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TECHTIMIZE

AI-Native Engineering

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AI Development & LLM Integration

AI-Powered Search

Semantic search and RAG pipelines built on Pinecone, Weaviate, and pgvector — replacing keyword search with natural-language understanding that retrieves the right answer from millions of documents in under 200ms.

Overview

What is AI-Powered Search?

Semantic search and RAG pipelines built on Pinecone, Weaviate, and pgvector — replacing keyword search with natural-language understanding that retrieves the right answer from millions of documents in under 200ms. Our team brings production-grade expertise to every engagement, ensuring your ai-powered search implementation delivers measurable business outcomes from day one. We architect, build, and maintain solutions that scale with your organisation and satisfy GCC regulatory requirements.

What's included

Vector database selection and optimised schema design (Pinecone, Weaviate, pgvector)
Intelligent document chunking strategies: fixed, semantic, and hierarchical
Hybrid retrieval combining dense embeddings and sparse BM25 search
Cross-encoder re-ranking pipeline for top-k relevance precision
Multi-tenancy with namespace isolation and per-user access control
Real-time index updates with zero-downtime ingestion pipelines
Key Benefits

Why It Matters

The measurable outcomes our clients achieve with AI-Powered Search.

Above 98% Retrieval Accuracy

Hybrid search and cross-encoder re-ranking eliminate irrelevant context and surface the exact answer users need.

Sub-200ms Query Latency

Optimised ANN indexes and caching layers deliver near-instant semantic search at enterprise scale.

Data Residency Compliant

Deploy on-premise or AWS me-south-1 Bahrain so your proprietary knowledge never leaves your jurisdiction.

Scales to Billions of Vectors

Architecture designed to grow from prototype to billions of document embeddings without a rebuild.

Delivery Lifecycle

How We Deliver

A structured, transparent process from kick-off to launch and beyond.

1
Discovery1 week

Data Audit & Source Mapping

Inventory all knowledge sources — documents, databases, APIs — and assess quality, format, volume, and access patterns.

2
Planning1 week

Chunking Strategy & Embedding Selection

Select chunking approach (fixed, semantic, hierarchical) and benchmark embedding models on your domain data for accuracy vs. cost.

3
Architecture1 week

Vector Store Selection & Schema Design

Select the optimal vector database (Pinecone, Weaviate, pgvector), design the index schema, namespace strategy, and access controls.

4
Build2–3 weeks

Vector Store Build, Ingestion & Hybrid Retrieval

Deploy and populate the vector database, implement hybrid dense+sparse retrieval, and add cross-encoder re-ranking.

5
QA & Security1 week

RAGAS Evaluation & Security Review

Run RAGAS evaluation suite against representative queries, validate data residency compliance, and configure multi-tenant access controls.

6
Launch & ScaleOngoing

Go-Live, Monitoring & Index Updates

Launch with retrieval quality dashboards, configure real-time index update pipelines, and establish monthly accuracy review cadence.

Use Cases

Industries & Scenarios

Where AI-Powered Search delivers the most impact.

Enterprise knowledge base Q&A portal
Legal document search and case law retrieval
Medical literature and clinical guideline retrieval
Internal HR policy and procedure lookup
Contract review and obligation search
Customer support knowledge retrieval
Regulatory compliance document lookup
Tech Stack

Tools & Technologies

The proven technology stack we use to deliver AI-Powered Search.

PineconeWeaviatepgvectorLangChainLlamaIndexOpenAI EmbeddingsCohere RerankFastAPINode.js
FAQs

Frequently Asked Questions

Everything you need to know about AI-Powered Search.

Ready to Start?

Ready to get started with AI-Powered Search?

Talk to our team and get a tailored proposal in 48 hours.