Many agents, one grounded brain.
Your whole company's knowledge answers in seconds - grounded in your real docs, cited so you can verify it, and able to take the next step instead of just replying.
The capability, defined.
This is the deep end of the ladder: a system, not a chatbot. A RAG layer that answers from your verified documents, tickets, and history instead of guessing - with citations you can click - and, above it, multiple agents coordinating like a team: an orchestrator planning, specialists executing in parallel. It's how you move from one helpful reply to a system that runs the whole workflow, grounded in what's actually true at your company.
Not a ChatGPT wrapper. Not a model with a prompt that guesses when it doesn't know. It's a grounded RAG layer over your verified sources with click-through citations, and above it a team of agents - an orchestrator planning, specialists executing in parallel.
What this costs you today.
The answer exists somewhere at your company - in a doc, a ticket, someone's head - and finding it still takes a Slack message and a half-day wait.
The anatomy of the system.
Most RAG failures are retrieval failures, not model failures. So we engineer the knowledge layer to a real standard first, then put agents on top of it - and we measure groundedness the whole way.
Engineered, not prompted.
We follow Anthropic's own complexity ladder - start simple, add agents only where they earn their keep - and build on Claude Code, the Claude Agent SDK, n8n, Railway, Vercel, Cloudflare, and Supabase.
What this looks like in the wild.
The reliability that ships.
Where most RAG quality is won or lost - hybrid search plus a re-ranker is the 2026 production default precisely because naive single-vector retrieval misses the right passage too often.
The grounding bar that separates a demo from production - every sentence carries a chunk ID, and uncited claims get stripped or rewritten before the answer ships.
Groundedness scored on live traffic, not just at launch - and only ~15% of GenAI deployments instrument this today (Gartner), which is exactly why most company chatbots quietly drift.
↳ Industry benchmarks and engineering standards, not Anfloy client metrics - we report your real numbers once you're live.
Named tools, and why.
The model is fungible - the system is the moat. Here's what we build it on, and the reason each earns its place.
Why not just a ChatGPT wrapper?
A naive chatbot is a model with a prompt - it answers from its training data and guesses when it doesn't know. A grounded RAG system answers from your verified sources, cites them, and is built to say 'I don't know' instead of inventing. The gap is the difference between a demo and something you'd put in front of a customer.
The honest fit check.
Companies with knowledge scattered across docs, tickets, and tools - support, ops, sales enablement, or a product that needs answers grounded in proprietary data - who need answers people can actually trust and verify.
If one well-scoped agent or a single retrieval step already does the job, you don't need a multi-agent system - we'll build the simpler thing. And if your knowledge base is thin, messy, or has no stable source of truth, the honest first move is fixing the data, not wrapping a model around it.
The honest answers.
When do I actually need multi-agent instead of one agent?
Only when the work is genuinely parallel or too broad for one context window - research across many sources, or several distinct specialist roles working at once. We follow Anthropic's guidance: start with the simplest thing that works and add agents only when simpler patterns fall short. Plenty of 'company brain' projects are a strong single-agent RAG system, and we'll tell you honestly when that's all you need - it's cheaper and more reliable.
How do you stop it from hallucinating?
Retrieval grounding plus continuous evaluation. The system answers only from your verified sources, attaches a citation to every claim via the Citations API, and is built to defer to a human rather than guess when there's no good match. We score groundedness, faithfulness, and context-precision continuously with an LLM-as-judge against a golden dataset - so unsupported answers get caught and measured on real traffic, not just hoped away at launch.
Who owns the knowledge base and the agents?
You do, entirely. The RAG layer, the vector store, the agents, and your data all live on your infrastructure and accounts. Built once, yours forever - it keeps running with or without us, with no Anfloy platform in the middle. Your proprietary knowledge never trains a public model and never leaves your perimeter when you don't want it to.
What happens when it breaks or a source is wrong?
Because every answer is cited, a wrong answer is traceable to its source - you click through and see exactly which passage it came from, so you fix the doc, not chase a black box. Retrieval and generation are monitored separately, so we can tell whether the system pulled the wrong context or reasoned poorly over the right one. Continuous evals catch quality drift before users do, and the ingestion pipeline re-indexes as your sources change so stale knowledge gets refreshed automatically.
How long does it take to ship, and does it run on our infra?
A grounded single-source brain can ship in a couple of weeks; a full multi-agent system over many sources takes longer, and we ship it in increments rather than one big launch. It runs entirely on your infrastructure - the vector store on your Supabase or cloud, the agents in your account, your keys - and for sensitive data we self-host the whole pipeline so nothing leaves your perimeter.
Our docs are messy - does that kill the project?
It's the most common starting point, not a blocker, but it's also where most of the real work is. Good retrieval depends on good chunking and clean, current sources, so part of what we build is the ingestion and freshness layer that keeps your knowledge usable. We'll be straight with you about where the data needs cleanup first - because grounding a model on a mess just produces confident, well-cited wrong answers.