Retrieval-Augmented Generation. Plain English: before answering, the AI looks up the answer inside your actual documents — your policies, your scripts, your product list — instead of guessing. It only tells the customer what is in your files. Nothing invented. Nothing hallucinated.
At 10,000 products, keyword search returns noise. At 100,000 — users give up and call your support team instead. Your support team is answering search questions. That is not a people problem. It is a search problem.
| Query | Keyword search returns | RAG search returns |
|---|---|---|
| "quiet dishwasher" | Noise Every product containing either word. 200+ results, most irrelevant. |
Intent matched The 3 models rated lowest for operating noise in your catalog. |
| "maternity leave" | Noise Every document containing those words. Staff reads through 40 pages to find the answer. |
Intent matched The exact policy clause with number of weeks and eligibility rules. |
| "force majeure construction contract 2023" | Noise 400 documents. Attorney manually reviews to find what is legally relevant. |
Intent matched The 5 clauses that are directly legally relevant to the query. |
| "software engineer Bangkok 3 years" | Noise Every job with any of those words. Misses jobs titled "Developer" or located in "กรุงเทพ". |
Intent matched Software engineering roles in Bangkok, ranked by relevance to 3 years' experience — in any language. |
Your staff spends 20 minutes manually looking up answers during live customer calls. The customer is on hold. The agent is searching. The call log shows "hold: 18 minutes."
That is not a call center problem. That is a search problem. The agent is not slow — the search is broken. RAG search in co-pilot mode returns the answer on the agent's screen in under 2 seconds. The hold time disappears.
Your customers leave your product page when the search returns the wrong items. They do not call to complain. They go to a competitor whose search works.
You never see this in your analytics — it shows as a bounce. The lost sale is invisible. The only thing visible is that conversion on search results is lower than it should be. The fix is the search engine, not the products.
RAG search loads your documents and data once — products, policies, listings, records, anything — and returns meaning-matched results on every query. Not by keywords. By meaning.
Use smart filters and AI-powered relevance sorting to find the right opportunities faster across multiple industries and countries.
AI Super RAG search powered by GDP industry data. One query searches Manufacturing, Retail, Construction, IT, Healthcare and more across every country simultaneously.
4,190 jobs. 13 countries. 30 languages. Searched by meaning, not keywords. Type a natural language query: "software engineer Bangkok 3 years" or "remote marketing manager Southeast Asia" — and watch what comes back. This is the same engine we deploy on your data.
Open Live Demo →Opens jobfreelancethai.com/search.html in a new tab. No signup required.
The technology is industry-agnostic. Only the data changes.
If you are already on any BPO option — the Free RAG Search Engine on your data is already in your plan. No extra line item. No separate quote. Send your documents and we index them as part of your deployment. If you only need search — no voice, no AI hiring tools — there is a standalone track below.
Your documents — any format your team already has them in. Examples:
Your deployment partner handles the submission as part of your onboarding. Nothing technical on your side.
RAG search is bundled into every BPO option at no additional charge. It is part of how Maya works during calls and on text channels — not a separate product. The infrastructure cost per client is under $20 total. We absorb it.
The search engine and infrastructure are already built. What changes per client is the front end: your name, your logo, your result card layout, your color scheme.
Customization is scoped per project. Basic customization — name, logo, colors, result template — is a straightforward engagement. Larger customizations (multi-language UI, custom scoring layers, API integrations) are quoted separately.
Send us 100 records from your data — CSV, JSON, PDF, or a spreadsheet export. We index them, run sample queries, and send back a demo within 48 hours. Free. No commitment. No pitch deck. You see exactly what your users would see before agreeing to anything.
Send 100 Records — Free Test →Any format. Any industry. We index your sample, run real queries against it, and send back the results within 48 hours. You see exactly what your customers or staff would see — before committing to anything.
Technology licensed by AI Vision Automations · ← Back
RAG Search — Retrieval-Augmented Generation — is a semantic vector search engine that searches your actual documented content rather than matching keywords. Every call center script, escalation guide, training manual, SOP, HR policy, compliance document, FAQ file, and product catalog is embedded as a 1,536-dimensional vector using Voyage AI voyage-large-2 and stored in a Supabase pgvector HNSW index. When an agent or candidate asks a question, the system runs a cosine similarity search and returns the relevant excerpt in under 50 milliseconds — the correct answer from your own documentation, live on screen, before the customer finishes asking. This is not a chatbot guessing from general knowledge. It searches only your corpus, returns only your procedures, and cites only your source files. Call center agents no longer need to put callers on hold to find information — the co-pilot delivers the answer in real time.
RAG Search covers any corpus in any industry: call center call scripts and opening spiels, HR policy and compliance documents, product catalogs for retail and logistics, legal and regulatory documents for finance BPO, medical procedure guides for healthcare BPO, ITES technical documentation, KPO research repositories, and onboarding materials for any organization that hires people. Cross-language semantic search works across all 30 languages including Filipino, Hindi, Thai, Vietnamese, Bahasa Indonesia, and Bahasa Melayu — the corpus can be in English and queries can arrive in any of the 30 supported languages. AIVA candidate screening evaluation is included: when candidates are sourced via the RAG search pipeline, AIVA scores each CV 0–100 with a HIRE / INTERVIEW / HOLD / REJECT decision and full written rationale across five dimensions. Building a custom RAG pipeline from scratch using an agency or freelance developer typically costs $19,000–$48,000 in initial development plus $2,000–$5,000 per month in ongoing maintenance — RAG Search is bundled free with every BPO plan and requires no development work from the client.
RAG Search pricing: Track 1 is bundled at $0 with all CONNECT, AIVA, Maya Voice, and Maya Chat plans — no extra line item, no setup fee, web development fully included. Track 2 is standalone for any organization that only needs document search without the full suite — send 100 records via email for a free 48-hour test, then contact to scope the corpus size and monthly query volume. The live public demo runs on jobfreelancethai.com using 4,190 real job listings across 13 countries, 30 languages, and 16 call center and BPO subcategories — any visitor can test the semantic search in real time. Philippine BPO call center hubs served include BGC, Makati, Alabang, Cebu, Davao, Clark Pampanga, Iloilo, Baguio, GenSan, CDO, Marikina, Tarlac, and Olongapo. India ITES and BPO operations served include Bangalore, Hyderabad, Chennai, Mumbai, Delhi/NCR, and Pune. Call center bpo meaning, call center non voice, call center work from home, call center online job, and call center tools — all document types indexed and returned in under 50ms across all supported languages and markets.