Cangler Quant · Whole-of-ASX research intelligence
Research intelligence for the whole ASX, built on point-in-time evidence.
Cangler Quant helps analysts, funds, and data teams explore ASX companies, announcements, and signals through point-in-time-controlled data, source lineage, and governed AI research workflows — so research is faster, reproducible, and trustworthy.
Research infrastructure only. Not financial advice. No performance claims.
01 / The problem
ASX research is fragmented, hard to reproduce, and increasingly hard to trust.
Analysts and investors stitch together screens, announcements, spreadsheets, broker notes, and manual judgement. The work is slowed by stale data, ambiguous sources, point-in-time leakage, inconsistent entity mapping, and backtests that can’t be reproduced.
AI summaries make it worse when they aren’t tied back to evidence. Cangler Quant is being built to solve that research-infrastructure problem — coverage of the whole market, with the controls that make an output worth trusting.
02 / The edge
In active developmentStructural edges, drawn from data unique to the ASX.
Beyond standard factors, we’re engineering signals from Australian-market disclosures that are rarely packaged cleanly for point-in-time quantitative research — corporate lifecycle, spatial geology, and the relationships between the people and companies behind each listing.
Cash-runway intelligence
In progressReads quarterly cash-flow filings to estimate how many quarters of funding a company has left — and flags the dilution risk that tends to arrive before a raise.
Geological nearology
In progressA point-in-time map of mining tenements that links explorers to nearby discoveries and the moves of same-basin peers.
Director & promoter networks
In progressEntity-resolved track records across boards and substantial holders — surfacing insider conviction and governance risk.
Narrative-drift detection
In progressDetects when management quietly shifts the metrics they emphasise — a documented tell that the story is moving faster than the business.
Glass-box AI
We don’t make the model the source of truth.
You can’t make a frontier model’s weights transparent — so we solve the black box architecturally, by not making the model the authority. The LLM can draft, route and explain; but contracts, semantic metadata, validation gates, review cycles and an evidence vault decide what is true. The model is wrapped in a governed evidence architecture — a glass-box surround.
The model drafts in plain English — but every claim has to clear the governed evidence surround. The answer comes back grounded and cited, or the system abstains and says exactly what’s missing. The model narrates; the surround decides what’s true.
Model as interface, not authority
The LLM drafts, routes and narrates. Source-owned contracts, tests and the evidence vault decide what is true — it can’t invent facts, run its own queries, or answer from memory.
Grounding & provenance
Every accepted value points back to the exact source page, section or cell. No provenance, no trusted answer — unsupported fields are rejected, quarantined or flagged unresolved.
Response verifier
Before an answer is returned, a verifier checks every factual claim against the retrieved evidence bundle and strips anything a citation can’t support.
Fail-closed abstention
When the evidence can’t prove a claim, the system says so and names what’s missing — never “probably”, “usually”, or “based on the docs”.
Adversarial review
Separate reviewer agents and deterministic gates attack each output for hallucination, look-ahead leakage, overclaiming, privacy and source-rights before it’s trusted.
Golden-question evals
Extraction routes are benchmarked against human-adjudicated “golden” datasets. AI reviewers can flag issues — they can’t promote their own conclusions to ground truth.
Numeric reconciliation
Figures read from filings are cross-footed and checked for sign, period, unit-scale and magnitude, so a misread number can’t flow silently into a feature or signal.
Runtime observability
Every answer logs its input hashes, retrieved evidence, model version, tool calls, validation and reviewer results — an evidence trail you can audit after the fact.
Ask the glass box
An explainer engine over a semantic metadata graph.
Ask in plain English what a value means, how it’s calculated, or why a stock ranked — and get an answer traced to deterministic evidence: the formula and inputs, the source citations you can click, a lineage subgraph, and an explicit “why this can’t be answered” when the graph can’t prove it. Every meaning has exactly one owner in a version-controlled contract; generated prose and dashboards are read-only views, never the authority.
Keeping the quant models honest, too
The black box isn’t only the LLM. Complex ML can stay complex — the decision process around it has to be inspectable and governed.
Interpretable diagnostics
Feature attribution read with care — alongside the model as evidence, never instead of it.
Calibration & uncertainty
Confidence is calibrated and labelled as model confidence — never passed off as a real-world probability.
Drift & decay monitoring
Features and models are watched for drift, with champion–challenger evidence required on every promotion.
PIT-safe reproducibility
Frozen fold boundaries and point-in-time manifests, so a result can be re-derived exactly as first produced.
The glass box isn’t a single model choice — it’s a governed evidence architecture around the model: a semantic metadata graph, source-owned contracts, provenance, validation gates, review cycles and an evidence vault. Described here as designed — much of it is in active development, and the platform claims evidence-grounded, provenance-backed and benchmarked outputs, never “hallucination-free” AI.
The research factory
The Agentic Alpha Lab — hypotheses proposed, tested, and falsified on the record.
A governed research factory — not a prompt-driven strategy generator. Agents turn academic literature and filings into feature candidates, search the approved hyperparameter space, and stress every result through walk-forward validation, negative controls, and leakage probes. Failed trials stay on the record, identities resolve point-in-time, and nothing promotes itself — discovery and promotion are separate gates.
In active development. Console is illustrative.
Cross-sectional momentum under thin liquidity
Order-flow imbalance & short-horizon drift
Post-announcement drift in small caps
advisory evidence only · never implementation authority
No result promotes itself. Discovery and promotion are separate gates — independent review + domain-engine verdicts required.
03 / Evidence-backed
Every output carries its own evidence.
Not a black-box score — a research card. Each one is designed to show its sources, its point-in-time boundary, its data-quality state, the model’s status, and what it doesn’t know — so you can judge whether to trust it in seconds.
- Source lineage on every value
- Point-in-time boundaries, enforced
- Data-quality and model status, shown
- Assumptions and limits, stated
Research card
Evidence trail
- Source
- Appendix 4C · 2026-Q1 · filed 28 Apr
- Point-in-time
- as of 2026-05-15
- Data quality
- verified
- Model
- candidate · not promoted
04 / Lineage & proof
Every insight traces back to its source.
No value stands alone. Each one is designed to trace the whole way back — model, features, joins, the original filing — as an append-only evidence chain, cryptographically signed at every link. So a result can be re-derived and verified, not taken on faith.
Signed with a post-quantum signature profile (FIPS 205 / SLH-DSA). In active development.
- 01Insightsha-256 · signed
the value shown on a research card
- 02Modelsha-256 · signed
candidate · version · calibration
- 03Featuressha-256 · signed
the engineered signals it used
- 04Joinssha-256 · signed
the point-in-time-safe spine
- 05Sourcesha-256 · signed
the original ASX filing · vintage · rights
Designed against
We map our controls to the standards institutions expect.
The platform is designed against — and maps control objectives to — the Australian regulatory, security, AI-governance and risk-data frameworks an institutional reviewer expects, so readiness can be evidenced as we build.
Australian regulatory & prudential
8 frameworksACSC Essential Eight
maturity level 2+
APRA CPS 234
information security
APRA CPS 230
operational risk
APRA CPG 235
data risk management
ASIC Market Integrity Rules
market integrity
ASIC CP 386
plannedautomated-trading controls
AU Voluntary AI Safety Standard
responsible AI
Privacy Act 1988 & APPs
data protection
Security & zero-trust
8 frameworksNIST 800-207
zero-trust architecture
ACSC Modern Defensible Architecture
secure-by-design
ISO/IEC 27001
information security
ISO/IEC 27701
privacy information management
SOC 2 (TSC)
service controls
NIST CSF 2.0
cyber risk
CIS Controls
baseline security
ACSC Secure AI Guidelines
secure AI development
AI governance, risk-data & assurance
4 frameworksISO/IEC 42001
AI management system
NIST AI RMF
AI risk
BCBS 239
risk data aggregation
ISO 9000
quality management
Alignment and control-mapping only — not certification, audit, or regulatory approval. Framework alignment is tracked as readiness mapping and evidenced where controls are in place.
05 / The platform & roadmap
In active developmentCangler Quant is built on Cangler OS.
Cangler OS is a horizontal institutional operating system for governed data analytics and AI. Cangler Quant is the first vertical product built on it — and at its core is UICE, the engine that routes every workflow through policy, compliance, assurance and point-in-time gates.
Cangler Quant
quant-finance vertical
Future verticals
reviewed strategy only
Cangler OS
core engine · UICE
A horizontal institutional operating system — governed data, assurance, research, simulation, evidence and command/control.
built by Cangler Labs
UICE — the Unified Intelligent Coordinating Engine
Not a free-form AI orchestrator — a deterministic control kernel. It’s designed to coordinate the platform’s governance engines so that no output is trusted without the evidence to back it.
Policy engine
Operating policy — assignment, review, access, capacity, promotion and publication.
Compliance engine
Applicable regimes, obligations and controls — with the gaps, blockers and evidence behind them.
Assurance kernel
Classifies the strength and limits of the evidence behind every decision.
Evidence vault
Append-only custody of run snapshots, audit chains and proofs.
UICE
Unified Intelligent Coordinating Engine
The deterministic kernel that routes every workflow through these engines — and holds a blocker when the evidence is missing, stale or insufficient.
Zero-Trust engine
Who — or what — may access each dataset, model, artifact and data-room surface.
Point-in-time engine
Verifies what was knowable on the date, and holds back look-ahead leakage.
Data-quality engine
Freshness, schema drift, nulls, referential integrity and lineage completeness.
Model-risk engine
Eligibility, validation depth, drift, calibration, promotion and rollback.
What it powers
Cangler Nexus
Live visual observability
A live, spatial observability layer that maps the platform's state — portfolio risk, model confidence, point-in-time safety, entity graphs and research campaigns — into scenes you can read in seconds.
Agentic Alpha Lab
Governed research factory
Where alpha hypotheses are proposed, sandboxed and tested against falsification controls. Failed trials stay on the record; promotion is gated by independent review and a full evidence bundle.
ASX Feature Store
Pre-engineered · PIT-by-design
600+ engineered features across the whole ASX — engineered to be survivorship-bias-free and point-in-time-correct, and drawn from sources distinctive to the Australian market. Coverage and validation depth expand as the platform matures.
On the roadmap
Sequenced with design partners and shipped under the same point-in-time and evidence controls as everything else.
API & export layer
PlannedStructured ASX intelligence out — a programmatic API, exports, and integrations into existing research stacks.
Historical-filing intelligence
PlannedSearchable, point-in-time intelligence across historical announcements and filings, with every result traced to source.
Resource & JORC intelligence
PlannedStructured resource and reserve signals from JORC-coded reports — added depth for the ASX resource sector.
Governed end-to-end
From raw ASX data to a scene you can trust.
ASX market data, announcements and alternative data are coordinated by UICE through a governed data-engineering layer — graded by the assurance kernel, checked against compliance controls, point-in-time-bounded and signed with lineage. The Alpha Lab then coordinates the feature store and models built on that governed data — all rendered as a scene you can read in Cangler Nexus.
Illustrative · governed data flow · in active development
Cangler Nexus
Live visual observability of the whole platform.
A spatial observability layer that maps the platform’s live state — entity graphs, model confidence, point-in-time safety, evidence and research campaigns — into scenes you can read in seconds, with every node tracing back to its source.
In active development.
06 / Use cases
Built for ASX-focused research workflows.
- 01ASX-wide opportunity screening
- 02Small- and mid-cap discovery
- 03Announcement & catalyst monitoring
- 04Watchlist & portfolio intelligence
- 05Feature & signal research
- 06Data-quality & source-lineage review
07 / Approach
Evidence over hype — by construction.
Point-in-time by default
Survivorship handling, entity resolution across ticker reuse, and date-correct joins — enforced, not bolted on.
Source-owned & reproducible
Outputs trace to sources with explicit assumptions and limitations, so results can be re-derived and audited.
Governed AI
Models accelerate research within claim controls and review gates — no unsupported certainty, no advice.
Detailed architecture, methodology, and signal logic are shared selectively with design partners under NDA.
Private design-partner beta
Help shape a whole-of-ASX research platform before public launch.
We’re partnering with a small number of ASX-focused analysts, funds and data teams. As a design partner you get early access, a direct hand in shaping the evidence cards and data products we build, founding-partner terms, and a direct line to the team — in exchange for candid feedback on what’s actually useful.
08 / About
Built by Cangler Labs.
Cangler Labs builds Cangler OS — a horizontal operating layer for governed, evidence-backed research. Cangler Quant is its first vertical: quantitative research intelligence for the Australian market.
The work is engineering-led and evidence-first: point-in-time discipline, source lineage, and an honest line between what’s live today and what we’re building next.