InfilonInfilon

Generative AI Development Services That Go Beyond the Demo

We design, build and run generative AI applications that write, summarize and answer questions using your own data, with the guardrails a real business needs.

Summarize this week's support tickets and draft replies for the five most common issues

Example Reads your help-desk data Drafts replies, never auto-sends Every answer links to its source
Start a Generative AI Project See Use Cases

Use cases

What Generative AI Can Do for Each Team

Generative AI is most useful when it takes over the first draft of work people already do. Pick a team to see what that looks like in practice.

Choose a team

Services

Our Generative AI Development Services

Custom Generative AI Applications

Web and mobile apps built around a language model, from internal assistants to customer-facing tools, with proper logins, roles and audit logs.

LLM Fine-Tuning

When a general model doesn't know your terminology or tone, we fine-tune it on your own examples so the output needs less editing.

Learn more about LLM Fine-Tuning

Guardrails & Evaluation

Automated tests for accuracy, tone and safety, plus filters for sensitive data, so you know how the system behaves before your customers do.

Learn more about Guardrails & Evaluation

How we deliver

From First Idea to a Tool People Rely On

  1. Stage 1

    Pick one task

    We choose a single, well-defined job where generative AI can save real time, and agree how we'll measure it.

  2. Stage 2

    Prototype on your data

    Within a short sprint you can try a working version on your own documents and see where it helps and where it struggles.

  3. Stage 3

    Harden it

    We add guardrails, access controls, cost limits and automated tests, and connect it to the systems your team uses.

  4. Stage 4

    Launch and learn

    We release to a small group first, watch the results closely and improve prompts, data and models based on real use.

Built responsibly

The Risks Everyone Worries About, and What We Do About Them

Generative AI has real weaknesses. Pretending otherwise is how projects fail. Here is how we deal with the four we get asked about most.

The risk

Confident but wrong answers

What we do

We ground answers in your own content with RAG, show sources, and test accuracy against real questions before launch.

The risk

Sensitive data leaking

What we do

Personal and confidential data is masked or kept in your own cloud, and we use enterprise API terms that exclude training on your data.

The risk

Costs that creep up

What we do

We set usage limits, cache repeated requests and route simple tasks to smaller, cheaper models.

The risk

Off-brand or inappropriate output

What we do

Tone guidelines, content filters and human review steps keep what goes out in line with your standards.

Choosing a model

GPT, Claude, Gemini or Open Source?

We are not tied to any AI provider. This is roughly how we think about the options before testing them on your actual task.

Comparison of generative AI model options
OptionExamplesBest forWatch out for
Hosted modelsOpenAI GPT, Anthropic Claude, Google GeminiGetting started quickly, complex reasoning, general writingPer-request costs and where your data is processed
Open-source modelsMeta Llama, Mistral, QwenSensitive data, on-premise hosting, predictable costs at scaleYou need infrastructure to run and update them
Fine-tuned modelsEither of the above, trained on your examplesSpecialist language, consistent format and toneNeeds good example data and periodic retraining

FAQ

Generative AI Development FAQs

What is generative AI development?

Generative AI development is building software that creates new content, such as text, summaries, answers, code or images, using large language models and similar AI. In practice it means choosing the right model, connecting it to your data, designing prompts and guardrails, and building it into a product your team or customers use.

How is generative AI different from traditional AI?

Traditional AI and machine learning usually predict or classify, for example forecasting demand or flagging fraud. Generative AI produces something new, like a reply to a customer or a summary of a contract. Many useful systems combine both.

Which is better for us, GPT, Claude, Gemini or an open-source model?

There is no single best model. Hosted models are quick to start with and very capable; open-source models give you more control over data and cost. We usually test two or three options on your actual task and compare accuracy, speed and cost before recommending one.

How do you stop generative AI from making things up?

We can't make a language model perfect, but we can make it reliable enough for the job. We ground answers in your own documents, require sources, limit what the model is allowed to answer, test against real questions and keep a person in the loop wherever mistakes would be costly.

How long does it take to build a generative AI application?

A working prototype on your own data can usually be ready in a few weeks. A production system takes longer because of integrations, security reviews and testing. We agree the scope and timeline with you after a short discovery phase.

Is our data safe when we use generative AI?

Yes, when it is set up properly. We use enterprise agreements that stop providers from training on your data, mask personal information, and can host open-source models in your own cloud when data must not leave it.

Let's find the first task worth automating

A 30-minute call is usually enough to tell whether generative AI is a good fit, and what a sensible first project would look like.

Book a Call

Useful to have ready for the call

  • 1The task you'd like AI to help with
  • 2Roughly how often it happens and who does it
  • 3Where the related data or documents live
  • 4Any rules on privacy or where data can be stored

None of it is required. We can work it out together.