AI document engine
Document in. Structured decision out. Every time.
Complex documents state what a system must do, what happened, or what a customer thinks, in free text, in formats nobody controls, at volumes nobody can read by hand. Quantscope builds the AI engine that reads them, extracts what matters, and makes the output traceable and testable every time.
Representative interface, synthetic records. Not a screenshot.
Deployment
Your environment
Quantscope holds no client data
Operating model
Your rules
Configured to your workflow
Handover
Full package
Documentation your team runs from
Audit
Invited
Scheduled before signature
Engagement model
Fixed-fee
Against written acceptance criteria
Production proof
The pipeline already runs in production
Two production systems. One analysis engine. One search engine. The same stack applied to complex governed documents is the enterprise offering.
01
A customer review analysis product
Sentence-level entity-based sentiment analysis across twenty-plus source platforms. Multilingual English and Dutch with distinct models per language. Document clustering with LLM-generated topic names per cluster. MCP-native tooling usable from Claude Desktop and Cursor.
02
A natural language property search product
Natural language intent parsed into ranked matches against live listings. Multi-source ingestion, normalisation and deduplication. The engine weighs commute, space, budget and constraints simultaneously rather than matching keywords. Every result carries the reason it ranked where it did. Operated by Quantscope Inc.
over 150,000 listings normalised, more than 100,000 currently active across 458 Ontario municipalities, several thousand records updated every weekday. Figures as of 19 September 2026.
03
The infrastructure underneath both
Multi-source ingestion normalised to typed outputs, with schema validation at entry and a full audit trail on every record. Runs inside your cloud account under your access controls, with no credential or key held by Quantscope.
Neither product is a regulated system and neither is sold as one. What carries over to an enterprise engagement is the stack underneath: the ingestion, normalisation, checking and retrieval path, already running against two unrelated data domains in two countries.
The pipeline
Four modules. One typed output per source.
Any document a system needs to act on has to be read, interpreted, and linked to other documents. Each source is mapped to a typed output in code.
Conduit
Any source the pipeline is pointed at, each one mapped to a consistent typed output in code. One integration profile is live today; adding a new source means writing a new field map.
Signal
The output from Read, checked against rule sets written as code.
Trace
Outputs from Read, searched and compared across sources. Finds what matches a query, surfaces where two sources describe the same thing differently, and ranks results with reasons.
Gauge
System outputs compared against a reference set to track consistency and drift over time.
Data quality at the source
Incoming data checked the moment it arrives. Issues found before anything downstream takes a dependency on them, while the person who can fix them is still in the workflow.
Governing document to specification
A governing document read once, producing a specification and the tests that prove it. Every line traces back to the clause that required it, and holds across every revision.
Human in the loop
Three outcomes, and only one of them clears on its own
An input that satisfies its rule set clears without a person. The result is written to the decision log with the version of the rule that produced it.
Anything the rule set cannot settle enters the review queue with its evidence attached, naming which check fired, on which input, against which version of which rule.
A flagged input does not progress, downstream systems do not receive it, and the queue does not clear itself on a timer. A person with authority accepts it or stops it, and the decision is recorded.
In a high-stakes workflow the halt is the property that makes the system trustworthy rather than a limit on what it can do. A system that moved records on its own would be making a judgement it has no authority to make.
How we work
What belongs to you, what belongs to us
Quantscope provides the engine. The client owns the data and the people with authority own the decisions. Each of the four below is written into the agreement.
01
You own the data
The preferred deployment runs inside your environment, against your storage, under your identity provider. Quantscope holds no client data and initiates no inbound connection.
02
You own the decisions
The engine extracts, checks and scores. It does not interpret, adjudicate or decide. A person with authority makes the call, and the log of who decided and why is a deliverable of the engagement.
03
You own what you paid for
Your configurations, your integrations and any model trained on your data are yours, with a licence to run the modules. Quantscope keeps the underlying platform and tooling.
04
Nothing trains on your data
The agreement says so in terms. No client data trains, tunes or evaluates anything outside that client engagement.
What is held, and what is not
A first-pass security review should not need a phone call. And a vendor that hides a gap during qualification has already told you how it will behave during the engagement.
- SOC 2: not held
- ISO 27001: not held
- Technical documentation: on request
- Security questionnaire: on request
- Client audit: invited
No badge appears on this site without an audit behind it. The full picture is on the Trust page, including the gaps.
Next step
Start with the data
Send the data type, the volume, and what currently has to be processed by hand. If the modules do not apply, that is the answer you get, and it costs one exchange to find out.
The four things worth knowing before a first call. Opening this fills them into a draft in your own mail client.
- Organisation and your role
- Data type: tabular, documents, free text, or another format
- Monthly volume: under 1k, 1k to 10k, or 10k and above
- The biggest thing currently processed by hand
Request a technical session· goes to info@quantscope.ai · response within two business days