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AMTEXConsulting

OCI AI services and Fusion AI

AI inside the Oracle estate, with your data staying where it is.

OCI Generative AI, Generative AI Agents, AI Vector Search in Autonomous Database, Fusion AI Agents, OCI Language, Speech and Vision, and Oracle Digital Assistant, applied to real finance, supply chain and HR work with human review built in.

Grounded AI inside the Oracle tenancy
  1. Enterprise data

    • Fusion ERP / SCM / HCM
    • Documents in Object Storage
    • Autonomous Data Warehouse
  2. Retrieve

    • AI Vector Search
    • Select AI
    • Identity-aware filters
  3. Reason

    OCI Generative AI / Agents

    Or Claude / OpenAI behind the same interface

  4. Act

    • Digital Assistant
    • OIC action
    • Review queue
    • Evaluation & logs

Overview

Oracle AI, done properly.

Most enterprises running Oracle already have the two things that make AI useful and hard: a lot of structured data and strict rules about where it may go. OCI's AI services and the AI capabilities arriving in Fusion let you apply models to that data inside the same tenancy and the same security boundary. AMTEX designs and builds those solutions, and we are honest about where a well-written rule beats a model.

We also build with Claude and OpenAI where a client prefers them. The provider sits behind an interface; the retrieval, guardrails, review queues and evaluation are the same either way.

Technologies

  • OCI Generative AI
  • OCI Generative AI Agents
  • AI Vector Search (Oracle DB 23ai)
  • Select AI
  • Oracle Digital Assistant
  • OCI Language, Speech & Vision
  • Fusion AI Agents & embedded AI
  • LLM APIs
  • Retrieval-Augmented Generation

What we deliver

Capabilities

Everything AMTEX builds, configures and runs across Oracle AI.

  • OCI Generative AI

    Hosted models with dedicated clusters, fine-tuning where it earns its cost, and no training on your data.

  • Generative AI Agents

    RAG agents over Object Storage documents and Autonomous Database, grounded with citations and permission-aware.

  • AI Vector Search in Autonomous DB

    Vector embeddings beside relational data so search, joins and security policies work together in one SQL.

  • Select AI

    Natural-language questions over your schema, constrained to approved tables and views.

  • Fusion AI Agents & embedded AI

    Adopting and configuring the AI features in ERP, SCM and HCM: narrative reporting, predictive planning, agent-driven tasks, with change management.

  • OCI Language, Speech & Vision

    Classification, entity extraction, sentiment, transcription and image analysis as building blocks in workflows.

  • Oracle Digital Assistant

    Conversational access to Fusion and enterprise data for employees and customers, in Teams, Slack or web.

  • Retrieval & evaluation

    Chunking, hybrid retrieval, evaluation sets and monitoring, so quality is measured, not assumed.

  • Human-in-the-loop

    Review queues and approval steps wherever the output changes money, people or compliance.

  • Governance

    Data residency, access control, audit logging and model risk documentation your security team can sign.

The hard parts

Where these projects become difficult

The places programmes slip, and what we do about each one.

  1. 01

    Data cannot leave

    The problem

    Security policy forbids sending finance or HR data to a public model endpoint.

    How AMTEX approaches it

    OCI Generative AI and AI Vector Search inside the tenancy, private endpoints, and a documented data flow for the risk review.

  2. 02

    Demo works, production does not

    The problem

    A chatbot that impressed in a demo answers wrongly on real questions.

    How AMTEX approaches it

    An evaluation set built from real questions before the prototype, hybrid retrieval, citations and a threshold below which the assistant defers to a person.

  3. 03

    Permissions ignored

    The problem

    The assistant answers from documents the asker is not allowed to read.

    How AMTEX approaches it

    Retrieval filtered by the caller's identity and the source system's security, tested with negative cases.

How we work

From first conversation to steady state

  1. 01

    Identify

    Processes where AI removes measurable work with acceptable risk.

  2. 02

    Prototype

    Two to four weeks on real data with an evaluation set.

  3. 03

    Productionise

    Guardrails, review queues, monitoring, integration through OIC.

  4. 04

    Improve

    Feedback loops, model updates, expanded scope.

FAQ

Common questions

Want AI over your Oracle data without it leaving the tenancy?

We will scope a bounded pilot with an evaluation set and a review step.