Netofficials builds recommendation engines that suggest the right products, content or next actions inside your own platform, fitted to your data and business rules. One cross-functional team works under a named delivery lead, and you own the code. Send us a short brief and we will return a written discovery scope.
We work out which behaviour and catalogue data can drive useful suggestions: views, purchases, ratings, search terms, content attributes. You get a clear view of what is usable now, what needs cleaning and what to collect, before anyone builds a model. That keeps weak data from becoming rework later.
We design how suggestions are chosen and ordered, from rule-based and popularity approaches to collaborative filtering and content-based models, and add your business rules such as stock, margin or exclusions. If a simpler approach fits your catalogue and traffic better, we will recommend it after scoping.
We connect the engine to your product pages, app, emails or admin tools through APIs, then test suggestion quality and set up measurement such as A/B tests, so you can judge results on your own numbers. Our architect is accountable for system design and code quality.
You and our team agree what the engine should recommend (products, content or next actions), where the suggestions will appear and which data signals are available. We review your catalogue, behaviour data and business rules, and write them into a discovery scope.
You receiveYou agree the written discovery scope
You review how the ranking approach, data pipeline and placement in your product will work, including rules such as exclusions, promotions and fallbacks for new users. Our technical architect sets out the system design and technology choices.
You receiveYou approve the design before development starts
The cross-functional team builds the data pipeline, the ranking logic and the integration with your platform in sprints. The named delivery lead keeps you updated on the cadence agreed before work begins.
You receiveYou review progress at the end of every sprint
QA checks that the engine returns sensible results across typical, edge and cold-start cases, and that it behaves correctly inside your product. You decide on acceptance against the criteria agreed in the scope, including how recommendation quality will be measured.
You receiveYou accept the recommendation engine against the agreed acceptance criteria
Netofficials deploys the recommendation engine into your platform and hands over documentation and deployment access. You check that your team can run, monitor and maintain it.
You receiveYou confirm the handover of documentation and access
Send a short brief on your catalogue, your data and where suggestions should appear, and we reply with questions and a written discovery scope for the recommendation engine.
Your scope starts with the data the engine learns from: purchases, views, searches, ratings or catalogue attributes. We also pin down the rules it must respect, such as stock limits, exclusions, promoted items and new-user handling. Settling these in the written discovery scope means you know what the first release ranks and why before the build begins.
Data quality, volume and the systems the engine must connect to are the biggest cost drivers. We check your catalogue, event tracking and storefront or app, then agree where suggestions appear: product pages, emails, feeds or in-app. Where data is thin, we may recommend a simpler approach first, such as rules-based or popularity-based suggestions.
You decide how relevance gets judged: offline checks against past behaviour, live A/B tests or both. QA covers ranking behaviour, edge cases and load at your expected scale. At release, you receive documentation and deployment access, so your team can run and extend the engine.
Project delivery, on fixed-price terms, suits a recommendation engine with a clear first release, such as suggestions on one storefront, content feed or marketplace.
A dedicated India-based team suits businesses that expect to keep extending recommendations over time, adding placements, data sources and ranking changes as results come in.
Staff augmentation suits you if your own team owns the product and needs extra engineers for recommendation work inside your existing tools and process.
Your recommendation engine's stack is chosen from your data, not from a house default. In the written discovery scope, we confirm the tools for event tracking, model training, ranking and serving against your catalogue size, your existing platform and the team that will maintain the engine after release. That removes mid-build technology swaps and rework. Our technical architect is accountable for the technology selection, so you know who answers for each decision. If a simpler rules-based approach would serve you better than a machine learning model, we will say so after scoping.
React
Node.js
Python
PostgreSQLCost and timeline are driven by your data quality, the number of systems the engine must connect to, and the scale of catalogue and traffic it has to serve. A catalogue with clean interaction history and one clear integration point takes less effort than scattered data across several tools. We do not quote prices on this page. After a written discovery scope, you receive an initial estimate with its assumptions, and any change outside that scope goes through a change request.
You own the code. IP assignment is confirmed in the contract before the build begins, so ownership is settled before any engineering starts. At release we hand over documentation and deployment access, so your team can run, retrain and extend the recommendation engine without depending on us. Throughout the project, a named delivery lead is your point of contact.
A custom build suits you when suggestions must reflect your own data, business rules and platform, and off-the-shelf tools cannot do that. If a simpler approach fits better, such as rule-based suggestions or a configured third-party tool, we will recommend it after scoping. Send a short brief about your catalogue, your data and your goal, and we will reply with questions and a proposed scope.
Send us your catalogue, content library or marketplace and the suggestions you want users to see. We review your data and business rules, then return a written discovery scope with the signals, ranking approach and integration points mapped out. If a simpler approach fits better, we will recommend it after scoping. Your named delivery lead keeps you informed throughout, and IP assignment is settled in the contract before any build begins.
