ARTIFICIAL INTELLIGENCE & DATA

AI that understands your business and data that shapes your future.

We don't experiment with AI: we implement solutions that automate processes, connect information, and generate measurable operational impact.

Identify AI opportunities

AI with purpose

Artificial intelligence only generates value when connected to real processes, reliable data, and clear business objectives.

At Gizlo we design AI and data solutions that help automate tasks, improve decisions, accelerate analysis, and transform corporate knowledge into reusable digital capabilities.

CHALLENGES WE ADDRESS

Challenges our clients face

Decisions based on intuition

When information is unavailable, unconsolidated, or outdated, strategic decisions become slow and imprecise.

Repetitive manual processes

Administrative, commercial, technical, or operational teams spend too much time on tasks that can be automated with AI, business rules, and integration.

Data scattered in silos

Information stored across multiple systems, documents, spreadsheets, or databases prevents a complete view of the business.

Corporate knowledge that's hard to leverage

Policies, manuals, contracts, procedures, and internal documentation are often scattered and depend on specific experts for interpretation.

WHAT'S INCLUDED

What the service includes

Generative AI Agents

We create specialized assistants that query company information to answer questions, analyze documents, support processes, and assist internal or external users.

Use cases:

  • Support team assistants
  • Intelligent internal policy queries
  • Contract or document analysis
  • Sales or customer service assistants
  • Semantic search across knowledge bases
  • Operational process support

Data Engineering

We build pipelines, data models, and analytical platforms that integrate information from multiple sources.

  • Data integration
  • ETL/ELT pipelines
  • Data Lakes
  • Data Warehouses
  • Analytical modeling
  • Data quality and governance

Operational Machine Learning / MLOps

We take predictive models to production with monitoring, versioning, automation, and practices to sustain them over time.

Use cases:

  • Demand forecasting
  • Customer classification
  • Operational risk
  • Anomaly detection
  • Segmentation
  • Recommendations

Process automation with AI

We combine AI, business rules, APIs, and integration to automate repetitive flows and reduce manual intervention.

Examples:

  • Document processing
  • Request classification
  • Data extraction
  • Response generation
  • Information validation
  • Backoffice automation
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

  • Automation of administrative and operational tasks.
  • Reduced response times.
  • Greater process traceability.
  • Consolidated information for decision-making.
  • Real-time insights.
  • Better use of internal knowledge.
  • AI models integrated into real processes.

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Backing technology

AI should solve real problems, not become an isolated experiment.

We implement purposeful AI integrated into your business processes and systems.

Explore AI solutions

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