Symhas Moves It to Production.Gemini 1.5 Pro in a demo on public documents is not enterprise AI. Enterprise generative AI needs to answer questions on private data the model was not trained on, respect document-level access controls, return traceable citations rather than hallucinations, and run inside the private network without data reaching the public internet. That is a RAG pipeline problem, not a prompt engineering problem.Symhas designs and deploys production Vertex AI generative AI — Gemini via the Vertex AI API with VPC Service Controls, RAG pipelines on BigQuery and Cloud Storage with Vertex AI Vector Search, Agent Builder for multi-turn conversational interfaces, and citation-backed retrieval that gives users sources, not hallucinations.
Production-Grade on GCP.
Every capability designed, deployed, and documented by Symhas GCP-certified data and AI architects. Fixed price. Production-ready.
Fixed Price. Fixed Timeline.
Four phases with go/no-go gates. Scope and price agreed before week one.
Generative AI use case prioritisation. Document corpus assessment — volume, format, update frequency. RAG vs Agent Builder vs fine-tuning selection per use case. VPC Service Controls and data governance design. Architecture approved.
Cloud Storage document corpus ingested. Vertex AI text embeddings generated. Vector Search index built and validated. Cloud DLP configured for PII redaction. CMEK keys provisioned. VPC Service Controls perimeter deployed.
RAG pipeline end-to-end tested with representative queries. Citation accuracy evaluated. Agent Builder data store connected and conversational agent configured. REST API endpoint deployed and load-tested. User acceptance testing completed.
Production RAG pipeline or Agent Builder live. Audit logging active. AI engineering team certified on Vertex AI pipeline management and Vector Search index updates. Symhas moves to advisory.
RAG on 12,000 Research Documents. Cited Answers in Under 3 Seconds.
The asset management firm had portfolio managers spending 4–6 hours daily searching through 12,000 internal research documents, analyst reports, and regulatory filings to answer client queries and prepare investment committee materials. Manual search returned keyword matches with no synthesis.
Symhas built a Vertex AI RAG pipeline — 12,000 documents chunked and embedded into Vertex AI Vector Search, Gemini 1.5 Pro generating cited answers from retrieved document chunks, and Agent Builder providing a conversational interface for portfolio managers. Average query response time: 2.8 seconds, with source document links in every answer.
Vertex AI RAG Pipeline — Financial Services Production
“Portfolio managers were spending half their day searching. Now they ask a question in plain English and get an answer with the source documents linked. That is 4 hours of research time returned to every portfolio manager every day.”
— Chief Investment Officer, Global Asset Management Firm
We Configure for This Capability.
Enterprise Gemini access — Gemini 1.5 Pro and Flash via Vertex AI, private VPC endpoint, CMEK.
Managed vector database — document embeddings, approximate nearest neighbour, and real-time index updates.
Document embedding — text-embedding-004 generating semantic vectors for RAG retrieval.
Managed conversational AI — data stores, grounding, multi-turn conversation, and REST API.
Data loss prevention — PII detection and redaction before document ingestion into the RAG pipeline.
Data exfiltration prevention — Vertex AI and Vector Search APIs restricted to authorised GCP perimeter.
BigQuery structured data available alongside unstructured documents in the RAG pipeline.
Vertex AI & ML PlatformVertex AI Feature Store and Pipelines — the ML platform that Gemini fine-tuning runs on top of.
Oracle ERP to GCP IntegrationOracle Fusion documents and structured data available in the generative AI RAG pipeline.
GCP Security & GovernanceVPC Service Controls and data governance protecting the generative AI data pipeline.
We Will Design the RAG Pipeline That Answers It in Seconds.A 30-minute Gemini assessment with a Symhas AI architect. We will review your document corpus, use case requirements, and data governance constraints — and design a production RAG architecture before the engagement price is agreed.No commitment. No pitch deck. An honest conversation about your data and AI ambitions.