Development That Helps Users Find Exactly What They Need
You need AI powered search that understands what your users actually mean, not a keyword matcher that returns irrelevant results. Whether you are looking for an AI search development company to build intelligent search into your product, need experienced AI search developers to replace a failing search system, or want to build AI search that surfaces the right content from thousands of documents, the starting question is always the same: what are your users searching for, and why can they not find it? Your end-to-end semantic search development covers everything from data indexing and embedding generation through to retrieval, ranking, and deployment. That means custom AI search for products, documents, knowledge bases, support portals, and enterprise data. Your work spans AI powered search for enterprise, SaaS platforms, e-commerce, and content-heavy applications. Ready for an AI search development quote? Tell us what your users cannot find.
AI powered search typically costs between $20,000 and $200,000 depending on data volume, search complexity, and integration requirements. A focused semantic search MVP takes 8 to 14 weeks. Enterprise-scale search platforms take 4 to 12 months. Data preparation and relevance tuning are the biggest cost drivers.
Core Capabilities and Features
Semantic Search for Products and E-commerce
If your customers search your product catalogue and get irrelevant results, you are losing sales. AI powered product search understands natural language queries ('comfortable running shoes for flat feet'), handles typos and synonyms, and surfaces results based on meaning. Product search is built using vector embeddings combined with metadata filtering (price, category, availability, brand) so users can search naturally and still apply filters. This integrates with your existing e-commerce platform (Shopify, WooCommerce, Magento, or custom) through APIs.
- Understands natural language queries, handles typos and synonyms, and surfaces results based on meaning
- Vector embeddings combined with metadata filtering (price, category, availability, brand) for natural search
- Integrates with your existing e-commerce platform (Shopify, WooCommerce, Magento, or custom) through APIs
Enterprise Knowledge Search
Most internal search is broken. Employees spend 20% to 30% of their time looking for information across scattered systems: Google Drive, Confluence, SharePoint, Notion, Slack, email, internal wikis. Enterprise AI search connects to all of these sources, indexes the content, respects permission controls, and gives employees a single search interface that actually returns useful answers. Enterprise search is built with role-based access, federated indexing across multiple data sources, and natural language query support.
- Connects to Google Drive, Confluence, SharePoint, Notion, Slack, email, and internal wikis in one interface
- Role-based access and federated indexing across multiple data sources with permission controls
- Natural language query support so employees find answers instead of spending 20% to 30% of their time searching
RAG-Powered Search and Retrieval
Retrieval-augmented generation (RAG) combines search with generative AI. Instead of returning a list of documents, the system retrieves the most relevant passages and generates a natural language answer with citations. This is the foundation of AI assistants that answer from your proprietary data. RAG search pipelines are built for support portals, documentation sites, legal research, medical knowledge bases, and internal Q&A systems.
- Retrieves the most relevant passages and generates a natural language answer with citations
- Foundation of AI assistants that answer accurately from your proprietary data without hallucinating
- Built for support portals, documentation sites, legal research, medical knowledge bases, and internal Q&A systems
Why It Matters
If your customers cannot find what they are looking for, they leave. If your employees cannot find the document they need, they waste an hour asking colleagues. If your support agents cannot find the answer in the knowledge base, they escalate a ticket that should have been resolved in 30 seconds. Search is one of the most underinvested features in most products and organisations. It is also one of the highest-impact ones. A 5% improvement in product search relevance can drive a measurable increase in conversion. An enterprise search that actually works can save every employee 30 minutes a day. The companies that get the most from AI powered search are the ones who treat it as a product, not a feature. That means measuring it, tuning it, and investing in it over time. The ones who struggle are the ones who deploy a vector database, declare it done, and never look at the analytics.
By the Numbers
$21.0B
Projected global AI search engine market size in 2026, growing at a 13.6% CAGR. By 2035 the market is expected to reach $66.2 billion. Investment in AI search is accelerating across consumer and enterprise segments.
Source: Future Market Insights, 2025
$7.47B
Global enterprise search market size in 2026, growing at a 9.31% CAGR. By 2031 the market is expected to reach $11.66 billion. Organisations are replacing legacy keyword search with AI-powered semantic and conversational search.
Source: Mordor Intelligence, 2026
80%+
Of enterprises are expected to integrate generative AI into their search capabilities by 2026. RAG-powered search is moving from experimental to standard deployment for knowledge retrieval.
Source: IMARC Group, 2025
54%
Of office workers spend more time searching for files than on actual work. Enterprise search is broken in most organisations, and AI search is the fix that makes information discoverable instead of buried.
Source: ShareFile / Gartner, 2025
95%
Accuracy improvement when RAG pipelines ground LLM outputs in enterprise data, compared to 70% accuracy without retrieval grounding. RAG search solves the hallucination problem by anchoring answers in real content.
Source: Mordor Intelligence, 2025
"The search projects that deliver the highest ROI are almost never the ones with the most sophisticated AI. They are the ones where someone actually sat down, looked at what users were searching for, measured how often they found what they needed, and fixed the gaps. Good search is not about the model. It is about the data, the relevance tuning, and the willingness to keep measuring."
Technologies
Our Tech Stack
Our Process
How we turn ideas into reality.
Data Audit and Indexing Strategy
Your data is assessed: what formats it lives in (structured databases, PDFs, web pages, product catalogues, internal documentation), how much of it there is, how clean it is, and how frequently it changes. Then the indexing pipeline that converts your content into searchable embeddings is designed.
Embedding and Vector Infrastructure
The right embedding model (OpenAI, Cohere, open-source sentence transformers, or domain-specific models) and vector database (Pinecone, Qdrant, Weaviate, pgvector, Elasticsearch) are selected based on your scale, latency, and cost requirements.
Search Pipeline and Relevance Tuning
The retrieval pipeline is built: query processing, embedding generation, vector search, hybrid search (combining vector with keyword for best results), re-ranking, and result presentation. Relevance is tuned using your data and real user queries until the results are consistently accurate.
Deployment and Monitoring
Deployment to production with performance monitoring (latency, recall, click-through rate, zero-result rate), analytics dashboards, and iteration based on real search behaviour. Search quality is not a one-time achievement. It requires ongoing measurement and tuning.
Pricing
Investment Overview
Data Volume and Complexity
Indexing 1,000 product descriptions is a different project from indexing 500,000 multi-page legal documents. The volume, format variety, and update frequency of your data directly impact the indexing pipeline cost.
Embedding and Infrastructure
Open-source embedding models reduce cost but may require fine-tuning for domain accuracy. Commercial models (OpenAI, Cohere) deliver strong out-of-the-box results but add per-query costs. Vector database hosting scales with data volume.
Search Complexity
Simple semantic search over a single data source costs less than hybrid search across multiple sources with faceted filtering, personalisation, and re-ranking. Every layer of sophistication adds development time.
Everything we do at Techneth is built around making data move reliably between the systems that matter. If you want to understand our approach before committing, you can read more about our team and how we work. Or explore the full range of digital product and development services we offer, like ai powered search and recommendations. And if you already know what you need, get in touch directly and we will find time to talk.
Frequently Asked Questions
Everything you need to know about this service.
- How long does it take to build AI powered search?
- A focused semantic search MVP over a single data source typically takes 8 to 14 weeks from data assessment to production deployment. Enterprise search platforms spanning multiple data sources with federated indexing, permission controls, and relevance tuning take 4 to 12 months. The biggest variable is data preparation: if your content needs cleaning, restructuring, or enriching, add 4 to 8 weeks.
- What is the difference between semantic search and keyword search?
- Keyword search matches exact words. If a user searches for 'affordable sedan' but your database says 'budget car', keyword search returns nothing. Semantic search converts both the query and the content into numerical representations (embeddings) that capture meaning. It understands that 'affordable sedan' and 'budget car' are about the same thing and returns the right result. Most modern AI search implementations combine both approaches in a hybrid model for best results.
- What is a vector database and do we need one?
- A vector database stores embeddings (the numerical representations of your content) and enables fast similarity search at scale. If you have thousands to millions of documents or products, a vector database is essential for fast, accurate semantic search. Options include Pinecone (fully managed), Qdrant (open-source), Weaviate (open-source), pgvector (PostgreSQL extension), and Elasticsearch (hybrid). The recommendation is based on your scale, budget, and infrastructure preferences.
- What is RAG and how does it relate to search?
- RAG (retrieval-augmented generation) combines search with generative AI. When a user asks a question, the system first retrieves the most relevant passages from your content using semantic search, then passes those passages to a language model to generate a natural answer with citations. It is the foundation of AI assistants that answer accurately from your proprietary data, and it solves the hallucination problem by grounding every response in real content.
- Can you build AI search into our existing product?
- Yes. AI search is embedded into existing applications through APIs and SDKs. Whether you are adding search to a SaaS platform, an e-commerce site, a support portal, or an internal tool, the search system integrates into your current architecture. Real-time indexing, permission-aware retrieval, and sub-second latency are handled within your existing tech stack.
- Do we own the search infrastructure after the project?
- Yes. You receive full ownership of all code, configurations, embedding pipelines, vector indexes, and documentation. Everything runs on your infrastructure and accounts. Technical handoff sessions are also provided so your team can maintain, update, and extend the search system independently.
Ready to get a quote on your ai powered search and recommendations?
Tell us what you are building and we will put together a scoped proposal within 3 business days. Here is what happens when you reach out:
- 1You fill in the short project brief form (takes 5 minutes).
- 2We review it and come back with initial thoughts within 24 hours.
- 3We schedule a 30 minute call to align on scope, timeline, and budget.
- 4You receive a written proposal with fixed price options.
No commitment required until you are ready. Request your free ai powered search and recommendations quote now.
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