Netofficials delivers predictive analytics services for B2B teams that hold business data and need forecasts, churn signals or risk scores they can act on. We scope the question, data and success measure in writing, then build and deploy the model with the software around it. Send a brief to start your discovery scope.
We start by writing down the decision your forecast or risk score has to support, the data sources behind it and how you will judge success. Discovery also tests whether your data can carry the model. If it cannot, we tell you plainly, and if predictive analytics is the wrong fit we recommend another approach after scoping.
Netofficials builds the predictive model for the way your team will use it: churn scores in a customer view, demand forecasts in planning tools, risk flags in an approval flow. Our technical architect is accountable for system design, technology selection and code quality standards, so the model is not left as an experiment in a notebook.
One cross-functional team tests the model together with the software around it, then deploys it. You review progress at the end of every sprint. At release you receive documentation and deployment access, with IP assignment already confirmed in your contract, so you can run and extend the result yourself.
We work with you to pin down the business question, the data sources and the success measure the model will be judged on. If your data is not ready or analytics is the wrong fit, we tell you here and recommend another approach.
You receiveYou approve the written discovery scope
We design how predictions reach your team, from data pipeline and model approach to the screens, alerts or APIs that put a forecast or risk score into a live workflow. You review the plan with our technical architect.
You receiveYou review the design before development starts
Our cross-functional team develops the data pipeline, trains and tunes the model, and builds the software around it. Each sprint ends with a client-facing review, so you see working output instead of waiting for a final reveal.
You receiveYou review progress at the end of every sprint
We test the model against the success measure agreed in discovery, using data it has not seen, and QA checks the surrounding software and integrations. You see where the model performs well and where it has limits.
You receiveYou accept the model against the agreed success measure
Netofficials deploys the model into the live workflow it was built for and hands over documentation and deployment access. You confirm who watches the predictions after launch and how the model will be updated.
You receiveYou confirm the handover of documentation and access
Send a short brief on the decision you want to improve and the data you hold, and we reply with questions and a written discovery scope covering the business question, data and success measure.
Where your data lives and how clean it is shapes the estimate more than any other factor. In discovery we review your sources, gaps and history against the business question, then tell you plainly whether the data can support a usable model. If it cannot, you hear that before build budget is committed.
A forecast, churn score or risk model costs more to build when it must read from several systems, write results back into your software or retrain on a schedule. We price the model and the integration work separately in the written scope, so you can see which part of the estimate comes from each and adjust the first release.
Your cost also depends on how you work with us: fixed-price project delivery, a dedicated India-based team or staff augmentation. We agree the communication window, update cadence and handover of documentation and deployment access up front. If another approach would serve you better than predictive analytics, we will recommend it after scoping.
Fixed-price delivery of one forecasting, churn, demand or risk model, suited to buyers who can name the business question and want a defined outcome.
An India-based team that keeps improving your models and the software around them, suited to roadmaps with several predictions to build and retrain.
Individual engineers who add data, back-end or QA capacity to your in-house analytics team, suited to buyers who already own the plan.
Netofficials builds predictive models in Python and stores the data they learn from and write back to in PostgreSQL, so your forecasts run on tools your engineers can read, maintain and extend. We confirm the exact stack in the written discovery scope, after checking your data sources, your existing systems and who will maintain the product after release. If your team already runs a different database or cloud setup, we say so at scoping and plan around it.
React
Node.js
Python
PostgreSQLNetofficials answers that during discovery, and we say so plainly if the data is not ready. We review the business question, the data sources you hold, how complete and consistent the history is, and the success measure the forecast or risk score must meet. The findings go into a written discovery scope. If predictive analytics is not the right fit, we recommend another approach after scoping.
Cost is driven by the number and quality of your data sources, the integrations needed to feed predictions into your live systems, the complexity of the model, and the team model you choose: fixed-price project delivery, a dedicated India-based team or staff augmentation. We set out scope, assumptions and commercial terms in writing before the build begins, so you can compare options without surprises.
You own the result: IP assignment is confirmed in the contract before the build begins. A named delivery lead handles day-to-day progress and communication, and you get a client-facing review at the end of every sprint. We agree the communication window, meeting schedule and update cadence with you up front, and hand over documentation and deployment access at release.
Tell us the decision you want to improve, whether that is churn, demand, risk or something else. We review your data and success measure, then set out a written discovery scope covering what a model can realistically do for you. If analytics is not the right fit, we will recommend another approach once scoping is done. Your named delivery lead keeps the conversation moving from the first brief.
