Comparison

Densery vs Hyperscience

Hyperscience is the incumbent in on-premise and air-gapped document processing, with FedRAMP High authorisation and a government and regulated-enterprise reference base we cannot match. If you are comparing us on deployment model you will find very little daylight. The difference is what arrives at the end of the process.

Work out your own number See the evidence layer

Hyperscience

On-premise intelligent document processing

COMPARED ON

Densery

Agents that complete the work

	Hyperscience	Densery
On-premise / air-gapped	Yes, and with FedRAMP High authorisation. Deployed across US federal and state agencies.	Yes, on a GPU appliance we supply and install. Live inside a supervised commercial bank. No FedRAMP authorisation.
What the output is	Structured data, at high accuracy, delivered into your downstream workflow. A person or another system acts on it.	The completed task. The file is assembled, the rule is applied, the decision is recorded and the entry is written back into the core, claims or quality system.
Evidence and audit	Processing logs, human-in-the-loop review and version control. Strong, and built for regulated review.	Per-decision provenance: page, model, version, rule, threshold, approver and override, exportable as a signed record. It is the product, not a log.
The extraction engine	ORCA, their own vision-language model, developed in-house and central to the pitch.	Model-agnostic. We route across best-in-class engines including open-weight models running inside your walls, and we do not charge a premium for reading a page.
Vertical depth	Broad. Nine industries and seven use cases, with real depth in public sector and financial services.	Four. Banking, insurance, manufacturing and telecom — where the ontology already exists and runs in production. Outside those four we would be starting from scratch and would say so.
Scale and support	Roughly 300 people, around US$300M raised, an established support organisation and analyst-leader placements.	Materially smaller. Six named deployments, none of them yet in the United States. Your account team would be senior people, because they are the only people.
Pricing unit	Per page and volume-tiered, in the conventional IDP model.	Per completed file. Page pricing is deflating by roughly half a year and we do not want your budget tied to a unit that is disappearing.

Statements about Hyperscience are drawn from their public website and published materials as at 15 August 2026 and are our reading of them, not their words. Product capability changes; check anything here that matters to your decision, and tell us if we have it wrong.

When you should buy Hyperscience.

Written straight, because you will find this out anyway and it costs us less to say it now.

You need FedRAMP High, StateRAMP or an equivalent authorisation on day one. We do not have it, and no amount of architecture argument substitutes.
You are a US federal or state agency with a procurement vehicle they are already on.
Your requirement really is extraction at very high accuracy and volume, and your downstream process is already automated.
You need a vendor with a support organisation of a few hundred people and a decade of enterprise references behind it.
Your document estate spans industries outside our four ontologies.

When Densery is the better answer.

Narrower than the list on the left, deliberately.

The bottleneck is not reading the document — it is everything after. Your team already gets clean data and still keys it into three systems.
A previous pilot produced good accuracy and never reached production because risk or audit could not see how a decision was reached.
You are under an effectiveness standard where you must show what was reviewed, on what basis, and what happened when it was wrong.
Your estate is multilingual or mixed-script and generic tooling degrades silently on it.
You want the vendor accountable for the completed work rather than for the quality of an extraction.

The honest verdict

If your constraint is an authorisation, buy Hyperscience. That is not a close call and we would rather you heard it from us than discovered it in week six of a procurement.

If your constraint is that work keeps arriving as data rather than as finished, defensible files, the comparison runs the other way — and it runs that way regardless of how good the extraction underneath is, because extraction is not where the cost lives. Roughly 57% of financial-crime compliance spend is labour, and most of that labour happens after the page has been read.

The most useful thing you can do with both of us in a room: bring twenty files and ask each vendor to show you the audit record for a decision on file eleven. The answers will not be similar.

Two ways to test this without talking to us

Both are ungated and neither asks for an email. Run your own volume through the calculator, then open a real file in the audit-trail explorer and click the fields that failed.

Value calculator Audit-trail explorer

The next step

Ninety minutes, your documents, three numbers.

A scoping session is not a demo. Bring twenty real files, redacted if you need to. We take three numbers off you — annual volume, fully loaded cost per file today, and what happens when the output is wrong — and hand back a one-page value case in your own KPIs.

If the arithmetic says we are not a fit, we will tell you in the room rather than six weeks later.

Book a scoping session Work out the number first

QUALIFY YOURSELF OUT

We are a fit if all three are true

More than 250,000 pages a year, or 25,000 claims or files
A legal obligation — regulator, board risk committee or parent-company policy — to keep the data in-house
An AI or agent pilot that did not reach production

If your data can go anywhere and your documents are already clean and digital, you do not need us. Use a hyperscaler document API and spend the money on something harder.