Engineering™ · Data & Intelligence

AI & Data Engineering

We clean, structure, and vectorize your corporate data to feed enterprise AI and RAG systems at scale.

Vector Databases · Advanced semantic chunking · Embedding pipelines · Data cleaning for LLMs. We transform messy unstructured document repositories into high-precision vector knowledge bases built for hybrid semantic search.

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Scope

Vector Database Deployment · Document Ingestion & Chunking · Hybrid Search (Dense + Sparse) · Metadata Tagging Pipelines

Estimated Timeline

5–8 weeks

Platforms

Qdrant / Pinecone · pgvector (PostgreSQL) · Unstructured.io / LlamaParse · LangChain / LlamaIndex

Target Fit

Is this for your company?

This service is for you if

✓You tried building an internal RAG or chatbot and got vague, hallucinated answers due to poor chunk quality.
✓Your high-value operational knowledge is trapped in complex PDFs with multi-column text, tables, or scanned receipts.
✓You need an automated pipeline that updates vector indices whenever internal teams upload or modify documents.
✓You require hybrid search combining semantic contextual understanding with exact keyword/part-number matching.

You probably do not need it if

✕Your data is already 100% structured in relational SQL tables and only requires standard numerical BI reporting.
✕You lack sufficient proprietary internal documentation to warrant maintaining a dedicated vector database.

Problem Space

What we solve

Advanced Parsing of Complex Documents

Accurate extraction of structured text, hierarchical tables, and metadata from scanned PDFs and technical diagrams.

Context-Aware Semantic Chunking

Splitting documents along logical sections and topical boundaries rather than arbitrary character-count breaks.

High-Accuracy Hybrid Retrieval (Dense + Sparse)

Fusing dense vector embeddings (conceptual meaning) with sparse BM25 indices (exact part codes and identifiers).

Automated Continuous Re-Indexing Pipelines

Automated cloud ingestion that detects document additions, updates, and removals, refreshing embeddings seamlessly.

Engineering Process

How it works

01 — Diagnose

Document Corpus & Format Audit

We assess document complexity: multi-column formats, technical tables, OCR requirements, and total file volumes.

02 — Design

Vector Architecture & Chunking Strategy

We select the embedding model, define chunk sizes and overlaps, design metadata schemas, and choose distance metrics.

03 — Build

Ingestion Pipeline & Vector Store Setup

We deploy the vector database (Qdrant/pgvector), build the document extraction pipeline, and index the corpus.

04 — Launch

Retrieval Quality Benchmarking & API Delivery

We evaluate Context Relevance and Faithfulness using RAG evaluation suites (Ragas) and deliver production search APIs.

Deliverables

What we deliver

Upon completion you will have

✓Production-ready vector database provisioned and indexed (Qdrant, Pinecone, or pgvector).

✓Automated continuous document ingestion pipeline generating dense embeddings.

✓OCR and advanced table extraction module for complex documents.

✓Unified hybrid search API ready for LLMs and autonomous agents.

✓Retrieval quality benchmark dataset and evaluation scoring reports (Ragas).

Delivery Plan

Implementation Phases

Tiempo típico de proyecto:5–8 weeks

Weeks 1–2Phase 1

Corpus Audit & Embedding Selection

Document structure analysis, embedding benchmark testing, and initial retrieval accuracy evaluations.

Weeks 3–5Phase 2

Extraction Pipeline & Semantic Chunking

Parser development for complex tabular layouts, metadata tagging, and semantic chunking engineering.

Weeks 6–7Phase 3

Vector Store Deployment & Historical Indexing

Vector database provisioning, bulk embedding generation, and search latency optimization.

Week 8Phase 4

Relevance Benchmarking & Production Handoff

Testing against real user queries, threshold calibration, and production API documentation.

Pricing Guidance

Estimated Investment

Target Investment

USD 5,800

Includes vector architecture, complex document extraction pipeline, hybrid search API, and RAG evaluation suite.

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Real-World Proof

Impact Case Study

From Useless Hallucinations to 94% Accurate Document Retrieval across 12,000 Technical PDFs

Initial problem

Industrial machinery company with 12,000 technical manuals whose field technicians received erroneous AI bot responses due to chopped-up technical spec tables.

Technical intervention

Pipeline redesign with LlamaParse for complex tables, section-aware chunking, and Qdrant hybrid search.

Outcome achieved

Retrieval accuracy surged from 41% to 94%, completely eliminating technical hallucinations and saving over 1.5 daily hours per field technician.

Clarifications

Frequently asked questions

Why are chunking and parsing so critical for AI systems?

Language models are only as reliable as the context retrieved. If your data pipeline feeds an LLM fragmented text cut midway through a specification table, the model will hallucinate. Clean data engineering is the single most important factor in eliminating AI errors.

Should we use pgvector in PostgreSQL or a dedicated vector database like Qdrant?

For smaller workloads under 500,000 vectors where you already run PostgreSQL, pgvector is cost-effective and simple. For larger corpora requiring sub-millisecond latency and complex metadata filtering, dedicated vector engines like Qdrant or Pinecone are superior.

How do vector indices stay up to date when documents are modified?

We configure event-driven webhooks with your storage buckets (Google Drive, S3, SharePoint). When files are modified or deleted, the pipeline automatically updates or purges the corresponding vector chunks.

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