AI & Intelligent Solutions · Service 05 of 14

LLM Application Development production apps, not prompt demos

O Clock Software, headquartered in Chennai, India, builds production applications on large language models such as GPT, Claude, Gemini and Llama. We choose the model per task, keep the model layer swappable, and add the evaluation, monitoring and security that a demo skips.

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  • 2009Founded in Chennai
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What we build

LLM Application Development: what we build

Production applications on GPT, Claude, Gemini, Llama and other models. Here are the six kinds of LLM application development work we are asked for most often.

Choosing the model

Which kind of language model fits your application?

The model is a design decision, not a default. We compare these options on your own examples, and often combine them in one application.

Commercial API model Open-weight model, self-hosted Small fine-tuned model
Examples GPT, Claude, Gemini Llama, Mistral, Qwen A compact model tuned on your examples
Best for Broad reasoning and the fastest start Data that must stay in your environment High-volume, narrow, repeated tasks
Data residency Provider regions under enterprise terms Entirely in your cloud or data centre Entirely in your environment
Operational effort Low Higher: hosting, scaling and GPUs Medium: training and evaluation pipeline
Watch out for Provider changes and usage limits Weaker on complex reasoning Narrow skills; retrain when needs change

How we deliver

How we deliver LLM Application Development

  1. 01

    Task breakdown

    Split the job into steps a model can do reliably.

  2. 02

    Model bake-off

    Shortlisted models compared on your own examples.

  3. 03

    Prompts, tools and schemas

    Instructions, tool access and output formats designed and versioned.

  4. 04

    Evaluation and red-teaming

    Test suites plus deliberate attempts to break the rules.

  5. 05

    Deploy with observability

    Tracing, quality metrics, fallbacks and alerts in production.

Technology

Technology for LLM Application Development

Chosen per project for fit, maintainability and your team's skills.

Models

  • OpenAI GPT
  • Anthropic Claude
  • Google Gemini
  • Meta Llama
  • Mistral

Frameworks

  • LangChain
  • LangGraph
  • LlamaIndex
  • DSPy
  • Structured outputs

Serving

  • Azure OpenAI
  • AWS Bedrock
  • Google Vertex AI
  • vLLM
  • Ollama

Observability & evaluation

  • LangSmith
  • Langfuse
  • Promptfoo
  • OpenTelemetry

Straight answers

Is LLM Application Development right for you?

We would rather recommend less than sell you more. Here is how we judge it.

A good fit when

  • The task is language-heavy: reading, writing or reasoning over text
  • Quality can be measured against examples
  • You need production reliability, not a pilot
  • The feature must live inside an existing application

  • A chat tool already does the job

    If a few people need occasional help, an off-the-shelf assistant may be enough.

  • Answers must come from your documents

    Start with RAG Knowledge Assistants, which add retrieval and citations.

  • The task is numeric prediction

    Forecasting and scoring are machine learning problems. See Predictive Analytics.

Use cases by industry

LLM Application Development by industry

The same capability, shaped by the data, rules and users of each sector.

Related work

Where we have shipped this

Platforms in production that use this capability. See all work

Related services

Often combined with LLM Application Development

Solutions built on this service

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FAQ

LLM Application Development FAQ

Which LLM is best for business applications?

There is no single best model; the right choice depends on the task, accuracy needed, response time, data residency and volume. We compare shortlisted models on your own examples, pick the one that meets the target, and keep the option to switch later.

Can we run an LLM in our own environment?

Yes. Open-weight models such as Llama and Mistral can be hosted in your cloud account or data centre, so no data leaves your environment. They need GPU hosting and more operational effort, which we set up and can manage.

How do you prevent lock-in to one LLM provider?

We put the model behind a single internal interface, keep prompts and evaluation sets in your codebase, and test each feature against at least two models. Switching then becomes a configuration and testing task, not a rewrite.

What is LLM evaluation?

LLM evaluation is testing a language model application against a fixed set of real inputs with expected outputs or scoring rules. It catches quality drops when prompts, models or data change, the way automated tests catch regressions in normal code.

How do you secure an LLM application?

We defend against prompt injection, restrict which tools and data the model can reach, mask personal data, validate outputs before they are used, and log requests for audit. Security testing includes deliberate attempts to break the rules.

Do you fine-tune LLMs?

Yes, when it helps. Fine-tuning suits consistent formats, tone or narrow tasks at high volume; for knowledge that changes, retrieval (RAG) is usually better, so we test prompting and retrieval first.

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