What is Knowably
Learn how Knowably connects to your existing knowledge sources and turns scattered company information into instant, reliable, AI-powered answers with source citations.
Your team's knowledge is scattered across eight different systems. SharePoint. Confluence. Jira. Email inboxes. Google Drive. GitHub repos. And a handful of PDFs that someone uploaded to an S3 bucket two years ago.
Everyone knows the information exists. Nobody can find it when they need it.
I built Knowably to fix that. Connect your data sources once, and your team gets instant, reliable answers from AI agents that actually cite their sources.
Get Started | See How It Works
Why I Built This
I spent 20+ years building software. The last few years I went deep on AI and RAG - first for a Dutch government project, then for clients who needed their scattered documentation to actually be useful.
The pattern was always the same. The knowledge exists. It lives in SharePoint folders, Confluence pages, Jira tickets, email threads, shared drives. The problem is never a lack of documentation. It is a retrieval problem.
And traditional search does not solve it. Keyword search misses documents that use different terminology. Search for "remote work policy" and you get nothing because the document is titled "flexible working arrangement guidelines." Even when search works, it gives you links - not answers. You still have to open the file, read it, figure out which paragraph applies.
The cost is real. Teams duplicate work because they cannot find what already exists. New hires spend weeks hunting for basic information. Support teams answer the same questions over and over.
I wanted to build one platform where you ask a question and get an answer. Sourced from your actual documents. Across all your systems at once.
What Knowably Does
Knowably is an AI-powered knowledge platform. Connect your existing data sources, and your organization finds answers through conversational AI agents.
One question, all your sources. Not a SharePoint search, then a Confluence search, then a Jira search. One question across SharePoint, Confluence, Jira, Outlook, GitHub, S3, Google Drive, websites, and file uploads. Your documents stay where they are. Knowably indexes them and keeps them in sync.
Answers grounded in your documents. The agent retrieves relevant passages and generates a response with inline source citations. Click through to the original document. If the information is not in your docs, the agent says so. It does not invent an answer to sound helpful.
Specialized agents for different teams. An HR agent only sees policy documents. A technical support agent only searches product docs. Each agent has its own knowledge scope, personality, behavior rules, and search strategy. Same platform, completely separate configurations.
You control the pipeline. Search weights, reranking thresholds, chunk filtering, query expansion, generation parameters - all configurable per agent. When something is not working, you can find out why and fix it.
Pick your AI provider. OpenAI, Anthropic, Ollama, Groq, Mistral - or switch between them. Your data and configuration are independent of the model provider.
Embed anywhere. Deploy agents as chat widgets on your website. One script tag. Session-based authentication. No user accounts required. Setup guide.
Your data stays isolated. Every organization gets its own dedicated PostgreSQL database. Not row-level filtering. Full isolation details.
Built for Trust and Security
AI that answers from company documents needs to be trustworthy. I learned this on the Dutch government project - they had non-negotiable requirements around data handling. Those requirements made the whole platform better.
PII detection and input security. Queries are scanned for personally identifiable information, prompt injection patterns, and malicious input before they reach the language model. Built for a government project where citizen data could never touch the LLM.
Configurable guardrails per agent. Prompt injection sensitivity, PII detection, chunk sanitization, origin validation for embeds - all independently toggleable based on your use case.
How It Works
Connect your data sources. Create a knowledge base. Configure an agent. Ask a question. The full walkthrough covers every step from first connection to monitoring answer quality in production.
See how it works | Deep dive into the RAG pipeline
Use Cases
Internal Knowledge Assistant
Help employees find answers across internal documentation, policies, and procedures. Reduce the repeated questions that flood HR, IT, and support teams. New hires get a single place to ask anything instead of guessing which Slack channel to post in.
Customer Support Widget
Embed an AI assistant on your website or help center. Customers get instant answers from your documentation without waiting for a human agent. Deflect common questions while your support team focuses on the complex ones.
Compliance and Regulated Industries
When answers need to be traceable, this matters. Every response is grounded in approved documentation with source citations that serve as an audit trail. No hallucinated answers. No unverifiable claims. Documented facts with clear provenance.
I built the PII detection and audit capabilities for a government project. Those same capabilities are available to every Knowably workspace.
Multi-Source Research
One question searches SharePoint, Confluence, Jira, and email at once. You stop switching tabs. You stop wondering which system has the answer. You just ask.
How Knowably Compares
Other RAG platforms exist. Most are cloud-only, treat the pipeline as a black box, and share a database between tenants.
I made different choices. An open, configurable pipeline where every step is tunable per agent. Database-level tenant isolation instead of row-level filtering. Self-hosted deployment as a first-class option with Ollama. Full observability on every query through Langfuse. Source code you can audit.
If you want a managed enterprise deployment backed by a large vendor, those platforms serve that need well. If you want to understand and control exactly how your AI answers questions - and run it on your own infrastructure when you need to - that is what I built.
Get Started
Connect your data sources. Create your first agent. Start asking questions.