AI & Intelligent Solutions · Service 02 of 14

Generative AI Development Services useful output, with guardrails built in

O Clock Software, headquartered in Chennai, India, builds generative AI into products and workflows: drafting, summarising, rewriting, translating, generating images and assisting with code. We design for the failure cases, such as made-up facts, prompt injection and off-brand tone, as carefully as for the demo.

  • 2009Founded in Chennai
  • 1,000+Projects delivered
  • 12+Countries served
  • 100+Engineers in-house
  • 4.6★Google rating · 59 reviews

Recognized & Reviewed On

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What we build

Generative AI Development: what we build

Text, image and content generation built into real products, with guardrails. Here are the six kinds of generative AI development work we are asked for most often.

Risk and guardrails

The six generative AI risks we design against

Every generative AI feature we ship has an answer for each of these before launch. This is the checklist we walk through with your team.

Risk What it looks like Guardrail we build
Made-up facts Confident answers with no basis in your data Grounding in your sources, citations and permission to say it does not know
Prompt injection Users or documents trick the model into ignoring its rules Input filtering, separated system instructions and restricted tool permissions
Data leakage Sensitive data appears in output or leaves your environment Personal-data masking, access control, enterprise or self-hosted models
Off-brand or unsafe tone Output that does not sound like you, or is inappropriate Style guides in prompts, output classifiers and blocked topics
Broken format Output that breaks the systems that consume it Schema-constrained output with automatic validation
Silent quality drift Quality drops after a model or prompt change An evaluation set rerun on every change before release

How we deliver

How we deliver Generative AI Development

  1. 01

    Use case and tone

    Define the task, audience, tone and what a good output looks like.

  2. 02

    Prompt and workflow design

    Single prompts or multi-step chains, with your examples built in.

  3. 03

    Grounding and guardrails

    Connect your sources and add the six guardrails above.

  4. 04

    Evaluation

    Automated checks plus human ratings on real inputs.

  5. 05

    Launch with review

    Start with human approval; reduce it as quality is proven.

Technology

Technology for Generative AI Development

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

Models

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

Orchestration

  • LangChain
  • LlamaIndex
  • Structured outputs
  • Function calling

Evaluation & safety

  • Promptfoo
  • Langfuse
  • Custom evaluators
  • Human rating queues

Hosting

  • Azure OpenAI
  • AWS Bedrock
  • Google Vertex AI
  • Self-hosted GPUs

Straight answers

Is Generative AI Development right for you?

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

A good fit when

  • Your team writes or reads similar content every day
  • A person can review output before it is used
  • You have a style guide or good examples to learn from
  • Volume is high enough that drafting time matters
  • Every output must be right with no review

    Generative models are probabilistic. For final legal, medical or safety decisions, keep a person in the loop or use deterministic software.

  • You need final creative work at a high bar

    Generative AI is strong at first drafts and variations, not at replacing a skilled writer's or designer's final judgement.

  • Answers must come from your documents

    That is a retrieval problem first. See RAG Knowledge Assistants.

Use cases by industry

Generative AI 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 Generative AI Development

FAQ

Generative AI Development FAQ

What can generative AI do for a business?

Generative AI drafts, summarises, rewrites, translates and structures content, and can generate images and code. It helps most with repetitive reading and writing work, such as support replies, reports and document summaries, with a person approving the output.

How do you stop generative AI from making things up?

We ground answers in your own sources, ask the model to cite them, allow it to say it does not know, and test against real questions before launch. Where errors matter, a person reviews output before it is used.

Is our data safe with generative AI?

Yes, when the system is designed for it. We use enterprise model terms that exclude your data from training, mask personal data before it reaches the model, enforce access rules, and can self-host open models when data must stay in your environment.

What is the difference between generative AI and an LLM?

A large language model (LLM) is one type of generative AI model, specialised in text. Generative AI is the wider category, which also includes image, audio and code generation models.

Can generative AI write in our brand voice?

Yes. We use your style guide and approved examples in prompts, and fine-tune a model when a consistent voice is needed at high volume. Output is checked against tone rules before it reaches customers.

How do you measure generative AI quality?

We build an evaluation set of real inputs with reference outputs or scoring rubrics, combine automated checks with human ratings, and rerun it on every prompt or model change so quality never drops silently.

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