Our generative AI development services take one business workflow, such as an assistant, document drafting or search over company knowledge, from written discovery scope to a working first release. Netofficials is for B2B teams who want an accountable delivery lead and a reviewed sprint cadence. Send a short brief to start scoping.
You bring a workflow such as an assistant, document drafting or search over company knowledge. We turn it into a written discovery scope: who uses it, what the model should produce, what data it needs and what the first release includes. If a simpler or non-generative approach would serve you better, we will recommend it after scoping.
Our technical architect is accountable for system design, technology selection and code quality standards. You see how the generative AI feature connects to your existing systems and data, and where review steps and fallbacks sit, before the build starts, so fewer surprises reach development.
One cross-functional team builds the feature in sprints, and you review at the end of each one. Acceptance criteria are agreed up front, so QA checks the output against them rather than a vague sense of quality. At release you receive documentation and deployment access, and IP assignment is already in the contract.
We write down the one business workflow the generative AI feature will serve, who uses it, which of your data it draws on and what a good answer looks like. If a simpler or non-generative approach would serve you better, we say so at this point.
You receiveYou agree the written discovery scope
Our technical architect sets out the system design and technology selection, and we draft the prompts, retrieval approach and user experience. You review how the feature behaves, including what it does when it cannot answer.
You receiveYou approve the design before the build starts
One cross-functional team builds the generative AI feature and connects it to your systems in sprints. Your named delivery lead keeps you updated on progress and on any decisions that need your input.
You receiveYou review the working feature at every sprint end
QA tests the feature against the acceptance criteria agreed at discovery, using realistic inputs from your workflow, including awkward ones. You see the results and the open issues before anything goes live.
You receiveYou accept the output against the agreed criteria
We deploy the generative AI feature to production and hand over documentation and deployment access. You review the release plan and agree how the feature will be monitored and improved after launch.
You receiveYou confirm the handover is complete
Send a short brief on the workflow you want to improve, and we reply with questions and a written discovery scope that sets out the first release and the team to deliver it.
The use case sets the biggest share of the estimate: an assistant, document generation or search over company knowledge each needs different design and testing. We agree acceptance criteria for output quality before the build, and where a simpler or non-generative approach would serve you better, we will recommend it after scoping.
Your content sources, access rules and the systems the feature must connect to drive both effort and risk. We map them during the written discovery scope, so the estimate reflects your real data and existing stack rather than assumptions made later.
Generative output needs more QA than fixed logic, and that testing is priced into the plan. We agree the communication window and update cadence before work starts, then hand over documentation and deployment access at release so your team can run what we build.
Fixed-price delivery of one generative AI use case, such as an assistant or document generation, suited to a buyer who wants a defined scope and a clear handover.
A dedicated India-based team that keeps extending your generative AI features sprint by sprint, suited to a product with a continuing roadmap.
Engineers who join your existing team to build or tune generative AI features, suited to a buyer who already has the product owner and process in place.
Netofficials does not start from a fixed model or vendor. We choose the language model, retrieval approach and surrounding application stack in the written discovery scope, based on your use case, the data the model may see, your existing systems and who will maintain the product after release. A technical architect is accountable for that selection and for code quality, so you get a reasoned recommendation rather than a default. If generative AI is the wrong fit for your workflow, we will recommend an alternative after scoping.
React
Node.js
Python
PostgreSQLCost and timeline are driven by the use case, the data and systems the model must connect to, and how much review and testing the output needs. A single assistant over one document set takes far less work than a multi-step workflow that writes back into your CRM or ERP. We confirm the estimate and assumptions in a written discovery scope before delivery begins, so you can compare it against your budget before committing.
We agree acceptance criteria and QA checks with you before the build, so output quality is judged against written tests rather than a demo. Each sprint ends with a client-facing review where you see the behaviour on real examples. If scoping shows that a simpler or non-generative approach would serve you better, we will recommend it.
You own the work: IP assignment is confirmed in the contract before the build begins, and we hand over documentation and deployment access at release. A named delivery lead manages day-to-day progress, and we agree the communication window and update cadence with you before work starts. You can engage us as fixed-price project delivery, a dedicated India-based team or staff augmentation inside your own tools. Send a short brief through the contact form to start.
Send us the workflow you want generative AI to improve. We review it with you, set out the written discovery scope and agree an estimate before delivery begins. If a simpler or non-generative approach would serve you better, we will say so after scoping. You deal with one named delivery lead, and IP assignment is confirmed in the contract before the build starts.
