Hire AI developers to build and scale your software.

There is no single rate for AI developers. Cost depends on the seniority you need, the engagement model you choose (adding developers to your team, a dedicated team, or a fixed-price project), how long the engagement runs and the scope of the work. For AI work, the biggest drivers are how clean and accessible your source data is, how much evaluation and human review the use case demands, whether the feature needs agents with tool access or a single model call, and the ongoing model usage the design implies. Share your requirements and we will come back with a quote for your situation.

You tell us the technology, seniority, role and team structure you need. We recommend AI developer profiles that match those requirements, and you review the profiles and speak with the engineers yourself before anyone joins your team. When you interview, the areas worth testing are designing a RAG pipeline: chunking, embeddings, hybrid search and reranking; building an evaluation set and regression-testing prompt changes; structured output, schema validation and handling malformed responses; prompt injection risks and guardrails for tool-calling agents; token cost tracking, caching and model routing; and handling provider rate limits, timeouts and fallbacks.

It depends on which engineers with the right skills are available when you get in touch, so we do not publish a fixed start date. Availability is part of the conversation when we recommend profiles, and the start date is agreed with you before the engagement begins.

Because you interview the engineers before anyone joins, fit is tested before work starts. If concerns come up once the engagement is running, raise them with us directly. How changes to the team are handled is set out in the terms you agree with us at the start.

A freelancer suits small, well-defined tasks you can manage yourself. A dedicated developer joins your team under your direction. A dedicated team adds several roles around your product, and a project-based team delivers a defined outcome end to end.

Start with retrieval-augmented generation in most cases. RAG grounds answers in your own documents, stays current when content changes and lets the feature cite its sources. Fine-tuning is better for teaching a consistent format, tone or narrow classification task, not for adding knowledge. Many production systems combine a well-prompted base model with retrieval and fine-tune only later, if at all.

Build an evaluation set before launch: real questions with expected answers or grading criteria. Score outputs automatically for accuracy, groundedness and format, use an LLM-as-judge only where it has been checked against human ratings, and rerun the suite whenever prompts, models or retrieval settings change. Production logs and user feedback then extend the set over time.

Route each task to the smallest model that passes your evaluations, trim prompts and retrieved context to what is relevant, and cache repeated responses or prompt prefixes where the provider supports it. Streaming improves perceived speed, batch APIs suit non-urgent jobs, and per-feature token tracking shows which calls are driving the bill.

Hallucination cannot be removed entirely, but it can be contained. Ground answers in retrieved sources and show citations, instruct the model to say when the context does not contain an answer, validate structured output against a schema, and add a human review step for high-stakes actions such as refunds or contract changes.

That depends on the provider's terms and your architecture. Business API terms from the major providers typically exclude submitted data from model training, and some offer regional hosting or reduced retention. Beyond that, redact personal data before sending it, enforce document-level permissions in retrieval so users only see what they may access, and store prompt logs securely.

Use an agent when the task needs several steps and decisions, such as looking up an order, checking a policy and drafting a reply. Keep tools narrow and well described, cap the number of steps, require human approval before irreversible actions and log every tool call so failures can be traced.

If you already run PostgreSQL, pgvector is often enough and keeps data in one place. Dedicated stores such as Pinecone, Qdrant or Weaviate become worthwhile at larger scale or when you need advanced filtering and hybrid search. Retrieval quality usually depends more on chunking, metadata and reranking than on the database choice.