Make your proprietary enterprise data AI-ready with hardened retrieval pipelines.

You cannot build reliable agentic systems on fragmented, unstructured data. We engineer end-to-end document OCR ingestion, semantic chunking, high-performance vector databases, and hybrid BM25 + ColBERT retrieval with strict Australian data residency and permission-aware access control.

What we deliver

Unstructured Document OCR & ETL: High-throughput extraction pipelines ingesting complex multi-page PDFs, scans, Word documents, and spreadsheets into structured Markdown and JSON with tabular data preservation.
Semantic Chunking & Embedding Strategies: Context-aware chunking algorithms (hierarchical, semantic, and markdown-aware) paired with domain-optimised embedding models (Voyage AI, OpenAI text-embedding-3, Cohere Embed v3).
Enterprise Vector Database Deployments: Production-grade vector index engineering on Qdrant, Pinecone, and PostgreSQL (pgvector) with metadata filtering, scalar quantization, and sub-50ms query latency.
Hybrid Retrieval & Neural Rerankers: Fusion retrieval combining lexical keyword search (BM25) with dense semantic search and cross-encoder rerankers (Cohere Rerank, ColBERT) to eliminate hallucinations.
Permission-Aware Access Control (ACLs): User-level security filters mapped to Azure AD, Okta, and Google Workspace, guaranteeing multi-tenant data isolation and preventing cross-department data leakage.
Australian Data Residency & Compliance: Private VPC architectures hosted on Australian AWS/Azure regions with zero data retention (ZDR) guarantees, satisfying APRA CPS 234 and Privacy Act standards.

Common engagement patterns

Common scenario: Legacy document extraction across thousands of PDFs, scanned contracts, and compliance filings.

Typical approach: Multi-modal OCR extraction pipeline with layout analysis, table extraction, and automated JSON schema normalization into cloud data warehouses.

Typical timeline: 4-6 weeks

Indicative outcome: 90% reduction in manual data entry time and 100% auditable digital extraction with source page bounding boxes.

Common scenario: Enterprise Knowledge Base & Permission-Aware RAG Foundation.

Typical approach: Ingesting scattered Confluence, Jira, SharePoint, and Google Drive silos into pgvector/Qdrant with dynamic role-based ACL metadata filtering and hybrid BM25 search.

Typical timeline: 3-5 weeks

Indicative outcome: Sub-second accurate knowledge retrieval for internal AI agents with zero cross-department data leakage (Gartner).

Common scenario: High-volume operational database indexing for semantic real-time agent search.

Typical approach: Change Data Capture (CDC) streaming pipelines from PostgreSQL/MySQL/Snowflake into vector indexes using Airbyte, dbt, and automated embedding generation.

Typical timeline: 3-5 weeks

Indicative outcome: Real-time agent data synchronisation with under 2-minute latency from transactional write to vector queryability.

Common scenario: Multi-tenant SaaS proprietary data isolation with hybrid reranking.

Typical approach: Tenant-partitioned vector namespaces with hybrid retrieval and Cohere Rerank models, enforcing strict cryptographic tenant isolation.

Typical timeline: 4-6 weeks

Indicative outcome: 35% higher answer precision in customer-facing AI agents while maintaining SOC 2 and ISO 27001 isolation standards.

Tools & Frameworks

QdrantPineconepgvector / PostgresUnstructured.ioLlamaParseCohere RerankAirbytedbtSnowflakeDatabricksPythonVoyage AI

We architect database and retrieval stacks for sub-second latency, deterministic recall, and strict tenant isolation.

Data Architecture

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Our 4-Step Data Engineering Methodology

Audit & Data Topology Mapping

We map your structured and unstructured data silos, access hierarchies, document types, and security constraints to design a resilient vector architecture.

OCR, Ingestion & Semantic Chunking

We deploy high-accuracy OCR parsers, layout analysers, and markdown-aware chunkers to preserve document structure, tables, and parent-child relationships.

Vector Indexing & Hybrid Retrieval

We benchmark embedding models, configure vector databases (Qdrant, Pinecone, pgvector), and implement hybrid BM25 + neural rerankers for pinpoint recall.

Governance, ACLs & Production Cutover

We enforce user-level permission filters, Australian data residency guards, and automated embedding pipelines with comprehensive latency and drift monitoring.

Frequently Asked Questions

Common questions regarding vector databases, document OCR, hybrid search, and RAG governance.

What makes agentic data engineering different from traditional data engineering?

Traditional data engineering moves tabular rows between SQL databases and data warehouses via batch ETL. Agentic data engineering prepares unstructured documents, audio, and knowledge bases for autonomous LLMs. It involves document OCR parsing, semantic chunking, vector embedding, metadata enrichment, hybrid BM25 + neural retrieval, and role-based ACL filtering so AI agents can query proprietary knowledge reliably without hallucinating.

Which vector database do you recommend: Pinecone, Qdrant, or pgvector?

It depends on your infrastructure and security posture. For teams already operating PostgreSQL on AWS RDS or Azure, pgvector provides zero additional infrastructure overhead and robust transactional consistency. For massive scale, ultra-low latency, and complex payload filtering, Qdrant (self-hosted in Australian VPC or cloud) is our preferred open-source engine. For serverless simplicity with zero cluster management, Pinecone is best-in-class.

How do you prevent data leakage and enforce enterprise permissions in RAG pipelines?

We enforce pre-retrieval and post-retrieval Access Control Lists (ACLs). Every document chunk in the vector database is tagged with user, team, and security group metadata synchronised with your identity provider (Azure AD, Okta, Google Workspace). When an agent queries the vector store, the search payload is cryptographically scoped to only return chunks the specific requesting user is authorized to read.

Why is hybrid search (BM25 + Dense Vectors) superior to pure vector search?

Pure vector search excels at conceptual similarity but frequently fails on exact keyword lookups, product SKU codes, acronyms, or specific customer IDs. Hybrid search fuses BM25 lexical keyword matching with dense semantic embeddings (using Reciprocal Rank Fusion) and passes top candidates to a neural reranker (such as Cohere Rerank or ColBERT). This achieves over 95% retrieval accuracy across both conceptual and exact queries.

How do you satisfy Australian data residency and regulatory privacy laws?

All data pipelines, OCR processors, and vector databases are deployed within Australian sovereign cloud regions (AWS ap-southeast-2 Sydney/Melbourne or Azure Australia East). We utilise Zero Data Retention (ZDR) enterprise APIs, guaranteeing that proprietary customer data is never cached, logged, or used for model training.

Vector Architecture & RAG

Transform unstructured enterprise archives into governed AI knowledge

Eliminate data silos, reduce hallucinations, and unlock high-accuracy semantic search with hardened vector database engineering and Australian data residency.

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