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02Artificial Intelligence

AI that works inside the enterprise, not beside it.

The distance between an impressive AI demonstration and a dependable enterprise capability is made of architecture, data, engineering and governance. HestiaPremise helps organizations close that distance: selecting the right use cases, designing the AI architecture, engineering production systems and putting the controls in place so AI can be trusted at scale.

Move artificial intelligence from experimentation into enterprise operations.

Capability 02 of 05 · AI turns intelligence into action.

The challenge

The pilot-to-production gap

Many enterprises have run AI pilots. Far fewer operate AI as a dependable capability. Pilots succeed on curated data and enthusiastic users; production demands integration with core systems, secure access to enterprise knowledge, evaluation, monitoring, cost control and clear accountability.

Generative and agentic AI widen the gap. When systems can retrieve information, reason over it and take actions, the questions become architectural: what may an agent access, what may it do, how is it evaluated, and who is accountable for its outcomes?

What we do

Artificial Intelligence

Strategy & architecture

02.1
  • Enterprise AI Strategy
  • Enterprise AI Architecture
  • AI Platforms

Generative & agentic AI

02.2
  • Generative AI
  • Agentic AI
  • AI Assistants
  • Enterprise Copilots
  • RAG Architectures

Applied machine intelligence

02.3
  • Machine Learning
  • Natural Language Processing
  • Computer Vision
  • Intelligent Automation
  • Knowledge Systems
  • Knowledge Graphs

Operations & trust

02.4
  • MLOps
  • LLMOps
  • AI Governance
  • Responsible AI

Reference architecture

Anatomy of an enterprise AI system

  1. L5Experience
    • Assistants & copilots
    • Agent workflows
    • AI embedded in applications
  2. L4Orchestration
    • Agent orchestration
    • Tool & API access
    • Guardrails & policies
  3. L3Models
    • Foundation models
    • Domain-adapted models
    • Classical ML
  4. L2Knowledge & context
    • Retrieval (RAG)
    • Knowledge graphs
    • Vector indexes
    • Feature stores
  5. L1Data foundation
    • Governed enterprise data
    • Documents & content
    • Events
Evaluation & monitoring: MLOps, LLMOps
Governance & security: access, risk, accountability
Reference architectureUseful enterprise AI is a system, not a model: governed context below, orchestration and guardrails above, and continuous evaluation alongside.

How it connects

No capability works alone. This is how this one depends on, and strengthens, the others.

Architecture connects everything.

Questions we help answer

  1. Q1

    Which AI use cases will create measurable value, and which are only interesting?

  2. Q2

    Should we build, buy or compose, and on which models and platforms?

  3. Q3

    How do we give AI access to enterprise knowledge without exposing what it should not see?

  4. Q4

    What controls must exist before an AI agent is allowed to act?

Let’s discuss what your enterprise is building next.

Bring the ambition, the constraints and the current landscape. We will bring the architecture.

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