AI Development Company

Which kind of AI does your product actually need?

Start from the job the AI must do, not from the technology. Most AI briefs fall into one of seven kinds of work, each with its own page. Stay here when your product combines several - a support assistant that also routes tickets, for example - and you want one team to own the whole system.

• Answering questions over your documents, drafting or summarizing text: an LLM application, usually with retrieval (RAG) over your own content.
• Taking actions across your tools - booking, updating a CRM, filing a ticket: an AI agent that calls your APIs under set permissions.
• Answering customers on your website, WhatsApp or in-app chat, with a handoff to a person: a chatbot.
• Predicting a number or a category from your records - churn, demand, fraud, price: a machine learning model trained on your history.
• Reading images or video - defects, documents, shelves, vehicles: computer vision.
• Classifying, extracting or translating text at volume, with no chat interface: NLP.
• Understanding what already happened in the business through dashboards and reports, with no model at all: data analytics.

Is a hosted model API enough, or do you need a custom model?

A hosted model from the GPT, Claude, Gemini or Llama families, called through an API, is enough when the task is general language work - drafting, summarizing, answering from documents supplied at query time - and your volume keeps per-token bills reasonable. It is the fastest route and often the right first release.

A fine-tuned or custom-trained model earns its extra cost in three situations: the task depends on patterns only your data holds, such as your fraud signals or your own document layouts; the data is not allowed to leave your infrastructure; or your volume makes per-call pricing dearer than running your own model on GPUs you control. We tell you which situation you are in before quoting, and we often start on an API and move to a custom model once real usage justifies it.

Is your data ready for an AI project?

Data readiness decides an AI timeline more than model choice does. Before scoping we check four things with you. If one fails, the first milestone is a data pipeline, not a model, and our data engineering team builds it.

• Access: the data sits in a database, warehouse or document store we can read - not only in inboxes and spreadsheets.
• Labels: for a prediction or vision model, past examples carry the right answer, such as fraud or not, defect or not. An LLM app over documents needs no labels, but it needs the documents to be current.
• Coverage: the examples include the rare cases the model will meet in production, not only the common ones.
• Rights: you may use the data for this purpose, and personal data is handled under the rules that apply to you. We build to the controls GDPR or HIPAA require; certification remains your audit.

How much does AI development cost, and what drives the bill?

A ready-made AI platform - the ChatGPT clone (from $2,799) or the HeyGen clone (from $3,699) - ships in 6 working days at a fixed catalogue price. A custom AI MVP typically runs $8,000-$25,000 and takes 2-8 weeks. Ongoing AI work runs from $2,299/month.

Inside the custom range, the price moves with data preparation and labeling, the model route, how accuracy is evaluated, the number of systems the AI connects to, and where inference runs. Plan for the running bill as well as the build: hosted model APIs charge per token or per request, and a self-hosted model needs GPU capacity sized to your traffic. We estimate the monthly running cost in writing before you commit.

How do you know an AI system works before it goes live?

Accuracy belongs to a model on a particular dataset, so a single accuracy figure quoted across an agency's projects tells you nothing about yours. Before launch we agree a test set drawn from your own data and the measure that fits the decision: precision for a fraud flag that blocks payments, recall for a defect detector that must not miss faults, answer correctness and source citations for an LLM assistant.

The model has to meet that target on data it never saw in training. After launch the same measures run on live traffic, and drift alerts tell you when your data has moved away from what the model learned, so retraining is planned rather than discovered through complaints.

Should you hire an in-house AI team or an AI development company?

An in-house team makes sense once AI is the core of your product and there is a steady stream of models to build, evaluate and retrain. Before that point, a single AI hire faces the whole stack alone: data pipelines, training, evaluation, serving, monitoring and the app around the model. Recruiting data scientists, ML engineers and MLOps engineers one at a time also takes months.

An AI development company brings those roles for the build and hands over the code, training pipelines, model cards and runbooks, so a later in-house team can take over without starting again. A common path: an outside team builds and runs the first version, you hire one or two engineers who learn the system from the handoff documents, and the outside team steps back to a smaller retainer.