Knowledge for AI agents
Your AI is only as good
as the source it reads from.
Customer support bots, internal copilots, agent platforms — they all need a verified knowledge layer to ground answers in. KnowledgeScout is the substrate your AI was missing.
Building AI agents on chaos doesn't end well.
Your customers don't care that you wired together a vector store last sprint. They care that the bot gave them a wrong answer about your refund policy and they made a decision based on it.
The bottleneck isn't the AI. It's what's underneath.
Who this is for
Four shapes of AI buyer keep landing on this page. Here's how KnowledgeScout fits each.
AI product teams
The pain
You're shipping an AI feature and the answer quality depends entirely on getting the source right. The bot can't go live until the knowledge layer is solid.
Where KnowledgeScout fits
Curate articles in a workflow your team already understands — review dates, drafts, version history. Your agent calls in via MCP or REST. You skip the months you'd spend building the substrate yourself.
Enterprise teams on agent platforms
The pain
Foundry IQ, Copilot Studio, Salesforce Agentforce — your platform needs a knowledge layer to call into. Most options are either generic doc stores with no governance, or vector DBs with no UI.
Where KnowledgeScout fits
MCP-native, REST-ready, configurable per tenant. KnowledgeScout is the structured source your platform reads from — not also a project tracker, not also a wiki. One job.
Customer support teams deploying AI
The pain
You're plugging Fin, Intercom, Agentforce, or your own bot in front of customers. The bot is only as good as what it reads. Stale Confluence pages and scattered shared drives become its voice.
Where KnowledgeScout fits
Read-only MCP endpoint scoped to articles you've actually approved as public. Citations on every answer. Internal content never leaks, regardless of how the bot is prompted.
Regulated industries with AI ambitions
The pain
Your compliance team will block the AI rollout until they can answer "what was our stated position on this date?" with something stronger than "whatever the AI synthesised." That answer is hard to manufacture after the fact.
Where KnowledgeScout fits
Per-agent audit trail. Human review on every write. Version history for human and agent contributions. Regional data hosting. The compliance answer is built in, not retrofitted.
How agents connect
Your agents call in via the Model Context Protocol (MCP) or a REST API. Customer-facing bots get read-only access scoped to public articles. Internal agents can also propose drafts, with every write going through human review.
That's the buyer-level summary. For the eighteen MCP tools, permission scopes, OAuth vs API key trade-offs, and the rest of the technical surface — head to the AI agent integration page.
See the AI agent integration pageWhy a substrate beats building one
Buyers building AI products often consider rolling their own knowledge layer. Here's what KnowledgeScout gets you that's hard to replicate in-house.
Time-to-first-bot is faster
You don't build the substrate before you can ship. KnowledgeScout already does articles, taxonomy, review workflows, permissions, audit trail, and MCP/REST retrieval. Curate content, point your agent at it, ship.
Compliance-friendly by default
Audit trail, human review on every write, regional data hosting, version history for both human and agent contributions. Built for the compliance conversation, not retrofitted to survive it.
Vendor-neutral
Bring your own Anthropic, OpenAI, Azure, or self-hosted model on Business and Enterprise. Connect any MCP-compatible agent. We're the substrate underneath — we don't lock you to a model or an agent vendor.
One substrate, every bot
Customer bot, internal copilot, support agent, ops automation — they all read from the same curated knowledge base. One curation effort feeds every AI surface you ship.
Common questions
Will my compliance team approve this?
That's the page we built it for. Per-tenant encrypted databases, regional data hosting (Sydney, London, Dallas), full audit trail of every agent action, human review on every write, version history for human and agent contributions alike. Designed to fit Australian Privacy Principles, UK GDPR, and US data residency requirements. The audit trail is the answer to "what did the AI say on this date?" — not a thing you have to bolt on afterwards.
We're building our own AI product. Can KnowledgeScout integrate without forcing our customers into your UI?
Yes. KnowledgeScout exposes content via the Model Context Protocol (MCP) and a REST API — both are headless. Your customers interact with your product, your agent, your branding. KnowledgeScout sits underneath as the substrate your agent calls into. They don't need to know it's there.
What does this replace in our stack?
The curation and governance layer most teams skip. KnowledgeScout isn't a vector database (those are storage; this is the full content lifecycle). It isn't an agent (Claude, OpenAI, Foundry IQ already do that better than we will). It's the substrate they call into — content authoring, structured taxonomy, version history, review schedules, permissions, audit trail, plus retrieval through MCP and REST. The thing you'd otherwise build yourself before you could ship.
Are we locked into a particular AI provider?
No. On Business and Enterprise, bring your own Anthropic, OpenAI, Azure OpenAI, or any OpenAI-compatible API key (including self-hosted). Pay your provider directly. We're MCP-native so any MCP-compatible agent calls in — Claude, OpenAI agents, Foundry IQ, Copilot Studio, custom builds. The substrate doesn't pick a model for you.
Give your AI a foundation worth standing on.
MCP-native, REST-ready, audit-trailed by default. Available on Business and Enterprise.