Dedicated AI Development Team

An AI feature is mostly ordinary software around a small, unpredictable core. The AI/LLM engineer owns that core: prompts, retrieval, model choice, agent tools and the evaluation harness. A back-end developer builds the APIs, tool endpoints and integrations the model calls. A data engineer handles ingestion, chunking, refresh jobs and permissions. A front-end developer builds the chat, copilot or review screens, including showing sources and letting someone correct an answer. QA with evaluation skills turns real cases into test sets and runs them on every change.

The mix shifts as the product matures. A proof of concept leans on the AI/LLM engineer and the data engineer, with a thin front end for internal testers. Production adds weight on the back end, QA and front end: authentication, rate limits, error handling, monitoring and the experience of answers that are uncertain or wrong. Once live, the team can shrink to the people who tune quality, watch cost and add the next capability. We review the mix with you at each milestone.

On an AI product, nobody can sign off quality by reading the code, so evaluation is a shared team responsibility rather than a task left to the end. The team builds an evaluation set with your product owner - real questions, documents and edge cases with agreed good answers - and every prompt, retrieval or model change is scored against it before merging. Failures found in testing or production are added to the set so they stay fixed.

Guardrails work the same way: limits on what an agent may do without a person approving it, input and output checks, refusals for out-of-scope questions, and logging that shows why the system answered as it did. They are reviewed in the same pull requests as the feature code.

Before the team touches real data, we agree the rules with you and record them in the project setup: which systems the team may read, whether it uses production data, masked copies or synthetic samples, which model providers may receive data, and what must stay on your own infrastructure. Access follows least privilege, secrets stay in your vault, and nothing personal goes into prompts or coding assistants.

Retrieval respects the permissions your users already have, so a RAG search does not show a document to someone who could not open it. The work is covered by an NDA signed before kickoff, and you own the code, prompts, evaluation sets and IP the team produces.

A proof of concept answers one question: can this approach reach acceptable quality on your data? The team builds the narrowest version that can be measured, scores it against the evaluation set and reports honestly, including when the answer is not yet. You decide whether to continue, change approach or stop, and milestone-based pricing means each step has its own transparent cost.

Production is a different job. The same team hardens what worked: tested services instead of notebook code, monitoring for quality, latency and cost per request, a release process where prompt and model changes pass evaluation like any other change, and a way for users to report a bad answer. Keeping the people who built the prototype avoids the loss of context when a proof of concept is handed to a new team.

AI means two different things here, so we keep them separate. The first is the AI product the team builds for you, which your users interact with and which is judged by its evaluation results. The second is the AI coding tools our developers use while building it - assistants that draft boilerplate, data-pipeline scripts, test cases and documentation so the team moves faster. Those tools follow your policies, you can rule them out on any repository, and they decide nothing: architecture, evaluation and guardrails stay with the developers, who review every AI-assisted change before it is merged.