Answers grounded in your documents, databases, and APIs

For teams tired of confident but wrong agent replies, appamass builds retrieval pipelines with citations, vector search on Google Cloud Platform (GCP), schema-valid outputs, and review points before actions execute.

  • Deterministic AI agent workflows without hallucinations using the Google Agent Development Kit (ADK) and the Google Cloud Platform (GCP).

  • Precise, context-grounded RAG knowledge bases for reliable enterprise data without hallucination risks.

  • Deployment of state-of-the-art vector databases on the Google Cloud Platform (GCP) for reliable and auditable information search.

Context-Grounded RAG Systems

We bundle unstructured enterprise data, manuals, and source documents into a secure, grounded knowledge base.

What users see

Precise answers to queries with directly linked, clickable original citations and page numbers.

How it works

Automated document chunking pipelines, embedding in vector stores (Cloud SQL PGVector or Vertex AI Vector Search), and precise retrieval.

What remains controllable

Document-level data access permissions and strict guardrails to prevent hallucinations remain controllable.

A first grounded knowledge flow

The first build should cover one clear knowledge area and make answers consistently verifiable.

Select sources

Only relevant documents, records, and knowledge areas enter the first search space.

Review citations

Answers show which passages were used and where uncertainty remains.

Add feedback

Corrections, ratings, and missing sources improve the knowledge base over time.

Related areas showing how mobile apps, React web systems, AI agents, and controllable automations fit together.

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