FACTORYZE

AI & MACHINE LEARNING

We build AI systems from prototypes to production, focusing on RAG, agents, evaluation, MLOps, and safety practices.

How We Work: Research to Delivery

Research Team

  • Problem framing, literature scan, benchmarking
  • Data audits, feasibility studies, baselines
  • Prototype design and experiment planning

Prototyping

  • RAG/agent prototypes with eval harness
  • Golden sets, rubric scoring, regression suites
  • Stakeholder reviews and iteration loops

Engineering Handoff

  • MLOps pipelines, observability, SLOs
  • Golden sets, rubric scoring, regression suites
  • Stakeholder reviews and iteration loops

Strategy & Capabilities

Strategy

  • Use-case discovery, ROI framing, risk analysis
  • Systems thinking: data, model, application, feedback
  • Build vs buy: API LLMs, open-source, fine-tuning

Capabilities

  • LLMs, Retrieval-Augmented Generation (RAG), agents
  • Classification, extraction, summarization, generation
  • Evaluation frameworks and human - in-the-loop review

Outcomes

  • Faster workflows through copilots and automation
  • Better results through evaluation and iteration
  • Reliable production systems with monitoring and safety checks

Solution Architectures

RAG Application

  • Chunking + embeddings + vector DB
  • Query re-writing and context post-processing
  • Citations and guardrails

Evaluation & E2E

  • MLOps pipelines, observability, SLOs
  • Golden sets, rubric scoring, regression suites
  • Stakeholder reviews and iteration loops

Agentic Workflows

  • Tool-use (APIs/DB)
  • Plans, memory, retries
  • Supervisor/worker patterns