EFF / AI–02AI Engineering

Drawing EFF / AI–02 · Capability system

AI engineering for the production bar.

We connect models to the data, applications and operating architecture that enterprise AI depends on — then verify the system with evidence before release.

01Five capability areas

From intelligence layer to operating system.

Each engagement is shaped around the system boundary, not a fixed technology package.

01 / AI capability

Agentic AI & enterprise applications

AI systems that act within real enterprise workflows, with explicit tools, boundaries and human control.

  • Agent and workflow architecture
  • Tool and system integration
  • Human-in-the-loop control
  • Enterprise application engineering

02 / AI capability

Enterprise chat & RAG

Knowledge systems built around measurable retrieval quality rather than plausible-looking answers.

  • Retrieval architecture
  • RAG evaluation
  • Enterprise search and chat
  • Access-aware knowledge systems

03 / AI capability

Document & data intelligence

Pipelines that turn complex documents and operational data into usable, reviewable information.

  • Document extraction and classification
  • Knowledge preparation
  • Structured data enrichment
  • Human review workflows

04 / AI capability

Machine learning & deep learning

Predictive and learning systems engineered around the data, acceptance criteria and operating context available.

  • Predictive modeling
  • Anomaly and pattern detection
  • Model evaluation
  • Deep learning where appropriate

05 / AI capability

AI platforms, MLOps & production architecture

The engineering layer that makes models and AI applications deployable, observable and maintainable.

  • AI-ready data platforms
  • Evaluation and release pipelines
  • Observability and operating controls
  • Production architecture
02Enterprise data engineering

The deterministic foundation beneath dependable AI.

Data quality, movement, lineage and operating reliability determine what an AI system can learn, retrieve and prove.

Data Engineering / AI foundation

Production data is part of the AI system.

We engineer warehouses and semantic models, complex ETL/ELT, real-time synchronization, high-performance processing and the controls required to evolve them safely.

Explore Data Engineering
03Production-grade AI engineering

The Effority Method.

We define the outcome and its acceptance criteria first, resolve AI uncertainty before the POC, and move toward production only through explicit evidence gates.

  1. 00–01frame

    Frame

    Define the AI decision, users, risks and measurable acceptance criteria.

  2. 02–03validate

    Validate

    Verify the data and test AI viability against locked evidence.

  3. 04–06engineer

    Engineer

    Design, prove and build the complete production AI system.

  4. 07–09control

    Verify & evolve

    Test, accept, release and evolve the AI through controlled gates.

Full method / 10 evidence-gated phasesExplore the Effority Method
04Where we fit

Own the hard interface between AI and enterprise reality.

05 / Define the system

Start with the business decision, the data and the production constraint. We will help define the engineering path between them.

Discuss an AI or data initiative.