Custom LLM development for retrieval, evaluation, guardrails, and integration. Prompting and retrieval come first. Fine-tuning is what we do when those two fail the same test twice.
Short answer
LLM development services are the work of putting a large language model inside a product you already run: retrieval over your own material, a way to tell whether an answer is good, limits on what the model may do, and a connection to the systems around it. That is the engagement you want from an LLM development company when a chat demo is no longer enough for customers or staff. If a hosted widget already does the job, that is usually the better buy, and we will say so.
Two shapes that are not this page
A conversation that answers and hands off to a person is AI chatbot development. A system that takes actions in your tools is AI agent development. This page is the model in the product: retrieval, the test that keeps it honest, and the integration.
Five pieces show up on almost every custom LLM build. We do not start all five at full size. The first slice is the one that can be wrong in a way you would notice.
Retrieval-augmented generation, usually called RAG: the model reads a passage from your documents, catalogue, or knowledge base and answers from that, with the source attached when the source supports it. If the passage is not there, the feature says so instead of filling the gap.
Instructions, a few real examples, and retrieval solve more of these projects than a training run does. We fine-tune when the same failure keeps showing up on the evaluation set and a prompt change does not move it. That is less often than the first sales call usually assumes.
A list of real questions with answers you already accept. Every prompt, model, or retrieval change is scored against it. Without that list, nobody can tell a tweak from a regression, including us.
What the model may say, what it must refuse, when it has to hand the thread to a person, and what gets logged. This is policy in the product, not a paragraph in a prompt that hopes for the best.
The model is called from the website, app, helpdesk, or internal tool you already have. Permissions follow the user who asked. LLM integration is this wiring, plus the failure mode when the provider is slow or down.
Some of this work is a chatbot. When the product is the conversation itself, the scope and the cost bands are written up as AI chatbot development.
The order is the point. A model choice before an evaluation set is a preference, not a result.
01
What the feature is for, and the answer that would be worse than no answer. We will say if a hosted tool already covers it.
02
Documents, tickets, or records as they actually are, including the messy ones. The evaluation set is written from those, not from a demo prompt.
03
Retrieval or prompting on that set. We change one thing at a time so a better score has a cause.
04
Refusals, handoff, logging, and the call from the product. A provider timeout has a defined path.
05
Quality against the set, latency, and cost per use. You can see all three after it ships, not only on the day of the demo.
The list is the one already used across our AI work. The choice for a project is whichever combination holds up on your questions, your privacy constraint, and the bill.
| Area | What we actually use |
|---|---|
| Models | OpenAI GPT, Anthropic Claude, Google Gemini, Llama, Mistral |
| Retrieval | Embeddings with pgvector, Pinecone, or Weaviate |
| Orchestration | LangChain, LlamaIndex, or plain TypeScript or Python |
| The product around it | Your existing web or app stack, including Next.js when we are building that too |
| Checks | An evaluation set, traces, and a cost figure per task before launch |
A scoping call, then a written proposal within 3 business days. The price is fixed for the scope we write down. Model API spend is estimated in that proposal and paid on your keys, separate from our fee.
01
Fits when
One workflow, a sample of your material, and an evaluation set. Our other AI pages describe a two-week proof of concept for a single feature. That is a typical shape for a narrow question, not a promise that every LLM project is two weeks.
02
Fits when
The feature is called from the product, with retrieval or prompting, guardrails, logging, and a support window after launch. Sibling AI pages put that production shape at several weeks to a couple of months, and name a 60-day support window on the production tier. Your proposal states the window for this scope.
03
Fits when
Several features, or a model you keep adjusting because the documents and the questions keep changing. We do not sell this to answer a question a short proof can answer.
On our AI pages, a focused proof of concept is described in an industry range of about $5,000–$25,000, and a production build with integrations, evaluation, and monitoring between $25,000 and $80,000 or more. Those are industry estimates, not a fixed Weblynx price. The number for your scope comes after the call. What moves it is how much material we have to prepare, how many systems the feature calls, whether permissions differ by user, and how strict the evaluation has to be. What drives the cost of a custom AI solution.
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They are the engineering around a large language model in a product: retrieval over your own material, an evaluation set, guardrails, and the integration into the system people already use. The model API is one part of that. The rest is how you know the answer is acceptable, and what happens when it is not.
We start with prompting and retrieval. Fine-tuning is the next step when the same failure remains on the evaluation set after the prompt and the retrieval have been given a fair chance. A lot of projects never need it. We will say so before anyone collects a training set.
Yes. LLM integration means the model is called from the website, app, helpdesk, or internal tool, with the caller's permissions, a timeout, and a path when the provider is down. We do not need to rebuild the product to do that. If the product itself is the thing that needs building, that is a separate scope and we will split it out.
We write an evaluation set from real questions and answers you accept, and we score changes against it. A demo that looks right on three examples is not that. You see the set, and you see the score before and after a change.
A focused proof of concept is described on our AI pages in an industry range of about $5,000–$25,000. A production build with integrations, evaluation, and monitoring is described between $25,000 and $80,000 or more. Those are industry estimates, not a fixed Weblynx price. Model API spend is separate and sits on your account. We send a fixed price for a written scope after a call.
A narrow proof is often a short sprint. Our other AI pages describe a two-week proof for a single feature, and a production build as several weeks to a couple of months. A two-week sprint that answers one question is not an eight-week project, and we will not sell you the longer one to answer a question the short one can answer. The schedule is confirmed once we have seen the data.
Whichever holds up on your questions at a latency and a monthly bill you can live with. We test OpenAI, Anthropic, Google, and open weights such as Llama or Mistral against the evaluation set. Brand preference is not a selection method. Privacy rules can rule a hosted API out, and we will say that early.
You own the documents, the prompts we store, the logs, and the API keys. We will tell you, before anything is sent, which provider would see a request and what that provider's terms say about training on it. If that answer is unacceptable, we pick a different host or a model that runs in your own cloud.
The parent practice: where a language feature sits next to agents, NLP, and generative work.
AI development servicesWhen the product is the conversation, including handoff to a person.
AI chatbot developmentWhen the system has to take a step in your tools, not only answer.
AI agent developmentA plain account of where a model helps a business, and where it does not.
Read the articleAdding a model at the edge of a website or app instead of replacing it.
Read the articleThe same cost bands, explained as what moves a quote up or down.
Read the articleTell us the question the model has to get right, and the answer you would not accept. We reply within 24 hours.
Talk through the use case