When is Freed the better choice?

Pricing is fully published across tiers with a trial that takes no credit card, and a business associate agreement is included by default on every tier rather than gated. The security disclosure is unusually specific for a self serve product, naming independent audits, federal cryptographic standards and United States data residency. It pushes the note into any browser based record system with one click, so the documentation reaches the chart without a native integration project.

When is Knowtex the better choice?

The founders spent a year working as medical scribes alongside physicians while building it, which is a different kind of domain grounding from an advisory board. Specialty behaviour is built per specialty rather than as templates over one model, and it generates codes and orders in real time from the encounter. A federal award behind it means the product went through procurement scrutiny rather than only customer selection.

Freed vs Knowtex

Last VerifiedAugust 4, 2026

Verdict

Two self serve scribes with different centres of gravity. Freed is the more complete commercial product: published tiers, a trial with no credit card, a business associate agreement on every tier and the most specific security disclosure in the self serve tier, with the note pushed into any browser based record system. Knowtex is the more technically ambitious, generating codes and orders in real time from the encounter with specialty behaviour built per specialty rather than as templates, and its founders spent a year working as scribes while building it. The gap that should decide it is oversight. Knowtex produces billing codes and orders with no described gate along that path and publishes nothing on retention or training use, while Freed publishes both and stops at the note. Buy on what you are willing to have generated unsupervised.

Select Freed if

Pricing is fully published across tiers with a trial that takes no credit card, and a business associate agreement is included by default on every tier rather than gated.
The security disclosure is unusually specific for a self serve product, naming independent audits, federal cryptographic standards and United States data residency.
It pushes the note into any browser based record system with one click, so the documentation reaches the chart without a native integration project.

Select Knowtex if

The founders spent a year working as medical scribes alongside physicians while building it, which is a different kind of domain grounding from an advisory board.
Specialty behaviour is built per specialty rather than as templates over one model, and it generates codes and orders in real time from the encounter.
A federal award behind it means the product went through procurement scrutiny rather than only customer selection.

Attribute Matrix

Side by Side

Axis	F Freed	K Knowtex
AI Centrality
Autonomy and Oversight Model
Model and Technology Transparency
Clinical and Operational Evidence
AI Safety and PHI Stewardship
HIPAA and BAA Posture
Security Certifications and Trust Center
FDA and Regulatory Status
AI Governance and Bias Disclosure
EHR and Interoperability Depth
Deployment Model and Data Residency
Commercial Transparency
Setting and Specialty Coverage

Keep Comparing

Related comparisons

Other published head to head assessments involving these vendors or their closest peers. The full set for this category is on the Ambient Scribes page.

Abridge vs Freed→Ambience Healthcare vs Freed→Commure vs Freed→DeepScribe vs Freed→Doximity Scribe vs Freed→Elation Health vs Freed→Freed vs Heidi Health→Freed vs Medscape Scribe→Freed vs Microsoft Dragon Copilot→Freed vs Nabla→Freed vs OrbDoc→Freed vs Suki→

Disclosure

Knowtex generates diagnostic and billing codes and orders in real time with no described gate along that path, and publishes no retention, de identification or training use statement despite a federal deployment, no named attestation and no trust centre. Freed states that its models are trained only on de identified notes, which means customer content does train the model after de identification, a different commitment from not training on customer data. Neither publishes an accuracy figure, evaluation methodology or subgroup performance analysis.