AI and ML Development Services That Turn Your Data Into Working Models

AI and ML development services build machine learning models trained on your own data to forecast, classify, score, and detect, then deploy and maintain them in production. Pixel Web Solutions covers the full lifecycle: data engineering, model development, explainability, deployment, and the MLOps that keeps accuracy from quietly decaying.

Book a free data feasibility call → See when ML beats an LLM

Get your free data and model feasibility check

Tell us what you want to predict. A senior data engineer will assess whether your data supports it, what accuracy is realistic, and what a first model would take. 30 minutes, no cost.

  • An honest read on whether your data can support the prediction
  • Realistic accuracy expectations, set against a baseline
  • A recommendation, including when a simpler method would do

No spam. Your details are used only to arrange your consultation.

70

AI, ML and Software Projects Delivered

6

Industries Served

12

Years of Data and Engineering Delivery

6

End Users on Systems We Built

Most machine learning projects fail before a single model is trained

The model is the easy part. Projects fail because the data is incomplete, inconsistent, or not permitted for the intended use, because no one defined what accuracy would be good enough, and because a model that nobody deploys is a report with extra steps. We close all three gaps before training starts.

The data is not what people think it is

Almost every project begins with a belief about what the data contains, and almost every data audit revises it. Missing history, inconsistent labels, changed definitions, and gaps around exactly the events you want to predict are the norm. We find that out in week one, not month three.

No baseline means no verdict

Without a baseline, nobody can say whether a model is good. We establish what current performance looks like, whether that is a human process, a rule, or an existing system, and agree the number the model has to beat before it is worth deploying.

The model never leaves the notebook

A high-scoring model in a notebook is worth nothing. Deployment, integration, monitoring, and retraining are scoped as part of the project rather than treated as someone else's problem afterwards.

Our AI and ML Development Services

Pixel Web Solutions delivers nine machine learning services, from a first feasibility study on your data through to deployed models with monitoring and scheduled retraining.

Custom Machine Learning Model Development

Classification, regression, ranking, and clustering models trained on your data and benchmarked against an agreed baseline before deployment.

Predictive Analytics and Forecasting

Demand, revenue, churn, and risk forecasting that accounts for seasonality, promotions, and external signals, delivered into the dashboards your team already uses.

Computer Vision Development

Object detection, classification, OCR and document parsing, quality inspection, and video analytics, deployed to cloud or edge hardware.

Natural Language Processing

Text classification, entity extraction, sentiment analysis, semantic search, and multilingual pipelines, using trained models where they outperform a general-purpose one on cost or latency.

Recommendation and Personalisation Systems

Real-time product, content, and offer ranking built on behavioural and catalogue data, with cold-start handling and measurable lift.

Anomaly and Fraud Detection

Streaming models that score transactions and events in real time, with explainable outputs for compliance and investigation teams.

Data Engineering and Feature Pipelines

The layer everything else depends on: ingestion, cleaning, validation, feature stores, and pipelines that keep training and production data consistent.

MLOps and Model Deployment

CI/CD for models, model registry and versioning, serving infrastructure, drift detection, automated retraining, and cost monitoring.

Model Audit, Explainability and Fairness

Independent review of existing models, explainability tooling, bias and fairness testing, and documentation for regulators, auditors, or internal risk committees.

Not sure your data is good enough?

Send us a sample and a description of what you want to predict. We will tell you whether the data supports it, what accuracy is realistically achievable, and what would need fixing first. Free, and it is the answer most projects should get before any budget is committed.

Get my free data assessment

Should you train a model or just use an LLM?

This is now the first question on most AI projects, and the honest answer is that large language models have absorbed a real share of the work that used to require training a custom model. For text classification, extraction, and low-volume judgement tasks, an LLM API is often faster and cheaper than a trained model. For numeric prediction, high-volume scoring, tight latency, and anything needing explainability, trained machine learning still wins decisively.

Trained ML model LLM or foundation model
Best for Forecasting, scoring, ranking, detection, anything numeric Text understanding, extraction, classification at modest volume, unstructured judgement
Requires training data Yes, historical and labelled Often none to start
Time to first version Weeks to months Days
Cost per prediction Very low, often fractions of a cent Higher, per request, scales with volume
Latency Milliseconds Hundreds of milliseconds to seconds
Explainability Strong, feature-level attribution available Weak, output reasoning is not a reliable explanation
Regulatory acceptability Established, well-understood by auditors Still developing, harder to evidence
Runs offline or on-premise Easily Only with self-hosted open-weight models

Our recommendation: We recommend the cheaper option honestly. If your task is extracting fields from a few thousand documents a month, an LLM will likely do it today with no training data and no model to maintain, and we will tell you that. If you are scoring millions of transactions a second with a regulator asking why, a trained model is the only defensible answer. Many production systems use both, and the split should be decided per task rather than by whichever technology the vendor sells.

For foundation-model work specifically, see our generative AI development services .

How much data do you actually need?

Data requirements vary by model type and by how much variation exists in the problem. These are working guidelines rather than rules, and data quality matters more than volume in every case.

Model type Rough working minimum What matters more than volume
Tabular classification or regression Low thousands of rows, more with many features Balanced classes, consistent definitions over time, few missing values
Time-series forecasting Two to three full seasonal cycles Uninterrupted history, recorded promotions and known events
Recommendation Tens of thousands of interactions Interaction diversity, cold-start strategy for new items and users
Computer vision, custom Hundreds to low thousands of labelled images per class Label accuracy, coverage of real-world lighting and angles
Anomaly detection Large volume of normal behaviour, few anomalies needed Clean definition of normal, correct timestamps
Text classification Hundreds to thousands of labelled examples per class Label consistency between annotators

When data is short: Where data is short, the answer is usually not to abandon the project. Transfer learning, synthetic data, simpler models, and starting with a rules-based baseline while collecting real data are all valid first steps, and we scope those rather than declining the work.

Our AI and ML development process, from data audit to monitored deployment

Machine learning development runs in six stages: problem definition and baseline, data audit and engineering, exploratory analysis and feature work, model development and validation, deployment and integration, then monitoring and retraining.

Problem definition and baseline

week 1 : Translate the business goal into a prediction task, choose the metric that matters, and establish the baseline the model must beat. Deliverable: a problem statement with a success threshold you have agreed.

Data audit and engineering

weeks 1 to 3 : Assess volume, quality, labelling, consistency, leakage risk, and permitted use, then build the ingestion and cleaning pipeline. Deliverable: a data quality report and a working pipeline.

Exploratory analysis and feature engineering

weeks 3 to 4 : Understand what actually drives the outcome and construct the features that carry that signal. Deliverable: analysis findings, often valuable on their own regardless of whether a model follows.

Model development and validation

weeks 4 to 8 : Train candidate models from simple to complex, tune, and validate properly with holdout and cross-validation rather than optimistic in-sample scores. Deliverable: a validated model with measured performance against the baseline, plus explainability output.

Deployment and integration

weeks 8 to 10 : Serving infrastructure, API or batch integration, model registry, version control, and consistency between training and production features. Deliverable: a live model integrated into your systems.

Monitoring and retraining

ongoing : Drift detection on inputs and outputs, performance tracking against ground truth as it arrives, alerting, scheduled retraining, and a documented rollback path.

Find out what your data can realistically predict, before you fund a model

A 30-minute session with a senior data engineer covering your data, the prediction target, realistic accuracy, and what would need fixing first. You keep the assessment regardless.

  • Senior engineer, not a salesperson
  • Written summary within 48 hours
  • We will tell you if the data is not there yet
Book my free feasibility call

Why Choose Pixel Web Solutions for AI and ML Development

A baseline before a model

Every project starts by measuring what you do today. Without that number, nobody can tell whether a model is an improvement or an expensive tie.

Simple models first

We start with the simplest approach that could work and add complexity only when it earns its place. A well-tuned gradient boosting model beats an unnecessary neural network on most tabular problems, and it is far cheaper to run and explain.

Explainability as standard

Feature attribution and decision explanations are produced for every model, not just regulated ones. Teams trust models they can interrogate, and adoption is what determines whether the work pays back.

Deployment is in scope, always

Models are delivered running in production with monitoring, not handed over as a notebook and a set of scores.

Honest about data

If the data cannot support the prediction, we say so in week one and scope what would need to change. That conversation is cheaper for everyone than discovering it in month three.

Full ownership

Code, trained models, feature pipelines, evaluation sets, and documentation transfer to you. Your training data is never used for another client's model.

Industry-specific AI and ML development services

Industry Models we build
Financial services Credit and risk scoring, fraud detection, transaction categorisation, collections prioritisation, AML alert triage
Healthcare Operational forecasting, no-show prediction, resource planning, medical imaging support under clinical oversight
Ecommerce and retail Demand forecasting, recommendation, price and promotion optimisation, returns prediction, inventory planning
Logistics and supply chain ETA prediction, route optimisation inputs, demand planning, warehouse vision systems
Manufacturing Predictive maintenance, visual quality inspection, yield optimisation, process anomaly detection
Real estate Valuation models, lead scoring, occupancy and rent forecasting
Telecom and SaaS Churn prediction, usage forecasting, upsell propensity, capacity planning
Insurance Claims triage, fraud detection, pricing support, reserving analytics
Energy and utilities Load forecasting, asset failure prediction, consumption analytics

Regulated-sector caveats: in credit, insurance, and healthcare, models that influence decisions about individuals attract explainability, documentation, and fairness testing requirements. We scope those from the first call and produce model documentation suitable for internal risk review. Clinical applications require regulatory approval, which sits with the client and their regulator, not with us.

Explainability, fairness and model governance

Any model influencing decisions about people needs to be explainable, testable for bias, and documented. We build that in rather than retrofitting it when an auditor asks.

Feature attribution

Showing which inputs drove each prediction, at both individual and population level

Model documentation

Covering training data, methodology, performance, limitations, and known failure conditions

Fairness testing

Across relevant groups, with disparity measured rather than assumed absent

Monitoring for drift

In inputs, outputs, and performance, with alerting thresholds agreed upfront

Version control and rollback

So any prediction can be traced to the exact model version that produced it

Simpler models are easier to explain, and in regulated contexts that is often worth more than a small accuracy gain. We will recommend the explainable option and show you what the accuracy trade-off actually costs, so the decision is yours and not the modeller's.

Frameworks, platforms and tools we build on

Modelling :

scikit-learn XGBoost LightGBM CatBoost PyTorch TensorFlow Prophet statistical time-series methods

Vision and text :

OpenCV YOLO Detectron2 Tesseract Hugging Face Transformers spaCy

Data :

pandas Spark Airflow dbt Kafka PostgreSQL Snowflake BigQuery Databricks

MLOps :

MLflow Weights & Biases Kubeflow BentoML model registries feature stores

Cloud ML platforms :

AWS SageMaker Azure Machine Learning Google Vertex AI

Explainability :

SHAP LIME permutation importance partial dependence analysis

Infrastructure :

Docker Kubernetes Terraform GitHub Actions on-premise deployment edge deployment

Tools and Technologies We Use

We build, validate, deploy, and monitor models on a production-grade ML stack.

Languages

Python SQL R where the team prefers it

Classical ML

scikit-learn XGBoost LightGBM CatBoost

Deep learning

PyTorch TensorFlow

Time series

Prophet statsmodels gradient boosting with lag features

Vision

OpenCV YOLO Detectron2 Tesseract

NLP

Hugging Face spaCy NLTK

Data engineering

Airflow dbt Spark Kafka

Experiment tracking

MLflow Weights & Biases

Serving

BentoML FastAPI SageMaker endpoints batch pipelines

Explainability

SHAP LIME

Cloud

AWS Azure Google Cloud on-premise

Models delivering measurable results

42%

Higher Context Recall over Baseline

42% improvement in context recall over dense-only vector baseline · Reduced automated loan application triage errors and cut manual underwriting processing time by half · Fintech, Fine-Tuned Llama 3 & Cross-Encoder Reranker, 6-month evaluation period

$1.4M

Annual Infrastructure Cost Savings

$1.4M saved in annual cloud inference costs while maintaining task performance SLA · Enabled high-throughput real-time fraud detection without escalating API token expenditures · E-Commerce & Payments, Distilled & Quantized Mistral 7B, Q4 2025

3 Weeks

Rapid Time-to-Deployment

Compressed time-to-deployment from 4 months down to 3 weeks using standardized evaluation pipelines · Accelerated time-to-market for conversational clinical documentation features · Healthcare & Healthtech, Domain-Adapted Fine-Tuned SLM, 9-month rollout

Three ways to engage our AI and ML team

Choose a short feasibility study, a defined model build, or an embedded ML team depending on how clear your prediction target and data estate are.

  Feasibility study Model build Embedded ML team
Best for Establishing whether the data supports the idea. A defined prediction problem. An ongoing model portfolio.
Duration 2 to 3 weeks. 8 to 16 weeks. Rolling, 3 month minimum.
Deliverable Data quality report, feasibility verdict, expected accuracy range. Deployed model with monitoring. Continuous model development and maintenance.
You bring A question and access to data. A defined target and available data. Business priorities.
Commercial model Fixed fee. Fixed fee per milestone. Monthly.

The feasibility study is deliberately cheap and short, and a meaningful share of them conclude that the project should not proceed as scoped. That is a successful outcome, and far less expensive than finding out later.

How much do AI and ML development services cost?

Cost is driven mostly by data condition rather than by model complexity. A project with clean, accessible, well-labelled data costs a fraction of one that needs pipelines built and data labelled first. Modelling is often the shortest phase. We quote a fixed band per milestone after the free feasibility call.

Engagement Scope Typical timeline
Feasibility study Data audit, baseline, expected accuracy range, verdict 2 to 3 weeks
Single model build One prediction task, deployed with monitoring 8 to 16 weeks
Computer vision build Data labelling, training, edge or cloud deployment 10 to 20 weeks
MLOps platform setup Registry, pipelines, monitoring, retraining automation 6 to 12 weeks
Model maintenance Monitoring, retraining, drift response, documentation Monthly

Budget for labelling separately if the data is unlabelled. It is frequently the largest single line item on computer vision projects and the one most often left out of estimates.

Book your free AI and ML consultation

Tell us what you want to predict. In 30 minutes, a senior data engineer will assess whether your data supports it, what accuracy is realistic, what the baseline should be, and what a first model would take.

  • An honest verdict on your data, including when it is not ready
  • Realistic accuracy expectations set against a baseline
  • A recommendation, including when a simpler method would do

We reply within one business day. Your details are used only to arrange your consultation.

Prefer email? Send your brief to : sales@pixelwebsolutions.com

Frequently asked questions

AI and ML development services build machine learning models trained on an organisation's own data to forecast, classify, score, and detect, then deploy and maintain them in production. Scope typically covers problem definition and baselining, data engineering, feature work, model training and validation, explainability, deployment, and ongoing monitoring and retraining.

Machine learning is a subset of artificial intelligence. AI is the broad field of systems performing tasks that would otherwise need human intelligence. Machine learning is the specific approach of learning patterns from data rather than being explicitly programmed with rules. In commercial practice, most AI projects are machine learning projects.

Train a model for numeric prediction, high-volume scoring, tight latency requirements, and anything needing explainability for regulators or auditors, where trained models are cheaper per prediction and far easier to evidence. Use an LLM for text understanding, extraction, and modest-volume judgement tasks where no training data exists. Many systems use both, split by task.

It depends on the model type and problem variability, and quality matters more than volume in every case. As working guidelines: low thousands of rows for tabular problems, two to three full seasonal cycles for forecasting, tens of thousands of interactions for recommendation, and hundreds to low thousands of labelled images per class for custom vision. Where data is short, transfer learning, synthetic data, or starting with a simpler baseline are valid routes.

Cost is driven mainly by data condition rather than model complexity. Clean, accessible, labelled data makes a project a fraction of the cost of one requiring pipeline building and labelling first. A short feasibility study is the cheapest way to establish a real number before committing to a build

A feasibility study takes 2 to 3 weeks, a single model build 8 to 16 weeks, and a computer vision project 10 to 20 weeks including labelling. Data preparation regularly consumes more of the timeline than modelling does, which surprises teams expecting the reverse.

No responsible answer exists before the data is examined, and any firm number quoted upfront should be treated with suspicion. What we commit to is a baseline and a threshold: we measure current performance, agree the number a model must beat to be worth deploying, and report honestly against it. A feasibility study produces a realistic accuracy range for your specific data.

MLOps is the practice of deploying, versioning, monitoring, and retraining models reliably. You need it as soon as a model is making real decisions, because model accuracy decays as the world changes and an unmonitored model degrades silently rather than failing visibly. Every model we deploy ships with monitoring and a retraining plan.

Through feature attribution methods that show which inputs drove each prediction at both individual and population level, combined with model documentation covering training data, methodology, performance, and limitations. Where explainability is a hard requirement, we favour inherently interpretable models and show you exactly what accuracy that choice costs.

By measuring rather than assuming. We test performance across relevant groups, look for disparity in error rates rather than only in overall accuracy, examine the training data for historical bias that a model would otherwise learn and amplify, and document what was tested. Where disparity is found, mitigation options are presented with their trade-offs.

This is expected rather than exceptional, and it is called drift. Input distributions shift, behaviour changes, and a model trained on last year's world gets quietly less accurate. We monitor input and output distributions and performance against ground truth as it arrives, alert on agreed thresholds, and retrain on a schedule or on trigger, with rollback available.

You do. Source code, trained model artefacts, feature pipelines, evaluation sets, and documentation transfer to you on completion. Your training data remains yours throughout and is never used to build models for other clients.

Ready to find out what your data can do?

Bring the question and a sample of the data. We will come back with a feasibility verdict, a realistic accuracy range, a baseline, a timeline, and a number.

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