DATA & AI FOUNDATIONS

The right data, ready for AI to use.

We build the data foundations, governance, and quality that any serious artificial intelligence initiative needs — without solid foundations, AI isn't reliable.

Evaluate data readiness

Without reliable data, there's no reliable AI

Most AI projects that fail don't fail because of the model: they fail because the data feeding it is scattered, outdated, or ungoverned. No agent or model can compensate for an unreliable data foundation.

At Gizlo we build the data infrastructure —engineering, governance, quality, and analytics— that turns scattered information into an asset AI can use with confidence. Our Data & AI Readiness Assessment is the starting point for knowing where your data stands today.

CHALLENGES WE ADDRESS

Challenges our clients face

Data scattered across systems that don't talk to each other

Critical information lives in silos —ERPs, spreadsheets, legacy systems— with no single reliable source.

Lack of data governance and quality

Without clear rules on quality, ownership, and access, the data used for AI produces inconsistent or unreliable results.

Unstructured corporate knowledge

Internal documentation, policies, and processes exist, but not in a format an agent or model can consume.

Models in production with no monitoring

AI and ML models deployed without observability lose accuracy over time, and nobody catches it in time.

WHAT'S INCLUDED

What the service includes

Data architecture and platforms

We design the data architecture —lakehouse, warehouse, and streaming— that feeds analytics, AI, and real-time business decisions, packaged in our Data Product Factory when the use case calls for it.

  • Modern data architecture design (lakehouse / warehouse)
  • Batch and streaming ingestion pipelines (Real-Time Data Accelerator)
  • ETL / ELT and data transformation
  • Legacy data platform migration

Data governance, quality, and catalog

We establish the rules, catalog, and lineage that make data reliable for any use, including AI.

  • Data ownership and lineage definition
  • Quality and validation rules
  • Enterprise data catalog
  • Data regulatory compliance

Enterprise knowledge, RAG, and vector search

We structure internal knowledge and build the semantic search capabilities agents and models need to answer with confidence — the foundation of our Enterprise Knowledge Platform.

  • Internal documentation structuring
  • Knowledge base design for RAG
  • Vector search and semantic layers
  • Access control for sensitive information

Analytics and semantic layers

We put data within the business's reach with reliable analytics and a semantic layer that's consistent across teams, through our Analytics Modernization program.

  • Analytics platforms and dashboards
  • Shared metrics and semantic layer definition
  • Democratized business access to data

MLOps and LLMOps

We put analytics, ML, and LLM models into production with the monitoring they need to stay reliable.

  • ML model deployment in production
  • Model and LLM monitoring
  • Retraining and continuous improvement
  • Version and experiment management
METHODOLOGY

From strategy to execution

  1. 01

    Discovery

    We understand the context, business objectives, current systems, constraints, risks, and improvement opportunities.

  2. 02

    Assessment and architecture

    We design the target solution, define roadmap, architecture, team, technologies, and implementation model.

  3. 03

    Iterative implementation

    We build in phases, prioritizing value, controlling risks, and continuously validating results with the client.

  4. 04

    Deployment and stabilization

    We support the move to production, configure monitoring, resolve initial incidents, and ensure operational continuity.

  5. 05

    Continuous evolution

    We optimize, automate, incorporate new capabilities, and support platform growth.

EXPECTED OUTCOMES

What we can achieve together

  • Higher quality and reliability of available data.
  • Shorter access time to critical business information.
  • Greater governance coverage across enterprise data.
  • More reliable, monitored AI models in production.

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3-4 weeks

Data & AI Readiness Assessment

Evaluate my data for AI

Without reliable data, there's no reliable AI.

We build the data foundations, governance, and quality that any serious artificial intelligence initiative needs.

Evaluate data readiness

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