AI Development Companies: Hire a Vetted AI Team

What AI development covers

AI development is the engineering around a model rather than the model itself. Most commercial work today starts from a foundation model — GPT, Claude, Gemini, Llama — and consists of retrieval, tooling, evaluation, guardrails and deployment. Training a model from scratch is rare outside research labs and a handful of well-funded product companies.

That changes what you should look for in a vendor. Data engineering, prompt and context design, evaluation harnesses, cost control and MLOps decide whether a system works in production. A team that can only demo a notebook will struggle at the point where latency, accuracy and unit economics start to matter.

What teams build most often

• Retrieval-augmented assistants. Internal search and Q&A over a company's own documents, tickets and wikis, with citations back to the source.
• Agentic workflows. Multi-step automations that call tools and APIs, with a human approval step on anything irreversible.
• Document and data extraction. Turning invoices, contracts, forms and email into structured records that feed an ERP or CRM.
• Predictive models. Demand forecasting, churn, credit scoring, predictive maintenance — classic ML, still the highest-ROI category in most industries.
• Computer vision. Defect detection, counting, OCR, medical imaging support and video analytics.
• Recommendation and ranking. Personalised feeds, search relevance, pricing and matching.

How to choose an AI development partner

• Ask what they have shipped to production, not what they have prototyped. The gap between the two is where most AI budgets are lost.
• Ask how they evaluate quality. A team without an evaluation set and a regression suite cannot tell you whether a change made the system better or worse.
• Check data handling. Where does your data go, which model providers see it, is anything retained for training, and can the whole system run in your cloud tenancy or on-premise?
• Get the running cost, not just the build cost. Inference, vector storage and observability are recurring, and a badly designed pipeline can cost more per month than it did to build.
• Insist on a bounded first phase. A four-to-six-week discovery or pilot with a defined success metric is cheaper than discovering in month seven that the use case was never viable.

Budgets, timelines and team shape

A scoped pilot — one use case, one data source, a working interface — typically runs $15,000–$60,000 over six to twelve weeks. A production system with integrations, evaluation, monitoring and access control is usually $80,000–$400,000, and ongoing model and pipeline maintenance runs 15–25% of the build cost per year.

Hourly rates vary more by geography than by skill: roughly $120–$250 in the US and Western Europe, $40–$90 in Eastern Europe and Latin America, $25–$60 in South and Southeast Asia. A typical delivery team is one ML or AI engineer, one backend engineer, a data engineer part-time, and a product owner who understands the domain well enough to judge whether an answer is correct.