LLM integration
We connect models from OpenAI, Anthropic, Google or an open-source option to the software you already run. The work is the layer around the model: prompts, structured output, validation, cost control and a way to change provider later.
Sends a task with only the data the model needs.
Builds the prompt, calls the model and checks what comes back.
A hosted API or a model you run yourself. Replaceable.
Your application talks to the layer, not to one provider.
01 / Is this the one
This is the simplest of the AI services. It fits when the task is narrow and you can say what a correct output looks like.
02 / What the work covers
Calling a model takes an afternoon. Making the feature dependable takes the rest of the project.
Real inputs with the outputs you expect, used to compare models and catch regressions.
Candidates compared on quality, price, speed and data terms. No default favourite.
Prompts, structured output, validation, retries and fallbacks behind one interface in your code.
Only the fields a task needs are sent. A fast decision model can read each request first and send it to the cheapest model that passes your tests; we have started using Jev for that step. Cost, speed and failures are tracked for each feature.
03 / Limits and risks
The same input can give different output. Tests reduce surprises. Critical decisions still need a rule or a person.
Models are retired and repriced on the provider’s schedule. Switching is practical, but each switch needs retesting.
Model usage is billed by the provider, not by us. We estimate it in the pilot. Real traffic decides the figure.
04 / Built and operated by NSDBytes
Our own AI product. India market news as short briefings on Telegram and Web Chat, each story linked to the listed company, commodity, index or currency it concerns. It shows a team that keeps an AI product running every day, not only a prototype.
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Add this address to Claude, Cursor or any MCP client.
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A client project. FocusPulse is our own product, and we have no published case study for a client LLM integration.
05 / What to do next
The first step is a free scoping call about one feature. Bring a few real inputs and the output you would want back. A written estimate follows.
A little clarity
Often it should not. If a ready-made tool does the job, use it. A custom integration is worth it when the feature must live inside your own product, use your own data and rules, or meet data-handling terms that a general tool cannot.
We cannot make a third-party service private, so we design around it. Only the fields a task needs are sent, sensitive values can be masked first, and we go through each provider’s data terms with you. Where data must not leave your environment, a model you host is the option.
Your application calls an integration layer, not the provider. When a model is retired, the change is made in that layer and the test set is run again before release. It still takes work, because a new model can behave differently on the same prompt.
The one that passes your tests at a cost and speed you accept. We compare candidates on your own examples. Many products end up with a smaller, cheaper model for routine requests and a stronger one for the hard cases, with a routing step in front that decides which is which.
It depends on how many features use the model and how strict the checks on its output must be. First releases at NSDBytes start from $5,000. The project cost estimator gives a range for an AI build, and a fixed quote follows a free scoping call. Hosting and AI model usage are billed by their providers and are separate.
Start wherever you are
We’ll help you define the task, the test and the smallest integration worth shipping.