The agent experience layer for regulated work
Densery's agents read the file, make the decision, write it into your system of record — and leave the evidence a regulator or auditor will accept. On your hardware, inside your jurisdiction, wherever the data is legally required to stay.
What Densery is
Every regulated business has the same category of job: a document arrives, somebody has to read it, judge it against a rule, act on it, and be able to prove afterwards that they did. Loan files. Claim files. Quality records. Site inspections. The work is repetitive, it is expensive, and being wrong is costly in a way that does not show up immediately.
Densery is the complete experience of putting AI agents onto that work. Not a model to evaluate, not a framework to build on, and not a document reader that hands you a spreadsheet. It is the whole chain — reading, judgement, action and evidence — delivered as a working process on your own systems, with people in the loop exactly where your policy says they must be.
We call it an agent experience rather than a platform because what you buy is not software you have to finish. You buy a document process that arrives working, that your people supervise rather than operate, and that you can defend when someone asks how a decision was reached.
Densery puts governed AI agents to work on your highest-volume regulated documents, and returns the work finished, recorded and provable.
Most tools in this market assist a person: they surface, extract or suggest, and a human still performs the task and still owns the error. Densery completes the task and produces the evidence. That is a different product, a different price and a different conversation with your risk committee.
The four layers
A complete agent experience needs all four. Vendors in this market typically sell one or two and leave you to source the rest — which is exactly where the value leaks away. The right-hand column is what you actually end up with if the chain stops at each layer.
Mixed layouts, several scripts in one file, handwriting, photographs of paper, forty-page PDFs, engineering drawings, imagery from a site. We route across best-in-class extraction engines, including open-weight models that run inside your walls.
An extraction API. Fields in a spreadsheet, generic accuracy, and every downstream step still done by a person. This layer is close to free across the industry and it is not a product.
Counterparty, beneficial owner, exposure, claim, policy, lien, part, revision, site, lease, obligation — defined once, with the relationships and constraints between them. This is where a specialist's judgement gets written down: what makes a file complete, which mismatch is material, which exception someone will ask about later.
Very good structured data, and a report. Accuracy improves because the model now knows what your documents mean — but somebody still has to key the result into the system that matters.
The agent performs the task rather than proposing it. The file is assembled, the check is made, the decision is recorded, the entry is written back through your API into the core, the policy admin, the claims platform or the quality system. Approval gates sit wherever your policy requires a signature, and the agent stops and waits.
Work that gets done and cannot be defended. This is where most pilots die: the process runs, risk and audit cannot see how any individual decision was reached, and it is never allowed into production.
Every action logged with full provenance: which document, which page, which model, which version, which rule, which human. Every field carries a confidence score, anything below threshold routes to a named person with the source page attached, and every correction they make is captured and fed back into your ontology.
A process that completes the work, proves each decision on demand, and gets measurably better on your own documents every month it runs.
Prove it without talking to us
Neither asks for an email. Both run in your browser. If either one tells you we are not a fit, that is a useful answer and it cost you four minutes instead of a quarter.
Your annual volume, your fully loaded cost per file, and what a wrong file costs you — the same three numbers we take in a scoping session. Every assumption is published, including the two we think are weakest. If the return comes back under 3x, the page says so.
Work out your number →Open a real document type and click any extracted value to walk the decision back to source — page, model, confidence, rule, approver, write-back. Two of the fields failed and stopped for a human. Those are the ones worth clicking.
Open a file →We publish our own head-to-head comparisons against Hyperscience, ABBYY, Instabase and building it in-house — each with a section on when you should buy the other one. If you need FedRAMP High today, the first of those says so in the second paragraph and you can stop reading.
Why all four, from one vendor
Buy extraction from one vendor, workflow from another and reporting from a third, and every handover reintroduces a person. A process that is 90% automated but still needs a human to move data between three systems saves a fraction of what the business case promised. The saving lives in the last step, not the first.
Provenance has to survive end to end. If the extractor cannot tell you which page a value came from, or the workflow tool cannot tell you which model version produced it, you have logs from four systems and no defensible account of a single decision. Assembled stacks produce assembled evidence, and that is the version an examiner tests hardest.
When corrections are made in a downstream system, the layer that made the error never learns of it. Accuracy flatlines at whatever generic quality you bought. In a single chain, the correction a reviewer makes on Tuesday changes how the same document is handled on Wednesday — on your estate, not on a public benchmark.
Four vendors produce four explanations when a file is wrong. One chain means one accountable party for the completed work, one security review, one audit trail to certify, and one contract that says what happens when the output is not right.
Why it matters to your company
The obvious one. Manual review is labour, and labour is the majority of what these processes cost — roughly 57% of the US$61 billion a year that financial-crime compliance consumes across the US and Canada, for example. What is less obvious is that the unit cost keeps declining after go-live, because every correction improves the next run.
Backlogs today are solved by hiring, overtime or an outsourcer, all of which take weeks to arrange and are hard to unwind. When the work is completed by agents, capacity is a configuration rather than a recruitment cycle — which matters most exactly when volume spikes and you have the least time to react.
Producing proof that a process was followed correctly is itself a large manual cost, and it is the reason most automation never reaches production. When evidence is generated as a by-product of the work, the approval that normally takes two quarters becomes a document you can hand over.
Across the market, 88% of agent pilots never reach production and 95% of generative-AI pilots show no measurable impact on the P&L. Leaders name three blockers: they cannot evaluate the output (64%), they cannot govern it (57%), and they cannot rely on it (51%). Those are not model problems. They are the second and fourth layers missing — which is why we built the company around them rather than around the model.
Plain about the limits
Category noise makes every vendor sound identical. These are the parts of the stack we deliberately do not charge you for — and you should hold anyone you are evaluating to the same list.
Where it is applied
The four layers are the same everywhere. The second one — the ontology — is not, and that is the part that takes years to build and cannot be bought. These are the four where ours already exists and is running in production.
KYC, AML and sanctions file review, commercial loan files, SBA documentation, post-merger document migration, multilingual estates.
The banking case →FNOL and claim triage, workers-compensation medical records, underwriting evidence, bordereaux reconciliation, TPA operations.
The insurance case →Engineering drawing take-off, bills of material, supplier documentation, quality and traceability records, customs and trade packs.
The manufacturing case →Automated structural inspection from imagery, site and lease documentation, permits and zoning, as-built reconciliation.
The telecom case →Proof, not projection
Densery was not built in a market where cloud-first was the default. It was built for banks, insurers and manufacturers across Asia Pacific whose data was not permitted to leave the building — which is precisely the condition a US regulated buyer is now trying to solve for.
Every deployment described on this site is a customer we can arrange a reference call with. Ask, and we will seek their permission to name them.
The next step
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.
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.
Densery