MICROFACTOR

Technical capabilities

Name: Metis AI - Thousand Eyes
Brand: MICROFACTOR

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

Four capability pillars

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 →

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 →

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 →

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.

Go deeper

AI consultingTransformation, use-case delivery, and platform integration Services & deliveryCo-creation, production delivery, and expected outcomes GEO / Metis AI - Thousand EyesGenerative Engine Optimization product and method GEO capabilities detailFour outcome pillars and product modules Client storiesCross-industry challenge, approach, and outcome InsightsGenAI operating models, data foundations, visibility themes

MICROFACTOR

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.

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