MICROFACTOR
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MICROFACTOR

Technical capabilities

Platforms, GenAI, MLOps, and marketing technology — the engineering stack behind consulting delivery. GEO is the marketing-tech module.

Four capability pillars

01

Data & AI platforms

Data-centric platforms, governance, and analytics foundations for decisions and AI apps.

  • Data platforms, lakehouse, and integration: collect, model, and serve
  • Governance and quality: master data, lineage, access, and compliance boundaries
  • BI and self-service analytics: metrics as operating assets
  • Customer and operations data for growth, service, and ops use cases

Related services →

02

GenAI & agents

LLM application engineering, knowledge systems, and agent orchestration — from pilots to operable apps.

  • Use-case selection and evaluation: value hypotheses, risk, acceptance metrics
  • RAG / knowledge bases: discoverable, citable, maintainable enterprise knowledge
  • Agent workflows: tools, human-in-the-loop, and audit trails
  • Prompt and output governance: safety, compliance, and quality control

AI consulting →

03

MLOps & delivery

Ship models and AI apps to production: engineering, monitoring, iteration, and transfer.

  • Co-creation and production delivery: runnable, acceptable, handoff-ready
  • CI/CD, environments, and config: close the demo-to-production gap
  • Monitoring, evaluation, and alerts: drift and cost visibility
  • Operating cadence and reuse across adjacent scenarios

Delivery approach →

04

Marketing tech - GEO

Metis AI: be seen, trusted, and recommended in generative answers.

  • Multi-engine visibility monitoring and competitor narrative comparison
  • Knowledge graphs and citable structured knowledge publishing
  • GEO content workflows: optimize for citation, not rankings alone
  • Probe → diagnose → structure → publish → re-probe closed loop

Explore GEO →

Engineering principles - delivery rhythm

Insight → Design → Build → Scale

Aligned with the consulting framework: anchor goals and feasibility, set architecture and integration, engineer and validate, then iterate and reuse scenarios.

Business outcomes first

Choose tech for acceptanced business metrics — not tool or model name-dropping.

Data and knowledge first

Without reliable data and a maintainable knowledge layer, GenAI and agents rarely create durable value.

Operable and auditable

Access, logs, evaluation, and cost transparency are required to leave the pilot stage.