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Machine Learning Development

We are a machine learning development company that builds predictive models on your own data: risk scores, forecasts, rankings, classifiers, and anomaly detectors. We measure the model against the rule or report your team uses today, on past cases kept out of training. We design it to run in shadow mode (scoring without acting) before it influences a live decision. The training step of one of our pipelines is public, so you can read the code before you call.

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Our Data Science Solutions

Plus8Soft’s machine learning development services build a predictive model around one decision your team makes by hand or by rule today. Examples are which claim to review, which lead to call, or how much stock to order. The work covers the data audit, the baseline (the rule or report your team would use without a model), and feature engineering (building the inputs the model reads per case). Training and selection follow. After training come the held-out evaluation, deployment into your workflow, and the monitoring that says when to retrain. The pipeline we build runs in the cloud account the scope names, yours or one we set up in your name.

The training step of our open-source AML pipeline is public, so your data scientist can read it. A fraud score, a demand forecast, or a document classifier is scoped the same way, baseline and held-out set first, shadow run before go-live.

Scoping takes two to three weeks at a fixed fee. It ends with the baseline, a held-out set of past cases with known outcomes (cases the model never trains on), and a pass bar. The pass bar is the margin the model must clear above the baseline. All three are named in the written scope and agreed before training starts. The pass bar is written in your units, such as frauds caught per hundred referred cases or hours of review saved per week. The data audit records what you have and whether each past case has its real outcome recorded. The first model follows in four to eight weeks and is designed to run in shadow mode. The shadow report ends with our recommendation, and the go decision is yours.

Generative features that write text belong to our generative AI development services. A score that becomes a feature of a product you already run, behind its existing API and feature flags, ships through our AI integration services. The model behind it is scoped on this page. Training pipelines, model versioning, and monitoring across several models are our MLOps consulting, and models on clinical and medical-billing data are scoped under healthcare data analytics. Send us the decision and a description of the past cases (how many, which fields, how outcomes are recorded) to start scoping.

Machine Learning Solutions We Build

Six kinds of models cover the decisions we are most often asked to automate. We measure each against the baseline before it reaches the workflow.

Risk and Fraud Scoring

A score per transaction or claim, with the features that drove it and a threshold set by what a false alarm costs you. Our open-source AML pipeline shows the training step, including graph features (degree and PageRank computed on the transaction graph).

Demand and Time-Series Forecasting

Forecasts of demand or load by item and period, scored against last year's actuals and, where recorded, the forecast your planners make today. The report lists its error by segment (region, product line, item group), a guide to where planners override.

Ranking and Recommendation

A ranking of candidates, leads, or products, trained on the outcomes your team recorded and checked by segment before it goes live. Hello, We Hire pre-screens 1,000 candidates per hour and ranks matches against custom criteria.

Document and Text Classification

Routing, tagging, and field extraction over tickets, contracts, forms, and emails, with a confidence score and a review queue for the cases below it. Built with the model type the validation runs favor, a classical scoring model or a language model.

Anomaly Detection

Transactions or sensor readings flagged when they leave their usual range, with the alert rate tuned to what your team can review. The baseline is the static rules you run today, measured on past cases with known outcomes by catch rate and alert volume.

Computer Vision

Defect detection, image and scanned-document classification, and object counting from your own photos and scans, with a labeled set your team reviews before training. The pass bar is set against the catch rate your inspectors reach on the same labeled set.

Our Data Science Capabilities

Machine Learning Consulting and Use-Case Scoping

Our machine learning consulting services start with the decision the model will support, who makes it today, and what a wrong answer costs in each direction. The baseline's numbers on past cases are the first result of scoping. The data audit records what you have, how late outcomes arrive, and where information that would not be available at decision time has leaked into the training data. Scoping ends with a feasibility verdict: build, start with a rule, buy an off-the-shelf platform, collect more data first, or stop at the analysis. We say buy when a vendor's model already covers your decision and your data adds little to it.

Data Preparation and Feature Engineering

We reconcile each training label (the outcome the model learns from) with the outcome your business records and exclude cases without a trustworthy one. We set the held-out set aside before training starts and keep it out of training and tuning until the final evaluation. Features come from your operational data (transaction history, account age, text fields, links between accounts). We version the feature code with the model, so training can be rerun from a commit. We remove or mask personal identifiers before training unless the scope names a field the model needs, and that list is yours to approve. Training and evaluation run inside your cloud account under access the scope grants.

Model Training and Selection

We start with classical models (scoring models for data in rows, such as logistic regression, gradient boosting, and random forests). We add deep learning where the data and the evaluation justify it. Runs record the data version, the feature code, the parameters, the metrics, and the random seed, so a rerun can be compared with the original run. We compare candidates on a validation split (a slice of the training data, separate from the held-out set) by the agreed metric, with a tiebreak defined in advance. Our open-source AML pipeline shows this selection step: it picks the best of three model types by ROC-AUC, with PR-AUC as the tiebreak. ROC-AUC is a standard score for how well a model separates the cases that went wrong from the rest.

Evaluation, Fairness, and Explanations

The final evaluation runs once on the held-out set and reports the model against the baseline on the agreed metric and the error by segment. For classifiers, it also reports calibration (whether cases scored 80% turn out positive about 80% of the time). Where the model touches people, such as hiring, lending, or claims, and your data carries the attributes, the error is reported by the protected groups you name before go-live. The results stay with the training record, and your compliance or model-risk owner signs off on them. Where the model type allows it, we attach to each prediction the features that contributed most to its score.

Deployment Into the Workflow

The model is served where the decision is made: a nightly batch into your database, or a column written into your claims or loan system through its API or import. Where the scope needs it, a real-time scoring service sits behind your API. We build the integration with your system admin. We design it to run in shadow mode first and to go live behind a feature flag (a per-account on/off setting) after you approve the numbers. Your reviewer can override the score on a case. We log the override and flag the case for review when its outcome is recorded. The serving code, the feature pipeline, and the trained model file live in your cloud account and your repository, or in a cloud account we set up in your name.

Monitoring and Retraining

After launch, a dashboard shows the live metric against the held-out result, the input drift (whether today's cases still resemble the training data), and how long outcomes take to arrive. Retraining is triggered by a signal, such as drift past a threshold or a scheduled batch of new labels, monthly or quarterly, as set in the scope. A person approves before the new version goes live, and we keep the previous version in the registry (the stored list of model versions) for rollback. This is the operations layer our MLOps consulting builds out across your models.

Customer feedback

“The entire collaboration experience was pleasant and professional.”

Karim Chaanine

Founder, Brand Growth OS

“Working with Plus8Soft has been a game-changer for our company. Their talented team didn’t just bring technical expertise to the table; they became an extension of our own workforce, working hand in hand to design solutions. Plus8Soft genuinely cares about the outcome. This is evident in every interaction, line of code, and solution they deliver.”

Jenn Heil

CEO, Revvel

“Plus8Soft saved us at the earliest stage. They prioritized tasks and allocated resources strategically. We rely on them moving forward. Highly recommend for early-stage projects.”

Humberto Pedrero

CEO, HolaSalud

“Plus8Soft has been great. They’ve consistently been top-notch with nothing to complain about.”

David Trabulo

Founder, FitEcho

Plus8Soft completed all the work on time and stayed in touch with us throughout the entire development process. The team was attentive and always offered the best solutions when integrating complex features for our application. Very positive experience!

Yulia Kholodova

CPO, Indlovu Inc

How a Machine Learning Project Runs

Four steps from one decision to a model running in your workflow. The timelines are planning estimates, confirmed in the scope.

1. Data Audit and Baseline

Two to three weeks. Output: the decision and its cost of error, the data audit, the baseline and its numbers on past cases, the held-out set, the pass bar, and the feasibility verdict. It ends with a written scope that prices the first model and lists the hours it needs from your domain expert.

2. First Model on the Held-Out Set

Four to eight weeks. Features, training runs, selection by the agreed metric, and one final evaluation on the held-out set against the baseline. At the end of this step you keep the data audit, the feature code, and the baseline, and the report records the result against the pass bar.

3. Shadow Run in Your Workflow

Two to four weeks. The model scores live cases next to the decisions your team makes, without acting, and the live numbers are compared with the held-out result. You decide on those numbers. Whichever way you decide, the model, the pipeline, and the shadow report stay with you.

4. Rollout, Monitoring, and Retraining

The model goes live behind a feature flag, with the dashboard, the drift alert, and the retraining trigger listed in the scope. We run it under a monthly scope (a fixed list of operations tasks at a fixed monthly fee). Or we hand it to your team with the runbook (the written procedures for running it).

Technologies and Tools We Use

Foundation Models

OpenAI

Anthropic

Llama 3

Gemini

Development

Python

TensorFlow

PyTorch

Scikit-learn

Orchestration

LangChain

LlamaIndex

Haystack

Vector Databases

Pinecone

Weaviate

MongoDB

Data Processing

Pandas

NumPy

Apache Spark

Embedding

OpenAI

Cohere

Engines

Apache Spark

Hadoop

Flink

Storage

Amazon S3

Azure Data Lake

Google Cloud Storage

Warehousing

Redshift

BigQuery

Snowflake

Our Case Studies

Hola Salud

Digital healthcare platform for Mexico.

We built a complete healthcare ecosystem, including a patient app, a doctor’s portal, and an admin backend, with self-registration, appointment booking, medical records, and integrated Stripe and Oxxo payments.

Read the entire case

Environmental AI

Environmental compliance automation.

We built a RAG-based system that automates environmental assessment documents and regulatory templates, cutting preparation time by up to 90%.

Read the entire case

Revvel

AI-powered women’s health platform.

We developed an AI platform that turns elite health experts into digital twins and reviews years of patient data in under 60 seconds, delivering risk assessments up to 10x faster and cutting time-to-care from days to minutes.

Read the entire case

InnerPeak.AI

Student mental wellness platform.

We developed a comprehensive web and mobile application that provides students and teachers with 24/7 personalized support and engaging resilience training to build essential life skills.

Read the entire case

Hello, We Hire

AI-driven recruitment automation platform.

We built an AI-powered recruitment system with automated pre-screening, real-time skills evaluation, and multi-stage verification that speeds up hiring by 94%.

Read the entire case

Hola Salud

Digital healthcare platform for Mexico.

Read the entire case

Environmental AI

Environmental compliance automation.

We built a RAG-based system that automates environmental assessment documents and regulatory templates, cutting preparation time by up to 90%.

Read the entire case

Revvel

AI-powered women’s health platform.

Read the entire case

InnerPeak.AI

Student mental wellness platform.

We developed a comprehensive web and mobile application that provides students and teachers with 24/7 personalized support and engaging resilience training to build essential life skills.

Read the entire case

Hello, We Hire

AI-driven recruitment automation platform.

We built an AI-powered recruitment system with automated pre-screening, real-time skills evaluation, and multi-stage verification that speeds up hiring by 94%.

Read the entire case

View all case studies

Flexible Developer Hiring Model

Your priorities define the way we work together — from how much control you want to the level of flexibility your project demands.

Team Augmentation

Quickly scale your workforce with skilled developers who integrate seamlessly into your in-house team, filling talent gaps and boosting productivity.

Dedicated Development Team

A whole, cross-functional team working exclusively on your project, providing end-to-end development, management, and long-term support.

Software Development Outsourcing

Delegate your entire project to our specialists and focus on business growth, while we handle development, delivery, and quality.

Why Plus8Soft?

Experience
Multiplied by AI

We blend deep engineering expertise with cutting-edge AI acceleration. By integrating intelligent tools into our workflow, we don't just write code—we engineer solutions faster and with higher precision.

Business-First
Transparency

We look beyond the ticket. Our team operates with hyper-transparency, treating your budget and goals as our own. We align technical decisions with your business strategy to create real, measurable value.

Committed to
Overdelivery

Meeting requirements is our baseline; exceeding them is our culture. Whether it's optimizing performance, refining UX, or anticipating future scalability, we consistently go the extra mile.

Frequently Asked Questions

What do your machine learning development services include, and what do they leave out?

Plus8Soft's ML development services take one business decision from data audit and baseline to a trained model scoring cases live in your workflow. They include the machine learning software development around it: the serving job, the integration into your system, and the dashboard. There is no Plus8Soft platform to license. The pipeline is code in your cloud account (or one we set up in your name) and your repository. One-off analysis without a model stays in scoping, and generative features that write text go to our generative AI development services. The training pipeline, model versioning, and retraining for several models are our MLOps consulting.

What do machine learning consulting services include?

At Plus8Soft, machine learning consulting services are the scoping phase: the decision, the baseline, the data audit, and the feasibility verdict. The verdict is build, rule, buy, collect more data, or stop at the analysis. Development is the model, the pipeline, and the deployment that follow. Scoping is priced on its own, the build is priced from it, and as a machine learning consulting company we hand over the scoping result if you stop there.

Our data scientist already has a notebook. Can you work from it?

Yes. Scoping starts from it: we run it as it stands, measure it on a held-out set, and compare it with a baseline. What clears the pass bar stays, and we build the pipeline on top of it. Our engineers work in your repository, your data scientists keep their models, and the handover is designed as a pipeline they can rerun from a commit.

Is this data science consulting?

It overlaps. Our scoping phase is the analysis, and the engagement continues into a model that scores live cases in your workflow, with the pipeline to retrain it. Where the question is a one-off analysis, we say so at scoping and keep it to the consulting phase.

How much data do you need for machine learning?

We need enough past cases with known outcomes to set some aside for the final evaluation and still train on the rest. As planning rules of thumb, we look for a few thousand labeled cases for a classifier and two or three years of history by item for a forecast. For computer vision, we look for a few hundred labeled images per class to start. The data audit in scoping gives the number for your case, and when the answer is not enough, the baseline rule runs first while labels accumulate.

How long does a machine learning project take?

Two to three weeks of scoping, four to eight weeks to the first model on the held-out set, then two to four weeks of shadow run before go-live. A first model is live in about two to four months from the start of scoping. Further models on the same data can follow faster, because the audit, the features, and the pipeline are reused. These are planning estimates, confirmed in the scope.

How much does machine learning development cost?

Scoping has a fixed fee, quoted before it starts. The first model is priced from the scope, and each further model is estimated on its own. The cost is driven by the state of the data and the labels. We plan the first model with an ML engineer, a data engineer, and a project manager. Your domain expert reviews the baseline and the labels for the hours listed in the scope. Cloud training and serving costs are paid to your provider and are estimated in the scope.

How do you measure whether a machine learning model works?

Plus8Soft judges a model by two numbers agreed before training, the baseline's result on past cases and the pass bar above it. A third is measured after: the model's result on the held-out set. That set never trains or tunes the model, and the report shows the metric by segment, the calibration for classifiers, and the error cost at the chosen threshold. Then the shadow run compares the model with your team's live decisions for two to four weeks.

Machine learning vs deep learning: which one does our project need?

On tabular data (rows in a table), our first candidate is a classical scoring model on good features, and it is the easier one to explain to an auditor. We add deep learning where the data is images, audio, or long text and the validation runs justify it. Tasks that read or write text get a language model, and the rest get a classical scoring model, decided at scoping. Where two candidates are close, the comparison runs on a validation split before the held-out set is scored.

How do you handle machine learning model explainability for auditors and regulators?

At the level the model type allows. Each prediction can carry the features that contributed most to its score. The evaluation reports the metric by segment, including the protected groups you name where the model touches people. The training record (data version, feature code, parameters) is the input to your validation, which stays with you. Your validation starts from that record, whether under SR 26-2 (which covers predictive models), an insurer's model governance review, or the EU AI Act for a hiring tool. For the EU AI Act, the contract records the provider and deployer roles the Act assigns to each party and the obligations that follow.

Do you build computer vision and NLP models?

Yes. Computer vision development services cover defect detection, image and document classification, and object counting, trained on your own photos and scans. Labeling is the main cost, and your team reviews the labeled set before training. NLP covers classification, extraction, and routing over text, with a classical model or a language model chosen by the validation runs.

Who maintains the model after launch?

Your team or ours. A monthly scope is a fixed list of operations tasks at a fixed monthly fee. Under it, we watch drift and the live metric, retrain when the trigger fires and a person approves, and keep the previous version ready for rollback. With a handover, your team gets the pipeline, the dashboard, and the runbook. The model, the feature code, and the machine learning software development written for you are yours under the contract in both cases. The open-source AML pipeline keeps its Apache 2.0 license.

Machine Learning Development

Our Data Science Solutions

Machine Learning Solutions We Build

Our Data Science Capabilities

Customer feedback

Technologies and Tools We Use

Our Case Studies

Flexible Developer Hiring Model

Why Plus8Soft?

Frequently Asked Questions

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