贝咨曼 MICROFACTOR

MICROFACTOR

Technical capabilities

Data platforms, GenAI engineering, MLOps, and marketing technology — the capability system behind consulting delivery, not a single-product feature list.

How technology serves transformation

MICROFACTOR’s technical capabilities support end-to-end work across consulting, data science, AI, and marketing: data foundations, model applications, agents, and operating loops that can be accepted in production. Metis AI·Thousand Eyes (GEO) is one marketing-technology product module — alongside platforms, GenAI, and MLOps — not the whole of Technology.

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·Thousand Eyes: 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.

GEO

How GEO relates to this hub

Technology covers platforms, GenAI, MLOps, and marketing tech. If you only need answer-engine visibility and brand knowledge structure, go to the GEO / Metis hub. The four pillars and product modules remain on the GEO capabilities deep-dive.

From capability to acceptanced delivery

Tell us your data and AI starting point — we combine platforms, GenAI, MLOps, or the GEO product path by scenario.