Stop reading docs. Start making decisions

Turn any document into structured entity data, reconciled against the registry, in minutes. No manual entry. No missed risk.

Sound familiar?

01
The registry goes dark the moment you cross a border

Jersey closed its API. Luxembourg dropped its UBO register. Cayman, Angola, BVI, opaque by design. Available data runs out, and the case goes back to manual.

02
The document arrived last Tuesday. It's still unread

The trust deed sat in a shared inbox. The customer questionnaire response is buried in a thread. The case is open because nobody's had time to read a PDF.

03
Your analysts are re-keying percentages from PDFs

Directors, shareholdings, registered addresses. Transcribed by hand, one line at a time. Mistakes creep in. And the real work, the judgment, hasn't started yet.

04
Email, Salesforce, compliance, no version of the truth

The document lives in four places. Nobody has both sides open at once, so nobody actually compares. The field says whatever the last person typed.

05
You're constantly re-finding what the registry should have

Ownership moves. Directors change. Registries update when they update. Your team keeps going back out to confirm what's current, then entering it by hand, because the filing hasn't caught up yet.

06
A thousand entities to re-review. The plan is spreadsheets

Remediation means working through five, seven, ten thousand entities, the same compare-and-update exercise, one at a time. Today it lives in spreadsheets thrown between systems, with no way to scale beyond more people and a longer week.

The difference Strise makes

70%
less time on complex entity entries.
Minutes
from document drop to reconciled entity. Down from a half-day.
100%
of enriched fields cite a source and a timestamp. Regulator-ready by default.

What customers say

Smiling bald man in dark sweater sitting at a table with a smiling woman in a beige top and brown skirt standing behind him by a window.
"Strise has revolutionised our approach to KYC."
Gavin Bergin
Director of Governance at British Land
Smiling woman wearing a dark long-sleeve shirt and a small microphone clipped to her neckline, standing indoors with a blurred plant and cushion in the background.
"With Strise we use manual labor where it's most worthwhile, and decrease costs."
Silke Oeverby
Chief Risk & Compliance Officer at Vipps MobilePay
Man with light brown hair wearing a white shirt, smiling in front of a red curtain background.
"With Strise, we have reduced false positives by 30%."
Endre Jo Reite
Director of personal markets
Smiling woman with long blonde hair wearing a black top against a wooden background.
"Strise has helped us move away from periodic review. That's fundamental."
Rebecca Robinson
Chief Risk and Compliance Officer at Tenora
Man with short dark hair and beard wearing a white collared shirt and blue sweater, speaking indoors with blurred background.
"With Strise, we get better accuracy and quality in our customer risk assessments."
Niri Kvammen Forberg
AML Specialist at SpareBank 1 SMN
Smiling woman with long brown hair looking slightly upward against a dark background.
"Strise saves us a considerable amount of time per onboarding"
Ragnhild Georgsen
Head of AML & Sanctions at Sparebanken Norge
Man in white shirt wearing a lanyard with 'Plenitude' logo, speaking indoors with blurred windows in the background.
"Strise helps our clients realise the benefits of AI quickly."
Alan Paterson
Founder and Chief Innovation Officer at Plenitude

From document to decision, automatically

1

Drop the document in

PDF, CSV, JSON, text. Straight onto the entity.

2

AI extracts. Fields mapped

Names, roles, percentages, addresses. Parsed to the same fields Strise uses everywhere.

3

Compared with registry

Side-by-side. Real differences flagged, formatting noise filtered, hidden risk exposed.

4

Analyst decides

Accept, override, or edit. Every change lands with source and timestamp attached.

How Document analysis runs itself

Closes the registry blind spots

Jersey, Cayman, Luxembourg, Guernsey, BVI, Angola. Where public registries fall short, a customer document closes the gap on day one. No waiting on a new data-provider integration.

Only real differences surfaced

New shareholder. Changed address. Missing UBO. Sub-threshold aggregation that adds up to control.

AI proposes. You decide

Nothing updates without a human decision. Full control, aligned with AMLA and EU AI Act expectations.

Audit-ready by default

Every change stamped with source, timestamp, and the person who approved it. When a regulator asks how you reached a decision, you open one file, not five.

Fewer tabs. More documents read
See what your team's day looks like when every document lands as reviewed, reconciled data on the entity.
Book a meeting

Things we get asked. Answered

What document types does Strise actually read?

PDF, CSV, JSON, and plain text — what your clients actually send. Trust deeds, articles of association, incorporation certificates, registry extracts, questionnaire responses, structure charts, annual reports, ownership registers. If it's computer-readable, Strise parses it.

How does Strise handle documents in other languages?

Multilingual by default. The extraction model reads the document in its native language and maps every field to the working language in your Strise instance. A Luxembourg trust deed in French, an Estonian registry extract, a German questionnaire response, the entity data lands in the same structured fields either way.

How do you stop the AI from hallucinating?

Every extracted value is grounded in the source: the field, the page, the exact text it came from. Low-confidence extractions are surfaced for review, not auto-applied. And nothing, high-confidence or low, is written to the entity without an analyst's click. Accuracy isn't trusted; it's verified, row by row.

What about privacy?

Documents stay isolated to your Strise instance. Nothing is shared across customers. Nothing trains a shared model. Your enriched data sharpens your own intelligence layer, not anyone else's. GDPR-compliant by default, with DPAs and data-processing documentation available at any point during evaluation.

How does this hold up under the EU AI Act?

Every AI step is labelled and logged. Extraction is proposed, not committed, the analyst accepts, overrides, or edits before anything touches the entity. Every automated decision maps to a human review. When an examiner asks what the model did and what the human decided, the audit trail answers both.

What happens when the registry later publishes something that contradicts a document-sourced edit?

It triggers a monitoring alert. Nothing silently overwrites an analyst decision. The registry update and the original document-sourced edit sit side-by-side in the same compare view your team used to create the record. You reconcile the conflict once, and the audit trail keeps both versions.

Running a formal evaluation?
Send us your RFP. We'll come back with real numbers for your setup.
sales@strise.ai