Intencion vs Northbeams

Intencion and Northbeams are both inference engines & infra tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Intencion

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Northbeams

Northbeams sits between your workforce and their AI tools, classifying what's running, blocking what shouldn't be, and generating the evidence chain your SOC 2 or HIPAA auditor will ask for. The browser-based agent installs without network changes, so IT doesn't need a procurement cycle to get visibility. Discovery is ungated, which means you can map your shadow AI footprint before committing to enforcement. The ceiling appears when your environment scales past a single site or when you need MCP agent governance — those capabilities are paid-only features. Teams running large multi-site deployments report that per-seat policy management becomes the operational bottleneck.

Attribute	Intencion	Northbeams
Pricing	Paid	Paid
Price	Free to $399/month	$9,600/yr + $12/user/mo (Sentinel)
Free trial	No	14 days
Open source	No	No
Has API	Yes	Yes
Self-hosted option	Yes	No
Platforms	Web-based SaaS; SDKs for Python and Node.js/TypeScript	Browser (Chrome, Edge, Brave, Arc), Mac, Windows, CLI
Pros	Session-level intent tracking across multi-turn conversations, so you can see not just that a user dropped off but what they were trying to do at the moment they left — without which most teams are guessing at failure causes from aggregate drop-off rates alone. No seat licensing model, which means the full product, data science, and engineering team can access conversation analytics without the tool becoming a bottleneck every time a new stakeholder needs visibility. Self-hosted deployment option, so teams in regulated industries or with strict data residency requirements can run observability on their own infrastructure instead of routing sensitive conversation data through a third-party cloud. API access, which means session and intent data can be pulled into existing data warehouses or BI tooling rather than requiring the team to context-switch into a separate analytics interface. Free tier covering 10,000 sessions per month, so a team running a pilot-scale production agent can validate whether the observability layer delivers signal before committing budget.	Agent deploys without network changes or procurement approval, so a security team can have full shadow AI inventory running in hours rather than after a six-week firewall project. Real-time PII, credential, and source-code interception fires before data leaves the browser, which means you catch the leak before it becomes a breach notification obligation. Automated generation of SOC 2, HIPAA, and EU AI Act audit evidence means compliance reviews don't require a two-week manual log reconstruction before every auditor call. Per-tool allow/block/sandbox policies for MCP agent access, so engineering teams using Cursor or Claude Desktop don't operate in a governance blind spot while the rest of the org is covered. Discovery tier is ungated, which means you can produce a complete AI tool inventory and make the business case for enforcement before spending a dollar — removing the 'prove it first' blocker most security budgets impose.
Cons	The product is built exclusively for monitoring conversational agents — teams that need observability across non-conversational pipelines (batch inference, document processing, structured output chains) will find no coverage here and will need a separate tool, at which point maintaining two observability layers becomes the new problem. Because this is a passive analytics layer rather than a testing or evaluation framework, it cannot catch failure modes before they reach real users — teams that need pre-production red-teaming or automated regression testing will hit that wall immediately and typically look at dedicated eval platforms instead. At the scale where session volume justifies the platform, the absence of disclosed SLA details and integration depth documentation (not surfaced in available source material) creates procurement risk for enterprise teams that need contractual uptime guarantees before sign-off.	Browser-agent coverage means any AI workload running outside the browser — CLI tools, server-side agents, API integrations — is invisible to Northbeams; teams with significant non-browser AI usage will maintain a separate inventory for those surfaces and live with two parallel governance systems. MCP agent governance and Fleet (multi-site policy management) are paid-only features, so organizations that deploy on the free tier and then discover their primary risk is in coding agents or distributed sites face a forced upgrade decision mid-rollout rather than before it. Teams that outgrow per-tool policy management at scale — typically when seat counts push into the hundreds across multiple locations — report that policy administration becomes a recurring manual burden; at that inflection point, organizations with dedicated security engineering staff typically migrate to a network-layer DLP or CASB that handles enforcement at the infrastructure level rather than the browser.

Bottom line

Intencion and Northbeams are closely matched on pricing model, openness, and API availability — pick by feature set and platform support in the table above.

Comparison data is sourced and verified by the AIDiveForge data pipeline. AIDiveForge is editorially independent.