rule or a report, we will say so before you buy a model.
Why Clients Choose Us
Full
Written IP Transfer
USA
Contract Entity
Yours
Weights & Data
Named
Delivery Lead
Ready to find out whether a model is the right tool?
What We Build With ML Models
The Machine Learning Work We Deliver
Every problem is different and the underlying jobs repeat: frame the decision, get the data straight, train something that beats a baseline, serve it and watch it. These are the models we are asked for most often.
Sales & Demand Forecasts
Volumes, seasonality, stock planning
By SKU Line
Churn Prediction
Who leaves, when, and why now
Early Signal
Recommendation
Ranking, similar items, next best
Relevant
Fraud And Anomalies
Outliers, rules, review queues
Flagged Early On
Price Models
Elasticity, margin, promotions
Tested Live
Credit Risk Scoring
Scoring, segments, explanations
Auditable
Computer Vision Models
Defects, counting, image sorting
Sorted
Predictive Maintenance
Sensor data, failure windows, alerts
Forecast
Model Rescue Work
Notebooks turned into services
In Production
MLOps And Retraining
Pipelines, monitoring, retraining
Kept Honest
Not sure which of these fits your problem? Let us map it.
Common Challenges
Why Do ML Projects Get Stuck?
Six patterns behind almost every model that never reached production. All six start long before the training run.
No Baseline Set
01
Nobody wrote down what the current rule, report or human decision achieves. The model reports a number that sounds impressive, and there is no way to tell whether it is better than what already existed.
Target Leakage Risk
02
A feature in the training set was recorded after the outcome it predicts. Results look excellent offline and collapse the first week on live data.
Labels Nobody Owns
03
The training labels came from an export nobody can explain. Two analysts would have labelled the same rows differently, so the model learns the disagreement.
One Average Score
04
Performance is reported as a single headline number. It hides the segments where the model is worse than guessing, which are usually the smaller groups and the newest customers.
Still Stuck In A Notebook
05
The model works on one laptop, against a CSV, with steps that live in someone's head. There is no interface, no schedule and no way for another system to ask it a question.
Nobody Watches It After
06
The model shipped and then nothing measured it. Inputs shifted, behaviour changed, and the predictions drifted out of usefulness with nobody watching.
Recognise a few of these? Let us do it properly.
Our ML Build Services
6 Machine Learning Development Services
Six ways to buy model delivery from one accountable vendor. Run one, or run several in parallel under a single contract.
Custom Model Development
01
End-to-end delivery of a defined predictive model: framing, data preparation, training, evaluation against an agreed baseline, deployment and handover, with a named lead reporting to you.
Data Readiness Work
02
An honest read on whether your data can support the model you want: coverage, quality, label consistency, leakage risk and the gaps to close, delivered before anyone trains anything.
Predictive Analytics Builds
03
Forecasting, churn, propensity and pricing models built against the decision they inform, with results reported per segment instead of one average.
Model Evaluation & Audit
04
An independent read on a model you already have: leakage checks, segment metrics, calibration, and a written view of where it is weakest and what to do next.
Deployment & Serving
05
The model behind a real interface, built with our own API development practice: batch or real-time scoring, versioning and a rollback path.
MLOps, Monitoring & Retraining
06
Pipelines, a model registry, drift and data-quality monitoring, defined retraining triggers, and the alerting that tells you before your users do that something moved.
Not sure which piece you need first? Let us scope it together.
Why Choose Us
What Makes Our Machine Learning Work Different
The details that decide whether a model changes a decision or just decorates a dashboard.
A US Legal Entity
01
Stallyons is registered in Delaware. Your contract, your invoice and your legal recourse sit with a US company, not an unknown one.
Baseline Before Model
02
We measure what your current rule or report achieves first, so every later number has something honest to be compared against.
Overlap You Set
03
You choose the hours we share with your working day, and stand-ups, reviews and escalations all happen inside that window.
Segment-Level Truth
04
Results are reported per segment as well as overall, because a flattering average usually hides the groups the model handles worst.
Reviewed Code
05
Every merge is reviewed against an agreed definition of done, on your board, where you can read it yourself.
One Contract
06
One contract covers the engagement, so procurement, legal and finance each deal with a single named counterparty.
Ready to see what a serious ML build looks like?
Our Process
From First Call To Model In Production In Six Steps
A build process that settles the decision, the data and the baseline before any training.
Discovery
Understand the decision, users and data sources
Scoping
Agree the baseline, metrics and cost
Labels
Sources, labels, leakage checks, feature work
Contracting
NDA, IP assignment, access and onboarding
Training
Train, evaluate, review on merge
Deploy & Watch
Serve it, then monitor drift and retrain
Want to see how this maps to your roadmap?
Technology Stack
What Our Machine Learning Engineers Build With
The languages, libraries and services we train models on, and the tooling that keeps them running.
Model Building
Python Toolchain
PyTorch Models
TensorFlow & TFX
scikit-learn
XGBoost GBM
Data & Feature Pipelines
Spark & ETL
Feature Stores
Data Labels
dbt SQL
Postgres Warehouse
Training & Eval
Experiment Logs
Tuning Sweeps
Cross-Validation Sets
Bias & Slices
Offline Metrics Suite
Serving & MLOps
Model Registry
Docker Runs
Batch Scores
Real-Time Endpoints
SageMaker / Vertex
Monitor & Retune
Drift Detection
Alert Rules
Datadog Metrics
GitHub Actions / CI
AWS & Cloud Infra
Who We Build This For
Machine Learning For Every Kind Of Business Decision
Eight kinds of business with different data and one shared need: a decision made better than it is today.
Retail & Commerce
Demand, pricing, assortment mix
Fintech & Risk Scoring
Credit models, fraud, disputes
SaaS & Subscription
Churn, expansion, usage signals
Logistics & Supply
Demand, routing, delivery windows
Manufacturing & Plant
Failure windows, yield, defects
Healthcare Operations
Capacity, no-shows, triage
Marketplaces & Ads
Ranking, matching, relevance
Energy & Utility Load
Load forecasts, asset health
Working in another sector? See all industries we serve.
How We Compare
Your Machine Learning Options, Compared
An honest look at your four delivery options.
| Capability | AutoML Platform | In-House Analyst | Freelance Data Scientist | Stallyons Technologies |
|---|---|---|---|---|
| Baseline before modelling | ✕ Not part of it | Sometimes | Varies by person | Measured and written down |
| Leakage and label checks | ✕ Your responsibility | Found late | Usually informal | Own phase before training |
| Segment-level evaluation | Headline metric only | On request | Rarely | Reported per segment |
| Deployment and serving | Vendor endpoint | ✕ Hands off to IT | ✕ Notebook handover | Built with the model |
| Drift monitoring after launch | Add-on tier | ✕ Nobody owns it | ✕ Contract has ended | Scoped from day one |
| Telling you not to build it | ✕ Never | Career risk | Paid to build | Said during scoping |
| Weights, code and data owned | ✕ Locked to platform | Your own accounts | Varies | Yours from day one |
See the difference for yourself
Complete Engagement
Everything Included In Your Machine Learning Build
From Scoping to Contracting to Delivery, One Vendor
Here is everything included when you build your model with us:
One Machine Learning Build Price: No Hidden Fees, No Surprises.
Every machine learning engagement includes all eight components above. One contract, one senior team, one predictable cost, and no vendor sprawl.
🔒 No obligation. We'll deliver a detailed proposal within 48 hours.
Plus, Get These Free Bonuses
Free Data Readiness
A written read on your data coverage, label quality, leakage risk and whether the problem needs a model at all, with the gaps ordered by what blocks you first.
Included Free
Build Plan And Estimate
A phased build plan with scope, milestones, the integrations it needs and a transparent, itemised estimate for the engagement.
Included Free
Free Vendor Checklist
The questions we would ask any machine learning development company about baselines, leakage, evaluation and drift, so you can test us too.
Included Free
Risk-Free Partnership
Our Machine Learning Promise
We stand behind every engagement with commitments that protect your investment.
01
Scope Agreed First
Scope, model, working hours and cost structure are written down and agreed before contracting, so nothing is discovered later.
02
Built to Last
Senior engineers, code review, automated tests, security and accessibility audits, and clean, documented code you fully own.
03
IP And Access Protected
NDA and IP assignment are signed before access, permissions are scoped per person, and your accounts stay under your control.
Start your machine learning build with confidence, backed by our Triple Protection Guarantee.
Track Record
Engagements That Ship, Scale, and Compound
500+
Projects Delivered
29+
Service Categories
81%
Repeat Client Rate
4.9 ★
Clutch Rating
"Stallyons took our Figma design and built it into a live web application, a cognitive game with level-based match play, messaging, a tutorial, and a directory that ranks users nationally. What impressed me most was their grasp of the code behind that logic, and the quality of the experience. Delivered on time with steady updates."
Jerry L.
Founder
PicCiti LLC
"We brought Stallyons in to absorb an overflow of work, and they delivered ten iOS and Android apps, from reporting to geo-location for logistics, plus several backend systems, owning design, development, and app-store submission. Everything stood out: code quality, speed, and reliability. Perfect code, on time, adopted company-wide."
William B.
Director
Amplo Solutions
FAQ
Frequently Asked Machine Learning Questions
A machine learning development company turns a business decision into a model that improves it, then keeps that model working. In practice that means framing the decision and its cost of being wrong, auditing the data and the labels behind it, establishing a baseline to beat, training and evaluating honestly per segment, deploying the model behind a real interface, and monitoring drift so it can be retrained.
Machine learning here means predictive modelling: you have historical data with outcomes, and you want a number or a category for a new case, such as demand next month or the risk on this application. Generative models produce new text, images or code and are better at open-ended language and content work. They solve different problems, and picking wrongly is expensive. If your problem is generative, our generative AI development team owns that.
Cost follows the state of your data far more than the choice of algorithm. A clean labelled dataset and a well-defined decision is a short engagement; scattered sources, thin labels and a vague objective is a long one, so a figure before scoping is guesswork. We scope the data first, then price, and itemise each phase so you can cut before you commit.
Ask what baseline the model has to beat and how they measured it. Ask how they test for leakage, and where the training labels came from. Ask to see metrics broken down by segment instead of one headline number. Then ask what happens in month three when the data shifts. The answers separate a modelling practice from a demo.
You do, from the first day. The repositories, the trained weights, the feature definitions, the training data and the cloud accounts are all registered in your name, and NDA and IP assignment are signed before anyone gets access. Everything produced on the engagement is assigned to you outright, with no licence-back.
Less than most people fear, and of better quality than most people have. What matters is how many examples carry the outcome you want to predict, how consistently they were labelled, and whether they cover the cases you care about. A readiness review answers this in days, and sometimes the right answer is to fix collection before modelling.
By treating it as a running system rather than a delivery. Input distributions and prediction distributions are monitored, data-quality checks run on the features, a retraining trigger is defined during scoping, and the offline metric is compared against what the model actually did in production. Alerts go to you as well as to us.
Those sit with a different practice. Classification, extraction, sentiment and search over text are language problems, and our natural language processing team owns them. We often work alongside each other, where a text signal becomes a feature in a predictive model, and we scope that boundary at the start.
Still have questions? Let's talk.
Schedule an appointment with us today!
Ready To Build A Model That Earns Its Place?
Get a free consultation. We will walk your data, tell you whether a model is the right tool, and send a written proposal.
