Our LLM development services give you a working language-model feature, from assistant to document workflow, built on your data and systems. Netofficials runs it from a written discovery scope with a named delivery lead and sprint reviews, and recommends prompting or retrieval over fine-tuning when that fits better. Send a short brief to start.
We start with the task your LLM feature must perform, the data it can draw on and how you will judge a good answer. Then we compare prompting, retrieval over your own documents and fine-tuning. Fine-tuning is used only where it is justified. If a simpler route serves you better, we will recommend it after scoping.
Our technical architect is accountable for system design, technology selection and code quality standards. You get prompts, retrieval over your content and, where scoping supports it, a fine-tuned model, all built around your workflow. Each design choice is reviewed with you before the build moves on.
We connect the LLM feature to your existing systems and test it against acceptance criteria agreed with you in writing. QA, deployment and handover run within the same cross-functional team, so you review a working feature at the end of each sprint rather than a promise.
We work with you to pin down the use case, the data the model will draw on and how you will judge a good answer. We also assess whether prompting, retrieval or fine-tuning is the simplest route that fits.
You receiveYou agree the written discovery scope
You review the proposed prompt and retrieval design, how the model connects to your systems, and the evaluation method. Our technical architect sets out the system design and technology choices for your sign-off.
You receiveYou approve the design and evaluation method before the build
The team builds the LLM feature in sprints, covering prompts, retrieval pipelines, integrations and, only where scoping justifies it, fine-tuning. You see working builds as they develop.
You receiveYou review the working build at the end of every sprint
QA and evaluation check the model's outputs against the acceptance criteria you agreed, using realistic examples from your own workflow. Weak answers and edge cases are logged and worked through with you.
You receiveYou accept the results against the agreed evaluation criteria
Netofficials plans the production deployment with you, then hands over documentation and deployment access so your team can run the LLM feature. You decide with us how monitoring and further tuning will continue after launch.
You receiveYou approve the release and handover
Send a short brief on your use case and data, and we reply with questions, a written discovery scope and a recommendation on whether prompting, retrieval or fine-tuning fits.
Your use case and your source data set most of the effort: an assistant over clean documents is a different job from a model tuned on messy, sensitive records. We settle the task, the content the model may use and the acceptance criteria in the written discovery scope. That tells us whether prompting, retrieval or fine-tuning fits, and we recommend the simpler route after scoping.
Connections to your CRM, document stores, internal APIs and login systems add scope, as do your choices on hosted models versus self-managed ones and where data may be processed. We list each system, its owner and the access we need before estimating, so integration surprises are priced into the plan rather than discovered mid-build.
Testing a language model means checking answers against agreed examples and edge cases, not only checking that screens work. We build that evaluation work into the plan, review progress with you at the end of every sprint, then hand over documentation and deployment access at release, so you can see what drives effort before you commit.
Fixed-price delivery of one defined LLM feature, such as a retrieval-based assistant or a document workflow, for teams that want a set scope and a clear handover.
A dedicated India-based team that keeps extending your LLM product, covering new use cases, evaluation and model updates, for roadmaps that will run past a first release.
Engineers with LLM and integration skills who join your in-house team and work in your repositories, suited to companies that already have product direction and need extra hands.
Netofficials confirms the stack for your LLM project in the written discovery scope, so model choice, hosting and tooling are agreed before the build starts. Our technical architect is accountable for those technology decisions. We pick the simplest route that meets your quality bar, whether that is prompting, retrieval or fine-tuning. The choice depends on your data, your existing systems and who will maintain the solution after release.
React
Node.js
Python
PostgreSQLOften prompting or retrieval-augmented generation (RAG, where the model answers from your own documents) is enough, and it is cheaper to build and easier to update than a fine-tuned model. Fine-tuning earns its place when you need a consistent output style, a narrow task done reliably, or behaviour that prompts alone cannot hold. Netofficials will recommend the simpler route after scoping, and fine-tune only where the written discovery scope shows it is justified.
Cost and timing follow the use case, the quality and volume of your data, the systems the model must connect to, how much evaluation is needed and the team you choose. A fine-tuning run adds data preparation and testing work that a retrieval-based build does not. We give no price before scoping. Scope, assumptions, review milestones and commercial terms are agreed in writing before the build, under fixed-price project delivery, a dedicated India-based team or staff augmentation.
You own the work: IP assignment is confirmed in the contract before the build begins. We can also work alongside your engineers, inside your tools and process, and agree the communication window, meeting schedule and update cadence before work starts. A named delivery lead owns day-to-day progress, and every sprint ends with a client-facing review. Send a short brief to start.
Send a short brief with the use case, the data involved and the systems it must connect to. We come back with a written discovery scope that names the simplest route that works, whether that is prompting, retrieval or fine-tuning. You see the plan, the team and the review points before any build starts, and IP assignment is confirmed in the contract first.
