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AMTEXConsulting

AI & automation

AI where it removes work, with people where it matters.

Enterprise search, document intelligence, AI assistants and workflow automation built on your data and your systems, with governance and human review designed in.

Talk to an ExpertWhat we deliver

Overview

AI & Automation, done properly.

Useful enterprise AI is mostly plumbing: getting the right documents and records in front of a model, constraining what it can do, checking its output and connecting the result to the system that needs it. AMTEX builds that plumbing, and we are honest about where a well-designed rule beats a model.

Technologies

  • LLM APIs
  • Retrieval-Augmented Generation
  • Vector Search
  • Document Intelligence
  • OCI Generative AI
  • PostgreSQL
  • Node.js
  • Oracle Integration Cloud
  • OCI Functions

What we deliver

Capabilities

Everything AMTEX builds, configures and runs across AI & Automation.

  • AI-powered workflows

    Classification, routing, extraction and drafting steps inside existing business processes.

  • Generative AI

    Summaries, drafts and explanations grounded in your data, with citations.

  • Enterprise search

    Search across documents, tickets and systems with permissions respected.

  • Retrieval-augmented generation

    RAG pipelines with chunking, embeddings, hybrid retrieval and evaluation.

  • Vector search

    pgvector or Oracle AI Vector Search alongside the data it indexes.

  • AI assistants

    Assistants that answer from policy and data and hand off to people cleanly.

  • Document intelligence

    Invoice, PO, contract and form extraction with confidence scores and review queues.

  • Workflow automation

    OIC, Functions and code that removes manual steps end to end.

  • LLM integrations

    Claude, OpenAI and OCI Generative AI behind one provider interface so you are not locked in.

  • API orchestration

    Tool use and agents constrained to approved actions with full audit trails.

  • Human-in-the-loop

    Review queues, approval steps and feedback loops that improve the system.

  • AI governance

    Data handling, evaluation, monitoring and policies your risk team can sign.

Pattern

How a grounded enterprise assistant is built.

Retrieval first, model second, actions constrained, humans where it matters.

Grounded assistant pattern

  1. Sources

    • Documents
    • Fusion data
    • Tickets & policies

  2. Index

    • Chunk & embed
    • Vector + keyword searchpgvector / Oracle AI Vector Search
    • Permission filter

  3. Reason

    LLM behind a provider interface

    Claude · OpenAI · OCI Generative AI

  4. Act

    • Cited answer
    • Approved actions only
    • Human review queue
    • Evaluation & logging

Principles

Six rules we do not break.

  1. 01

    Ground everything

    Answers cite the document or record they came from. No citation, no answer.

  2. 02

    Constrain actions

    Models can only call approved tools with approved parameters, and every call is logged.

  3. 03

    Keep people in the loop

    Low-confidence extractions and consequential actions go to a review queue, not straight to the ledger.

  4. 04

    Measure before and after

    An evaluation set is built in the prototype and run on every change. If quality drops, it does not ship.

  5. 05

    Prefer a rule when a rule works

    Deterministic logic is cheaper, faster and auditable. We use a model where judgment is genuinely needed.

  6. 06

    Own your data path

    Provider abstraction, data residency choices and no training on your data. Switch models without rewriting the product.

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 against real data with an evaluation set.

  3. 03

    Productionise

    Guardrails, monitoring, review queues, integration.

  4. 04

    Improve

    Feedback loops, model updates, expanded scope.

Related solutions

Have a process that drowns people in documents?

That is usually the best first AI project. Let us scope it.

Scope an AI pilot