Netofficials provides machine learning development services for founders and product teams with a prediction, classification, recommendation or automation problem. One cross-functional team, led by a named delivery lead, builds the model into your product and takes it to production. Send a short brief and we will scope it in writing.
We start by testing whether machine learning fits your prediction, classification, recommendation or automation problem. The written discovery scope covers the data you hold, the decision the model supports and the workflow it plugs into. If a rules-based or simpler approach would serve you better, we will recommend it after scoping.
A model only pays off once people can use it. One cross-functional team covers back-end, front-end, QA and DevOps, so the model ships with the APIs, interfaces and deployment it needs. A technical architect is accountable for system design, technology selection and code quality standards.
You see working progress at a client-facing review at the end of every sprint, with a named delivery lead owning day-to-day communication. At release you receive documentation and deployment access, and IP assignment is already confirmed in your contract, so your team can run and extend the system.
We start from a written discovery scope. You and our delivery lead agree the business decision the model supports, the data you already hold and what a useful first release looks like. If a simpler rules-based approach would serve you better, we say so after scoping.
You receiveYou agree the written discovery scope
Our technical architect sets out the system design: how data reaches the model, how predictions appear in your workflow, and which technologies fit. You review the user flows and the design before any build work begins.
You receiveYou approve the design and architecture before the build starts
The cross-functional team builds the data pipelines, model training and the back-end and front-end around the model, with QA running alongside. At the end of every sprint you see progress in a client-facing review.
You receiveYou review the work at the end of every sprint
QA and the team test the model and the software around it against the acceptance criteria you agreed, so you can judge whether the system supports your workflow before it goes live.
You receiveYou accept the model against the agreed criteria
We deploy the model inside your software, then walk you through how to monitor it and when to retrain it. Documentation and deployment access are handed over at release.
You receiveYou confirm the handover before release closes
Send a short brief on the problem, the data you hold and the workflow it should improve, and we reply with questions and a proposed written discovery scope, including whether machine learning is the right fit.
Cost and timing in machine learning development start with your data: how much exists, where it lives and how clean it is. We review the prediction, classification, recommendation or automation task with you during discovery. If a rules-based approach would serve you better than ML, Netofficials will recommend it after scoping.
A model only pays off when it plugs into a real workflow. We map the systems it must read from and write to, plus the people who act on its output. Because one cross-functional team covers back-end, front-end and DevOps, integration work is estimated up front, not discovered mid-build.
The team shape and engagement model change your estimate: fixed-price project delivery, a dedicated India-based team or staff augmentation. We agree scope, assumptions, review milestones and terms before delivery starts. We also confirm IP assignment in the contract and set the communication window and update cadence early.
Fixed-price delivery of a defined machine learning system, suited to a clear problem such as prediction, classification or recommendation with a scope you can sign off.
A dedicated India-based team that keeps improving your models and the product around them, suited to ML work that continues past a first release.
Machine learning and data engineers added to your in-house team, suited to companies that already have a roadmap and need extra skills.
Netofficials picks the machine learning stack after reading your data, your existing systems and the problem you want solved, then records it in the written discovery scope for your approval. Our technical architect is accountable for technology selection and code quality standards, so the choice rests on what fits your product and your maintainers, not on what is fashionable. You see the reasoning before the build starts, which removes surprises and rework later. If a simpler approach than a model would serve you better, we will recommend it after scoping.
React
Node.js
Python
PostgreSQLThe cost and timeline of a machine learning project depend mainly on the state of your data, the systems the model has to connect to, how the output will be used in your product, and the team the work needs. Data that is scattered or unlabelled adds preparation work, and each integration adds build and testing time. We set out these assumptions in a written discovery scope, and agree scope, review milestones and commercial terms before delivery begins. Send a short brief and we will tell you what we would need to see to give an estimate.
You do. Netofficials confirms IP assignment in the contract before the build begins, so ownership of the code and the work built for you is settled before any engineering starts. At release we also hand over documentation and deployment access, so your team can run and maintain the system. If you have questions about specific assets, such as your own training data or third-party libraries, raise them in the first conversation and we will cover them in the contract.
If a simpler approach would serve you better, Netofficials will recommend it after scoping. Some problems are better solved with business rules, reporting or a workflow change than with a trained model. Discovery tests that question before you commit to a build. Tell us the decision, prediction or manual task you want to improve, and we will advise on whether machine learning fits and what a first release should include.
Tell us the prediction, classification, recommendation or automation problem you want solved, and what data you already hold. We reply with questions and a proposed written discovery scope. If a simpler approach than machine learning would serve you better, we will say so after scoping. A named delivery lead stays your contact from there, and we agree the communication window before work begins.
