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 my data for AI

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Most AI projects that fail don't fail because of the model: they fail because the data feeding it is scattered, outdated, or ungoverned.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.

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 engineering and data architecture

We design the data platforms that feed analytics, AI, and business decisions.

  • Modern data architecture design
  • Ingestion and transformation pipelines
  • Integration of scattered sources
  • Storage and access strategies
  • Legacy data platform migration

Data governance and quality

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

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

Enterprise knowledge and RAG

We structure internal knowledge so agents and models can query it reliably.

  • Internal documentation structuring
  • Knowledge base design for RAG
  • Content update strategies
  • Access control for sensitive information

Analytics, MLOps and LLMOps

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

  • Analytics platforms and dashboards
  • ML model deployment in production
  • Model and LLM monitoring
  • Retraining and continuous improvement
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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