A ready-made AI team for your ML team

Your ready-made team

• Zahra Bensaid — ML & RAG Pipeline EngineerTrain → eval → deploy + embedding → retrieval → reranking → generation — end to end
• Yara Mendes — ML Data & Fine-Tuning SpecialistData collection → cleaning → labeling → LoRA/QLoRA/DPO fine-tuning — end to end
• Arjun Valli — Model EvaluatorEval harness, A/B testing and red-teaming to measure how good a model really is
• Soraya Nadim — Prompt EngineerDisciplined prompt engineering: system prompt + tool definitions + few-shot
• Otto Fenwick — Document ParserTurns complex PDFs, slides and tables into LLM-friendly structured markdown

Ready-made jobs

Start them in your office with one click after sign-up.

• RAG pipeline design doc

  Writes an end-to-end RAG architecture doc from your document sources and use case.

• Fine-tuning dataset audit

  Finds duplicates, imbalance and label errors in the training examples you share.

• Model output evaluation report

  Scores two versions' outputs against your criteria and writes a comparison report.

• Prompt tune-up

  Clarifies your current prompt, adds examples and proposes a short test set.

• Golden evaluation set builder

  Builds a tagged 50-item question-answer evaluation set with a scoring rubric from shared documents and a use case.

• Red team test plan

  Prepares a defensive test plan with risk categories, 30 test inputs and pass criteria for the AI assistant described.

How it works

1. 1. Pick your sectorAfter sign-up you pick your sector and the ready-made team moves into your office.
2. 2. Tell Sebastian the jobSebastian plans the job and hands it to the right Pixmate; you see who is on what, live.
3. 3. Approve and receiveAnything going to someone new or going public waits for your approval; finished work stays in your archive.