We build custom machine learning models and create synthetic datasets for companies with a specific problem to solve.
Synthetic records can reproduce useful patterns. Utility and privacy need separate checks.
Meet Artificial Modelling. See how we develop custom AI models and synthetic datasets, from a clear scope to a usable handover.
Every business has a different problem. Your AI should be built to fit.
At Artificial Modelling, we develop custom models for forecasting, language, vision and recommendation. Built around your data, your goals, and your constraints.
When useful examples are missing, we create synthetic datasets for training, testing, and edge-case coverage.
First, a clear scope. Then, a baseline. We develop the work, evaluate the result, and document the handover.
You receive evaluation that explains the result. Relevant metrics. Realistic tests. Error analysis. And known limitations.
Models or datasets, reports, interfaces, and documentation. Deliverables agreed before we build.
What are you trying to build? Artificial Modelling. Built to fit.
Tell us the decision you need to make, the task you need to automate, or the data you’re missing. We’ll scope the work from there.
Machine learning built around your data, constraints and success criteria.
Explore model developmentFrom a first baseline to a model your team can run. We scope the data preparation, training, evaluation and integration together.
Purpose-built datasets for training, testing and covering the cases you’re missing.
Explore synthetic dataGenerate records and examples to a defined specification. Check their structure, coverage and usefulness before they enter your workflow.
A working milling-machine benchmark. Generate synthetic telemetry, train a failure classifier and compare it with a source-data model on the same test records.
An attributed public synthetic benchmark. All computation runs in your browser. Inspect the method, change the recipe and download the measured results.
These are example project types, not client case studies. The right approach depends on your data and the result you need.
Forecast volumes, prioritise work or flag patterns that need a closer look. Start with historical records and compare the model against the way your team works today.
Every project starts with a feasibility check. Some problems need better data before they need a model.
You should know what’s being built, how it will be judged, and what you’ll receive before development starts.
Discuss your workflow, available data, constraints and intended users. Agree on what a useful result looks like.
Output: a scoped project brief
Review data quality and build a baseline. Identify gaps, leakage risks and cases that need extra coverage.
Output: a feasibility assessment
Train the model or generate the dataset. Run agreed checks, inspect errors and document the trade-offs.
Output: tested project artifacts
Package the result for your team. Document how to run it, its limitations and the next steps for integration.
Output: a documented handover
Deliverables are agreed in the scope. We aim for a handover your team can understand, reproduce and build on.
Discuss your requirementsArtifacts in the formats your team can use, with a clear version and specification.
Relevant metrics, representative test cases, error analysis and known limitations.
Training or generation scripts and instructions, where included in the scope.
How to run the work, what to watch for and what future changes may require.
Synthetic does not automatically mean anonymous. Data access, permitted use, privacy checks and ownership are agreed before project data is shared.
Scope, milestones, timeline and fees are quoted after an initial discussion. No fixed package needs to fit every project.
For a team deciding whether a model or synthetic dataset is a sensible next step.
For a clear use case with agreed deliverables and a way to measure success.
For additional evaluation, new data, model revisions or integration after a build.
A few practical things to know. For anything specific to your project, start a conversation.
No. Start with the task, the current process and the result you need. We can compare a simple baseline with more complex options during scoping.
Yes, subject to a data review and agreement on permitted use. We look at formats, labels, sample size, quality and access constraints before choosing an approach. You can describe the data in your first enquiry without sending it.
It depends on the use case. Synthetic data can help with testing, coverage gaps and augmentation. It should be evaluated against the intended task and representative real data where available. It is not a guarantee of accuracy or privacy.
Ownership and usage rights are set in the project agreement. We identify third-party models, libraries and source data with their applicable licences. Any transfer of custom deliverables is defined before work starts.
Confidentiality, access and data handling requirements can be discussed during scoping. Share a high-level problem description first. Do not put confidential records or personal data into the enquiry form.
Fees and timelines depend on the data, technical uncertainty, integration needs and deliverables. After an initial discussion, the proposal sets out milestones, assumptions, fees and the expected schedule.
Integration can be included in the scope. That might mean a batch pipeline, an inference API or a documented interface for your developers. Hosting, ongoing operation and monitoring responsibilities need to be agreed separately.
A few sentences are enough. Tell us what your company needs, what data you have and where you’re getting stuck.
aditya@artificialmodelling.in