Hire Dedicated AI Developers from India

Rarely, and we will usually argue against it. For the large majority of business problems, a good retrieval setup and careful prompting on a frontier model beats fine-tuning on cost, time and maintenance — and does not go stale when your data changes. Fine-tuning earns its place for narrow, stable, high-volume tasks like consistent formatting or domain classification. We do not train foundation models, and anyone promising you one for a mid-size budget is selling something else.

Mid-level (4-6 years) is $25/hour, senior (6-10 years) $32/hour, and lead $40/hour. That is above our web rates because the supply of engineers who have actually shipped AI features — not just prototypes — is smaller. Full-time is 160 hours a month, so roughly $4,000, $5,120 and $6,400. We wrote up the full project economics in what it actually costs to add AI to your product.

Claude, GPT and Gemini through their APIs, plus open models such as Llama and Mistral where self-hosting is required for data or cost reasons. We build behind a provider abstraction so switching models is an afternoon rather than a migration — which matters, because pricing and quality shift every few months. Choosing a model is a measurement exercise against your own test set, not a matter of opinion.

With an evaluation set: 50-200 real cases from your data with known-correct answers, built with someone who knows the domain. Every change is scored against it, so “is this better?” becomes a number instead of an argument. This is the most commonly skipped step in AI projects and the main reason teams cannot tell whether a prompt change helped. We treat it as part of the build, not an extra.

We sign NDAs and data processing agreements, and we configure API access so your content is not used for model training — the major providers offer this for business and API tiers. For sensitive workloads we can run open models in your own cloud so data never leaves your perimeter. For European clients we provide Article 28 GDPR terms with Standard Contractual Clauses, and we scope work to environments holding no personal data wherever possible.

Yes, and that is most of our AI work. We join your repository and sprint process, add the feature behind a flag, and ship it to a subset of users behind a human review step before anything runs automatically. Whether your stack is Laravel, PHP, Node, React or something else, the integration pattern is the same — the AI part is rarely the hard part.

A focused feature on clean data is typically 3-8 weeks including evaluation. The prototype takes days, which is exactly what creates unrealistic timelines — the remaining work is edge cases, permissions, latency, failure handling and the test set. If your data needs cleaning or unification first, add time for that and we will tell you before quoting rather than after.

Yes, scoped narrowly and with a human gate on anything irreversible. We are deliberately cautious here: reported production failure rates for enterprise agents run from 70% to 95%, and the leading cause is unclear success criteria rather than weak models. Agents work when the task has a measurable definition of done, the tools and data they need are genuinely reachable, and someone reviews consequential actions. We wrote about the failure patterns in agentic AI in 2026.

No, and we have published our reasoning rather than just asserting it. AI replaced a large share of code typing, not engineering judgement — and as generated output rises, verification becomes the constraint. Our own view, with the 2026 research behind it, is in will AI coding agents replace offshore developers. Hire fewer, stronger engineers with AI leverage rather than more people producing more code.