Every AI assistant that needs to answer accurately based on a company's own data rests on the same foundation: a vector database that can find the right piece of content, fast, no matter how much data sits behind it.
That sounds like a detail. In practice, it's often the part that takes the longest to get right: scaling, indexing, isolation between customers, and a price tag that doesn't spiral out of control as the data volume grows. Most teams end up spending more time on that part than on the actual product.
What the Problem Really Is
If you build your own vector search, you typically face three decisions at once: which database, how to operate it, and how to scale it as data or traffic grows. None of those decisions matter to the customer, who just wants a fast, correct answer.
That's exactly the layer we've built a managed solution for: a hosted vector database, production-ready from day one.
What It Looks Like in Practice: ScrapeGoat
A concrete example is ScrapeGoat, a tool that builds AI chatbots from companies' own websites. You point the tool at a website, and it turns into a chat that can answer questions based on what the site actually contains.
That creates a very concrete requirement: when a visitor asks a question, the chat needs to find the right section of the website — out of possibly thousands of pages — instantly. A regular database is good at looking things up when you know the exact name or the right keywords in advance. But visitors rarely ask using the same words that appear on the page ("can I bring my dog" instead of "pet policy"). That requires a vector database that finds the right content based on meaning, not literal words.
Step 1: The Website Becomes Searchable Data
When a new customer's website is added, every page is read, split up, and stored as vectors in our managed vector database. This happens automatically, without anyone having to manually set up a database or plan capacity in advance.
The only thing the customer has to do themselves is add a small JavaScript snippet to their website. From there, everything runs on its own: the content is continuously scanned, split up, and loaded into the database, and the chat updates automatically whenever the page changes — no manual setup, no separate integration, and no waiting on a development team.
From website to searchable knowledge, automatically, and kept continuously up to date.
Step 2: A Question Becomes an Answer
A visitor types a question into the chat. The question itself is converted into a vector, matched against the right content in the database, and ranked so the most relevant answer comes first.
Step 3: It Grows Without Grinding to a Halt
As ScrapeGoat gains more customers and more pages get indexed, the number of vectors grows significantly. Because it's built on a managed vector database, no one has to manually scale the database up — it's built to grow with demand.
More customers and more indexed pages mean more vectors. The database scales along with it, without anyone having to turn any dials manually.
The Result
ScrapeGoat can spend its time making the chat experience itself better, instead of operating and scaling a database on the side. Vector search stays fast and stable whether there are ten pages behind it or ten thousand.
What You Choose Between
The solution comes in several tiers, from a free option to get started, through a shared solution for early-stage workloads, to dedicated infrastructure in various sizes for those who need more isolation or capacity. You typically start small and move up as demand grows, without having to switch databases along the way.
Just One Piece of a Bigger Puzzle
Our managed vector database is a standalone service you can purchase on its own. But it also fits naturally into a larger pipeline: from ingest, through embedding and retrieval, to rerank, bundled as a RAG package, where the database layer itself is handled by the same solution.
We deliberately keep answer generation (the inference itself) outside the package. You choose which model generates the answer yourself, and pay for it separately and based on usage, so you're not locked into one particular model just because you chose our database or RAG layer.
See you in the next installment, where we dig into the RAG workflow itself: ingest, embedding, retrieval, and rerank, and how it all works together in practice. The part after that is about MCP servers.
Is This Relevant to You?
Are you building something that leans on semantic search or RAG, and spending more time on the infrastructure than on the actual product?
That's far from the only place we can help. Beyond our managed vector database and the RAG package, we also advise on how to put together your AI architecture from the ground up: which models make sense for which tasks, how data should be structured and secured along the way, and how it's all operated in practice. We can also handle the inference itself — the part that generates the answers — as a standalone, usage-based service where you're free to choose the model.
Get in touch, and let's talk about where you could start.