'Cancelling' versus 'ending your plan'
Keyword search found nothing.
BoldBellowsRAG API and vector search integration for teams whose keyword search misses documents that use different words
RAG API and vector search integration that finds passages by meaning and returns them with their sources through a simple API
Vector search turns text into embeddings so that passages with similar meaning are found even when the words differ, and the best matches are passed to the model as context. BoldBellows lists the sources the search must cover, how they change, who may see which and the questions people ask, before any text is embedded, splits sources into sensible chunks, turns them into embeddings, stores them in a vector database with their permissions, retrieves the closest chunks for each query and hands them to the model with their sources, then builds RAG API and vector search integration in layers: your sources are collected and cleaned, text is split into chunks that keep their headings and context, each chunk is turned into an embedding and stored in a vector database with its source and permissions, a query is embedded and matched to the closest chunks, filters narrow results by document type, date or access, results are re-ranked for relevance, the API returns passages with their sources, and a RAG endpoint hands them to a model to write a cited answer. Updates are synced so the index follows your documents. Custom RAG API and vector search integration can add hybrid keyword and vector search.
View Pricing
One-off setup from $2,235
No monthly subscription
- RAG API and vector search
- Search by meaning
- Sources and permissions kept
- Chunks keep context headings and sources
- Search by meaning different words, same idea
- Permissions on every chunk people see what they may
- Index follows your documents updates are synced
- A written handover Controls and recovery steps arrive as a document
- No renewal date $2,235 covers the build once, and nothing renews afterwards
Chapter 01 the different-words problem
A search box that needs the exact word hides the document that says the same thing differently
A user searches for 'cancelling a subscription' and the help center says 'ending your plan'. Keyword search finds nothing, and the user opens a ticket. A team that tries to add AI answers pastes the whole knowledge base into every prompt, which is slow, dear and sometimes confused. A prototype index is built once and never updated, so answers drift from the documents. Permissions are ignored, and a chunk from a restricted file shows up in someone's answer. The prototype impresses, then disappoints.
A search for 'cancelling' found the 'ending your plan' article first, and the ticket never got written.
A whole knowledge base in every prompt
Slow, dear and confused.
An index built once
Answers drift from the documents.
Before
After
Before
- Retries and repeats leave extra copies of the search query behind
- Keyword search misses documents that use different words
- Prototype indexes ignore permissions and go stale
After
- The query, the retrieved chunks and their sources sends every repeat back to the same search query
- Meaning-based search finds the right passages with their sources
- The index respects permissions and follows document changes
Chapter 02 How it works
How your documents become an API that finds passages by meaning
Qualify, identify, match and write: the order never changes, and the search query is touched only when it needs to be.
Accept or hold
Each AI Team event is verified and recorded, and it proceeds only if it meets your rule; otherwise it waits with a reason.
Chunk, embed, store
Sources are split into chunks, embedded and stored with their source and permissions.
Retrieve, filter, return
A query finds the closest chunks, filters apply, and passages are returned with sources for a model to use.
What the build relies on in AI Team
- Embeddings find meaning: Vector search turns text into embeddings so that passages with similar meaning are found even when the words differ, and the best matches are passed to the model as context.
- Chunking decides what search can find: Because a passage split in the wrong place loses its meaning, the build splits by headings and sections and keeps the surrounding context with each chunk.
- Read, then write: The route reads RAG and Vector Search API on the query, the retrieved chunks and their sources before every repeat attempt and writes again only when the record is missing.
Chapter 03 What ships in the build
How retrieval works: chunk, embed and filter
Base is where it begins, Steady is where it grows and Checked is where it is held to account.
Trigger filter
Looks at each AI Team failed request as it lands and lets only the ones you approved begin any work.
Document tracking
Follows each source document through the index and stores the outcome, so a re-sent file updates its chunks instead of duplicating them.
Scope limiter
Removes any AI Team detail that RAG and Vector Search API has no use for before a write is attempted.
Source collection
Collects and cleans documents from drives, wikis and databases.
Chunking and embedding
Splits text into chunks that keep context and turns them into embeddings.
Vector database
Stores embeddings with their source and permissions.
Retrieval API
Finds, filters and re-ranks passages and returns them with sources.
Sync
Keeps the index in step with changes to your documents.
Follow-up queue
Puts every unresolved AI Team case in order of age with the person responsible beside it.
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Chapter 04 Who this fits
For real estate firms and add search by meaning to your product or intranet, with an API that returns the best passages and their sources, ready for a model to answer from
People rarely use the same words as the documents, and search by meaning closes that gap, provided the plumbing around it is done carefully.
Developer
"Add search by meaning."
A simple API serves it.
Product manager
"Improve findability."
Different words still match.
Support lead
"Deflect tickets."
Answers come from the right passage.
IT lead
"Respect permissions."
Chunks carry access rules.
Knowledge manager
"Keep the index fresh."
Changes sync.
Founder
"Use your content."
It becomes searchable.
Chapter 05 The options
Three ways to link AI Team and RAG and Vector Search API: keyword search that misses the document because it used different words, a zap or chunking, embeddings, a vector database, permissions, retrieval and a search API
Each can search text. Only one finds passages by meaning, keeps their sources and permissions and stays in step with your documents.
| Question | Exact-word search | Stale indexes | Our retrieval route |
|---|---|---|---|
| What starts the work | A person spots it eventually | Each match of the trigger, relevant or not | A failed request that meets your rule and no other |
| How identity is decided | Whoever is on duty looks it up by name | The spelling of a name or an address | Exact-word search and stale prototypes |
| What reaches RAG and Vector Search API | Details retyped from another screen | Raw values with no structure | Meaning-based retrieval with permissions and sync |
| After an uncertain result | A spreadsheet and a lot of scrolling | The failure waits for someone to see it | Checks live state before touching anything and routes doubts to a person |
The newest state wins
When two AI Team events cross on the way, the later one is kept and the earlier one is logged beside it, so RAG and Vector Search API never steps backward.
A restricted document matches a query from someone without access
The permission filter removes its chunks before ranking, the answer uses only permitted passages, and the restricted file is never mentioned. The query, the retrieved chunks and their sources becomes a lookup in RAG and Vector Search API, and the result lands in one of four places: absent, same, different or doubtful. A fresh search query is the exception: it follows an empty lookup and nothing else.
The agreed identifier, the query, the retrieved chunks and their sources, is fixed first and every later decision is built on it.
Interlude Price follows queries, chunks and sources
Pricing for retrieval: sources and volume
Work through three scope questions for AI Team RAG and vector search API and the price settles on a figure from $2,235.
View Pricing
One setup fee · Range shown first
Pricing
Build your AI Team to RAG and Vector Search API price
Pick a package, answer three short questions about how AI Team data should reach RAG and Vector Search API, and your price and turnaround move as you choose.
Chapter 06 The process
Six steps from keyword search to a retrieval API
From brief to monitored launch there are six steps, and every one ends in something you can read.
-
About 30 minutes
Choose and clean sources
We choose the sources, clean them, note how they change and who may see which.
-
Access review
Approve access
AI Team and the neighboring tools are opened only as far as RAG and vector search API requires, and the pairing keys are agreed before the build.
-
Most of the project
Agree chunking, permissions and sync
The chunking rules, the permission model and the sync schedule are written down and signed off.
-
Before you review
Stress the route
Before it goes live the route is fed repeats, late arrivals and withdrawn permissions inside a safe copy of RAG and Vector Search API.
-
Handover day
Launch with notes
The route starts only after you have read the launch notes, and its first runs are watched by us.
-
About 30 minutes
First-month review
We read the checkpoint log together at day thirty, look at exceptions and drift, and adjust the route to the evidence.
Chapter 07 Turnaround
The schedule behind every package
Timing begins when the sources and permission rules are available to us.
Base
6 to 10 business days
Steady
10 to 16 business days
Checked
16 to 24 business days
Chapter 08 Getting started
Preparing for day one of the RAG and Vector Search API build
A login we can use to read the agreed AI Team items
Access to the sources, the permission rules and a set of real queries to test with
Limited AI Team access for the resources this route uses and nothing beyond them
A shared view of which failed requests matter and which can be left alone
Willingness to test against a practice copy of RAG and Vector Search API
A rough idea of volume, so limits and pacing are set to fit
Chapter 09 Trust
The products are left alone, and the field map decides what moves
Data from AI Team moves only along the paths you approve for RAG and vector search API, and the permissions behind them are no wider than required.
Nothing reaches inside
Neither AI Team nor RAG and Vector Search API is opened up: the build asks them questions and gives them instructions through their public interfaces and nowhere else.
Scoped, revocable access
No credential does more than its own job, and any one of them can be turned off by you alone.
What is not needed is not sent
The search query gets the details the work depends on, while the remainder of the AI Team record is kept exactly where it is.
Look before retrying
The route reads the search query from RAG and Vector Search API on the query, the retrieved chunks and their sources first, so a retry never duplicates a write that already succeeded.
Minimal logging
The log says what happened and whether it worked, and it leaves the contents of the record out.
Full handover
You keep the diagram, the list of credentials and the repair guide, so the route can be run by someone else.
"Good retrieval is plumbing: unglamorous, careful and the reason every answer built on top of it can be trusted."
Steven GrratFounder, BoldBellows
Chapter 10 In their words
What BoldBellows clients say
Verified reviews from business owners who use BoldBellows automations every day, from marketing and data to support and operations.
AI Workflow Automation for Agencies
Running a digital agency with many clients was overwhelming. BoldBellows built one automation dashboard that tracks tasks, sends updates and creates client reports automatically.
Clients love the transparency and we love the time we get back. Productivity is up, and project delays have dropped to almost zero.
AI Customer Journey Automation
We wanted a smoother path from first contact to purchase. BoldBellows built an AI system that guides each customer there automatically.
It sends personalized messages, recommends products and follows up at just the right moment. Customer retention has improved dramatically.
AI Email & Smart Inbox Management
Our inbox used to be chaos. BoldBellows built an AI email system that sorts messages, drafts replies and flags anything urgent.
It learns how we write and gets better every week. We reply faster and no longer miss important messages. It feels like having a personal assistant working around the clock.
AI Booking & Scheduling Automation
Our coaching business needed a better way to schedule. BoldBellows automated all of it: bookings, reminders, cancellations and follow-ups.
No-shows fell by 60% and clients enjoy how smooth it is. It feels personal, not robotic. One of the best upgrades we’ve made.
AI Translation & Localisation
When we expanded abroad we needed fast, accurate translations. BoldBellows built an AI translation system that turns our content into several languages instantly.
The translations read naturally and suit each culture. We now serve customers in five new countries without hiring translators. BoldBellows helped us go global.
AI HR & Recruitment Automation
Our HR team was drowning in applications. BoldBellows automated screening, skills-based filtering and interview scheduling.
It saves us hours every week and means we only interview qualified people. Hiring is now faster, better organized and more professional.
AI Accounting & Invoice Automation
BoldBellows automated our whole invoicing process: creating invoices, sending reminders and tracking payments.
Late payments dropped sharply, and our accounting team finally has time for strategy instead of repetitive admin. Worth every penny.
AI Data Entry & Reporting Automation
We used to spend hours collecting data and building weekly reports by hand. BoldBellows automated all of it.
The reports are clean, visual and easy to read, and our managers rely on them for quick decisions. Human error is gone and our numbers are more accurate across the board.
AI E-commerce Listings Automation
BoldBellows automated our product listings. The AI writes descriptions, organizes photos and publishes listings across several marketplaces.
It even answers customer questions instantly. Our store looks more professional and sales have noticeably increased.
AI Chatbot & Customer Support
BoldBellows built an AI support chatbot for our online boutique, and it has changed how we run the business. We used to have two full-time staff answering the same questions all day. Now the AI handles order tracking, product recommendations and even refund requests on its own.
What surprised me most is how natural it sounds. Customers compliment the ‘agent’ without realizing it’s AI. Our support workload dropped by 75% and customer satisfaction went up dramatically.
AI Content & Social Media Automation
Our marketing agency couldn’t keep up with content. BoldBellows set up an AI content engine that writes blog posts and captions and even designs our social media creatives.
It spots trends and suggests topics for us, so we stay ahead of competitors. We now produce three times the content with the same team, and engagement has soared. They didn’t just automate our workflow, they lifted our whole brand.
AI Lead Qualification & Sales Automation
Our sales team wasted hours chasing cold leads. BoldBellows built an AI lead-scoring system that reads behavior, email engagement and buying signals.
It filters out weak leads and sends personalized follow-ups automatically, so our team only talks to people who are ready to buy. Conversions rose 40% in the first month. It paid for itself straight away.
AI Video & Voice Automation
We publish product videos every week, and BoldBellows introduced us to AI video automation. The system writes the script, voices it and produces the finished video in minutes.
The quality is remarkable: professional voiceovers, clean visuals and smooth editing. Production time went from three days to under 30 minutes, and we scaled our video marketing without adding cost.
AI Document & Contract Automation
Onboarding was slow because we wrote every contract by hand. BoldBellows automated the lot: contracts, welcome emails and client setup.
What took 48 hours now takes under 10 minutes, and clients regularly tell us how professional it feels.
AI Social Comment & DM Automation
We get thousands of comments and messages every week. BoldBellows built an AI system that replies instantly, filters spam and surfaces the important ones.
Engagement is up and our page looks active around the clock. It’s like having a full social media team that never stops.
AI CRM Automation
Our CRM was barely used because updating it by hand was tedious. BoldBellows automated data entry, tagging, segmentation and follow-ups.
Now the CRM is always up to date and our campaigns are far more accurate. It improved our whole sales pipeline.
AI Voice Assistant
BoldBellows built a custom AI voice assistant for our customer hotline. It answers questions, books appointments and handles simple requests.
Customers love how fast it is, and our call-centre workload dropped dramatically. One of the smartest upgrades we’ve made.
AI Automation Partner
BoldBellows is hands-down the best AI automation partner we’ve worked with. Their systems are fast, reliable and built around how our business actually works.
Every automation they’ve delivered, from customer service to content, runs flawlessly. We’ve saved time, cut costs and grown faster than we expected.
Chapter 11 Questions
RAG and vector search: what developers and product teams ask
Below are plain answers to what buyers like you usually ask first, on the RAG API and Vector Search Integration service.
What does a zap miss in AI Team RAG and vector search API?
If all you need is search that finds what people mean, not just what they typed, a zap will do; to find relevant passages by meaning and hand them to a model with their sources, it will not.
What is a vector database?
A database that stores embeddings so it can find passages with similar meaning to a query.
Can it respect who may see what?
Yes. Each chunk carries access rules that filter results for every asker.
How is the index kept current?
By syncing changes on a schedule or when documents change.
Which vector databases can it use?
Common ones such as Pinecone, pgvector and others, as confirmed in the quote.
Can it combine keyword and vector search?
Yes. Hybrid search can be added to catch exact terms and meanings.
What drives the price of retrieval integration?
Sources, volume and the permission model fix one price; hosting for the vector database is separate.
Is field matching for AI Team RAG and vector search API part of the package?
Sources, chunking, permissions and the sync schedule are set out for approval before any text is embedded.
Can the RAG and vector search API rules differ by team or product?
Yes. We turn your rules for RAG and vector search API into explicit steps in AI Team, starting at $2,235.
Does the handover cover AI Team changes?
Affected records pause when either side changes, and you are told which ones.
Give your product search by meaning, with sources and permissions
Tell us what you want to be able to find and who may see it; your written quote arrives before any document is embedded.
- From $2,235, paid once
- Chunks keep context
- Permissions on every chunk
- No monthly fee from us