What Vertex AI unlocks for your business.
Each Vertex AI capability maps directly to a business problem Gilligan Tech solves for clients today.
Vertex AI Search and RAG Engine gives us a managed retrieval stack with grounding — meaning every answer cites the exact source document and page. No hallucinated references.
- Vertex AI Search — Managed semantic search over your document corpus
- RAG Engine — Retrieval-augmented generation with automatic grounding
- Grounding with Google Search — Verify AI answers against live web sources
Gemini 1.5 Pro's 1-million-token context window is unmatched for analysing large document sets — legal contracts, financial reports, compliance handbooks — in a single inference call.
- Gemini 1.5 Pro — 1M-token context for full document library analysis
- Gemini 2.0 Flash — High-speed reasoning for real-time queries
- Multi-modal input — Text, images, PDFs, audio in one prompt
Vertex AI's text embedding models power the semantic search layer in every RAG pipeline we build. Combined with Vertex Vector Search, queries return conceptually relevant results — not just keyword matches.
- text-embedding-004 — State-of-the-art semantic embeddings for retrieval
- Vertex Vector Search — Managed approximate nearest-neighbour at any scale
- Multimodal embeddings — Joint text + image embedding for mixed-media corpora
Gemini's native vision capabilities and Google Cloud Document AI let us extract structured data from any document — scanned PDFs, photos of invoices, handwritten forms — with high accuracy and zero template configuration.
- Gemini Vision — Multi-modal document understanding in a single model call
- Document AI — Pre-trained processors for invoices, receipts, contracts
- Imagen 3 — Visual content generation for marketing and product assets
How Gilligan Tech deploys Vertex AI.
Every Vertex AI integration we build follows a consistent pattern — ensuring reliability, auditability, and cost control from day one.
- Task classification: Incoming requests are classified by type — retrieval, generation, extraction, or conversation — to select the right Gemini model and pipeline.
- Context assembly: Relevant documents are retrieved from your vector store using Vertex AI Search. The top-ranked chunks are assembled into a grounded prompt context.
- Gemini inference: The assembled prompt is sent to the appropriate Gemini model (Flash for speed, 1.5 Pro for depth). The model generates a response grounded in retrieved context.
- Grounding verification: Vertex AI's grounding layer checks the response against source documents. Citations are extracted and returned alongside the answer.
- Audit logging: Every call is logged — model used, token count, latency, source documents cited — giving you a full audit trail accessible from your dashboard.
Gemini & Vertex AI models we use.
| Model | Context | Best for |
|---|---|---|
| Gemini 2.0 Flash | 1M tokens | High-throughput chat, real-time suggestions, rapid summarisation |
| Gemini 1.5 Pro | 1M tokens | Deep document analysis, multi-document reasoning, complex extraction |
| Gemini 1.5 Flash | 1M tokens | Cost-efficient production workloads requiring long context |
| text-embedding-004 | 2048 tokens | Semantic search, RAG retrieval, document clustering |
| Vertex AI Search | Managed | Enterprise document retrieval with grounding + citations |
| Imagen 3 | N/A | Image generation for marketing assets and product visuals |