RAG System Development Agency

Retrieval-Augmented Generation (RAG) & Semantic Search Systems

We build custom RAG pipelines and vector database systems using pgvector, Pinecone, Qdrant, and Milvus — connecting your enterprise files, database catalogs, and wikis to LLMs for accurate, hallucinations-free responses.

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Direct Desk: ai@trustoryx.digital

<5%

Hallucination

<150ms

Search Latency

20+

RAG Setups Done

Ragas

Validated Core

Growth Obstacles

Problems This Solves

Your AI chatbot frequently hallucinates, citing fake facts or outdated internal information.

Your company documentation is locked in PDFs, wikis, and legacy SQL servers, inaccessible to LLMs.

You struggle to query millions of embeddings efficiently, suffering from high database search latency.

You worry about data privacy and need role-based access checks for your retrieval pipelines.

Methodology

Our Proven Process

Document Parsing & Extraction

We ingest unstructured files (PDFs, docx) using LlamaParse, preserving tables and image structures.

Chunking & Embedding setup

We design token chunking strategies and generate embedding vectors using OpenAI or Cohere models.

Vector Database Provisioning

We set up Pinecone, Qdrant, or pgvector schemas with optimized HNSW index parameters.

Hybrid Search & Reranking

We write retrieval algorithms blending BM25 keyword matches, semantic vectors, and Cohere reranker steps.

Ragas Evaluation & Deploy

We benchmark search recall and accuracy using Ragas, launching production APIs under secure JWT gates.

Document Parsing & Extraction

We ingest unstructured files (PDFs, docx) using LlamaParse, preserving tables and image structures.

Chunking & Embedding setup

We design token chunking strategies and generate embedding vectors using OpenAI or Cohere models.

Vector Database Provisioning

We set up Pinecone, Qdrant, or pgvector schemas with optimized HNSW index parameters.

Hybrid Search & Reranking

We write retrieval algorithms blending BM25 keyword matches, semantic vectors, and Cohere reranker steps.

Ragas Evaluation & Deploy

We benchmark search recall and accuracy using Ragas, launching production APIs under secure JWT gates.

Scope of Work

What's Included

TypeScript or Python RAG pipeline source code (LlamaIndex / LangChain core frameworks)

Vector database configurations and index definitions (Pinecone / pgvector migrations)

Automated document ingestion scripts (supporting S3, Google Drive, and database webhooks)

Hybrid search resolver algorithms combining dense embeddings and sparse keyword lookups

Ragas automated evaluation reports detailing context recall, precision, and faithfulness

API endpoint routes exposing query interfaces with JWT security checks

30-day post-launch optimization support and vector index maintenance warranty

Growth Targets

Expected Results

Over 95% reduction in LLM hallucinations via strict database grounding rules

Sub-150ms semantic search query latency across millions of token indexes

Support for text tables and markdown tables extracted from complex enterprise PDFs

Context precision scores exceeding 90% validated by Ragas tests frameworks

Granular query filters matching user authentication scopes on database rows

Tech Stack

Technologies We Master

LlamaIndex

Pinecone

pgvector

Qdrant

Milvus

LangChain

OpenAI

Cohere

Exhaustive Solutions

Comprehensive Capabilities

We don't just scratch the surface. Here is a detailed breakdown of everything we can engineer, optimize, and execute for your business.

Document Ingestion Connectors

Building auto-sync connectors to fetch files from S3, Google Workspace, Notion, and SQL databases.

Optimized Embedding Maps

Configuring embedding pipelines using OpenAI text-embedding-3 or open-source HuggingFace models.

Vector Index Tuning

Configuring HNSW, IVF, or flat indexes on Qdrant and pgvector to minimize recall query times.

Hybrid Semantic Search

Combining vector similarity searches with BM25 lexical keyword scoring and rerank middleware.

Evaluation & Guardrails

Using Ragas metrics to check answer correctness and implementing security guardrails to block prompts.

Row-Level Metadata Filters

Integrating query filters matching user permission metadata to secure search outputs.

Specialized Focus

Specialized Services

Explore our specialized engineering teams and tailored solutions for this domain.

AI & Automation

Custom AI automation workflows.

OpenAI Integration

Custom integrations using OpenAI models.

LangChain Development

Expert LangChain AI agents development.

Python Development

Expert Python systems development services.

Transparent Pricing

Investment Plans

Transparent pricing with no hidden fees. Every plan includes dedicated support and monthly reporting.

Foundational RAG MVP

$3,500USD · USD fallback/starting

Ingest up to 100 files, setup a Pinecone serverless index, and connect to OpenAI API.

Ingestion of up to 100 PDFs/Docs

Pinecone serverless index setup

Standard recursive text chunking

OpenAI API embedding connection

Context retrieval prompt injection

Basic API query controller

14-day post-launch support

Full repository code ownership

Build RAG MVP

Enterprise RAG Platform

$8,000USD · USD fallback/starting

Dynamic file sync pipelines, hybrid search, self-hosted pgvector, and Ragas testing.

Unlimited document sync pipelines

pgvector PostgreSQL self-hosted setup

Hybrid search (Dense + Sparse)

Cohere rerank API integration

LlamaParse document tables parser

Ragas accuracy test evaluation

30-day post-launch support

Detailed ingestion documentation

Build Enterprise RAG

Custom Vector Architecture

$15,000USD · USD fallback+/project

High-throughput Qdrant/Milvus cluster managing millions of vectors with metadata filters.

Millions of vector index deployments

Qdrant or Milvus cluster configuration

Custom chunking & embedding models

Metadata-based permission filters

Kubernetes deployment configurations

Priority SLA support contracts

60-day post-launch support

24/7 Slack communication channel

Discuss Custom Vector App

All plans are month-to-month with no long-term contracts. Custom enterprise plans available.Contact us for a tailored proposal.

Why Trustoryx

Why Choose Us

No Long-Term Contracts

Month-to-month engagements. We earn your business every single month.

Dedicated Team

A named strategist, not a rotating cast of juniors. Consistent point of contact.

Revenue-Focused

We report on revenue impact, not vanity metrics. Every dollar is attributed.

Rapid Execution

Strategy in week 1. Execution by week 2. Results tracked from day one.

Support

Frequently Asked Questions

RAG is an AI pattern that fetches relevant documents from a database when a user asks a question, providing those documents as reference context to the LLM. This prevents hallucinations and anchors responses in your real data.

We typically recommend pgvector if you already use PostgreSQL. For serverless SaaS we use Pinecone. For high-performance cluster configurations we choose Qdrant, and for huge enterprise datasets we use Milvus.

We use advanced parser models like LlamaParse or Unstructured to extract tables and convert layouts to structured Markdown, ensuring tables are read correctly by embedding algorithms.

Yes. We deploy local database containers like Qdrant and connect them to local open-source LLMs (such as Llama 3 or Mistral) running on private GPUs using Ollama or vLLM.

Ready to Get Started?

Start with a free audit. We'll analyze your current performance and show you exactly where the growth opportunities are.

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Or email our dedicated desk: ai@trustoryx.digital