Combine Domain Knowledge with Technology,And Technology with Expert Judgement
What sets TradeComplize apart is not a single feature; it is domain focus, explainability, architecture and human control designed together.
Not a Single Feature,
Five Choices Designed Together
What sets TradeComplize apart is not a single feature; it is domain focus, explainability, architecture and human control designed together.
Focused on the LC Domain
Designed around the LC workflow and its terminology, rather than a general-purpose chat experience.
Explainable Output
Aims to make visible which document, field and assessment context each finding arises from.
Model-agnostic Approach
Follows a hybrid technical approach where different models and methods can be assessed together as needed.
Expert Control
A way of working where the final decision stays with a person, and feedback adds value to the system and the process.
Institutional Options
Deployment, data-residency and integration options are assessed against the institution's requirements.
What the Technical Structure Means for the Institution
Each architectural choice maps to something concrete on the bank's or the company's side.
Systematic evaluation
Accuracy is not merely asserted; it is tracked with a defined data set and metrics.
Model-agnostic architecture
A replaceable technology structure not tied to a single model or provider.
Hybrid solution
Traditional machine learning and generative AI are used task by task in the same system.
On-premise and offline operation
Core components can run on the institution's isolated infrastructure without internet access.
Configurable data retention
Retention periods, or not retaining data after processing, are adapted to institutional requirements.
Ground-truth reference data set
Accuracy, speed and cost are measured regularly against the company's own reference set.
Internal evaluation infrastructure
Progress on false positives, missed findings, precision and recall is tracked per release.
Solution family approach
Separate solutions plus a layer that combines them into end-to-end LC decision support.
An Architecture Focused on the Right Result,
Not on a Single Model
LC documents involve many rules, exceptions, document types and contextual relationships. Using a general-purpose OCR or LLM alone cannot deliver the consistency a production environment requires. TradeComplize is designed as a model-agnostic system that brings different AI and document-processing components together around domain expertise and rule assessment.
Domain-expert Centred
The technical system is developed around real cases, rules and the decision logic of LC experts.
Model-agnostic
The system does not depend on a single model; the components best suited to each task can be used together and replaced over time.
Hybrid
Traditional machine learning and generative AI work in complementary roles across tasks; deterministic checks operate where appropriate.
Measurable
Accuracy, speed and cost are monitored regularly with a reference data set and defined evaluation metrics. Expert feedback is part of the quality loop.
Technical Principles Behind the Architecture
- Systematic evaluation: accuracy is not merely asserted; it is tracked with a defined data set and metrics.
- Model independence: a structure not tied to a single model or provider, with replaceable components.
- Hybrid task allocation: the most suitable method per task — rules, classical machine learning or a generative model.
- In-house operation: components are designed to run in the institution's isolated server environment.
- Configurable data retention: retention periods are addressed according to institutional policy.
- Internal evaluation infrastructure: false positives, false negatives, precision and recall are tracked per release.
From Raw Components to an Explainable Finding
- 01
Raw components
Document processing, OCR and language models are the input layer of the system, not the result on their own.
- 02
Domain expertise
Real cases from LC examiners, the reading of UCP 600 and ISBP 821, and institutional practice sit at the centre of the architecture.
- 03
Hybrid architecture
Traditional machine learning, generative AI and deterministic checks work together, task by task.
- 04
Systematic evaluation
Outputs are measured regularly against a ground-truth data set with task-specific metrics.
- 05
Explainable finding
Each discrepancy is presented with its document, field and rule reference; the final assessment stays with the expert.
Two Ways of Looking
At the Same Documents
General tool
TradeComplize approach
Extracts text or fields.
Classifies the document in LC context and evaluates it with its related fields.
Draws on a single model's general knowledge.
Uses multiple components together with domain rules and expert feedback.
Production fitness must be tested separately.
Outputs are measured regularly with a reference data set and task-specific metrics.
Does not naturally contain institution-specific decision logic.
Targets an architecture adaptable to institutional requirements and rule structures.
OCR and language models are not the product; they are components of the system.
OCR and large language models are components of the system, not the final product. Production-level consistency comes from domain-expert-centred design, multiple components and a systematic evaluation infrastructure.
Measure Performance by Decision-support Quality,
Not Just Speed
TradeComplize monitors accuracy systematically — not only through outcomes, but with a ground-truth data set, task-specific metrics and regular evaluation processes. Performance results are published together with the data set and infrastructure conditions used.
Ground-truth Data Set
Performance is tested on expert-verified document sets, accounting for document-type and case diversity.
Task-specific Metrics
Discrepancy detection, false alarms, duration, explainability and robustness are each tracked with their own metric.
Internal Evaluation Infrastructure
Progress on precision, recall and similar metrics is tracked per release with in-house evaluation tools.
Measurement framework
Discrepancy detection
Example measure
Recall / missed findings
Publication condition
Labelled test set and expert agreement
False alarms
Example measure
Precision / unnecessary findings
Publication condition
Reporting by error type
Duration
Example measure
Median and distribution per file
Publication condition
Document count, hardware and scenario details
Explainability
Example measure
Accuracy of evidence links
Publication condition
Human evaluation rubric
Robustness
Example measure
Varying document quality and language
Publication condition
Release and data-set notes
What this page will contain once results are published
- Metrics we measure
- Accuracy, precision, recall, F1; processing time and infrastructure conditions.
- Reference data set
- Document types, case diversity, sample size and date range.
- Comparison
- General OCR, general-purpose language model and the TradeComplize approach on the same data set.
- Results
- Only reproducible metrics approved by the technical team.
- Methodology note
- A link to the measurement detail next to every figure.
- Update date
- Benchmark release, measurement date and change notes.
TradeComplize is optimised to analyse average sets of documents presented under an LC in under five minutes, given suitable infrastructure conditions.
Processing time varies with the number and nature of the documents, file quality, server configuration and the institution-specific scope of checks. The time covers processing of the full presented set; the final discrepancy assessment is completed by expert review.
No measured benchmark result, such as an accuracy rate, is published on this page. The timing statement above is not a benchmark result but the target range the system is optimised for, subject to the conditions in its footnote. Verified benchmark results will be published here together with the test-set scope, measurement date and methodology.
AI That Can Run Inside the Institution
For Critical Data
For banking and trade data, the architecture decision is never just a technology preference; it is handled together with data classification, access, logging, continuity and institutional policy. TradeComplize's on-premise architecture aims for the system's core components to run on the institution's own infrastructure.
Deployment in an Isolated Environment
The solution can be installed in an isolated physical or virtualised server environment provided by the bank, and can operate without an internet connection.
Configurable Data Retention
Retention of document and transaction data can be configured to the institution's policy; the institution may require that the data is not kept after processing.
Alignment with Institutional Standards
Operating system, security policies and infrastructure requirements are handled according to the institution's technical standards.
Clarified Together During Deployment Assessment
- Which components run on-premise, and their scope
- Data boundaries and retention policy for model calls
- Encryption, access, logging and deletion approach
- Business continuity, backups and shared responsibilities
- Certificates, audits and contractual evidence available
On-premise operation is a technical capability; security testing, approved software lists and institution-specific requirements are addressed separately in every bank integration. The final data flow is defined during technical design, according to the chosen deployment model.
Support Your Examiners' Capacity And
Decision Consistency
LC operations manage file volume, SLA pressure, long expert-training cycles and differing examination approaches all at once. TradeComplize structures the repetitive checks, helping experts focus on critical assessments and making institutional knowledge more visible.
Capacity and consistency
Trade operations
Make examination steps visible and manageable in a shared flow.
Traceability
Risk / Compliance
Review each finding with its document and assessment context.
Architecture and integration
IT / Security
Assess deployment, data flow and the access model against institutional requirements.
Knowledge transfer
Training / Competence
Support expert development with scenarios and feedback.
Scope, deployment model and integration are defined together through a discovery call and technical assessment.
Compare LC Examination Approaches
Against Your Business Needs
The right question is not "which tool is newer", but "which approach fits the realities of the bank's examination process". The comparison below is made by approach, not by product name.
Domain context
- General-purpose AI
- Depends on prompt and source
- Manual work
- Depends on the expert's experience
- TradeComplize approach
- A structure focused on the LC workflow
Explainability
- General-purpose AI
- Variable
- Manual work
- Depends on personal notes
- TradeComplize approach
- Document-, field- and reasoning-oriented output
Consistency
- General-purpose AI
- Depends on configuration
- Manual work
- May vary across teams
- TradeComplize approach
- Standard flow + expert control
Deployment
- General-purpose AI
- Depends on the provider
- Manual work
- In-house
- TradeComplize approach
- Assessed against institutional requirements
Final decision
- General-purpose AI
- Depends on usage
- Manual work
- Expert
- TradeComplize approach
- Expert
This table makes no claims about any specific competitor product; the comparison is criteria-based.
What Technical Teams
Ask Most Often
The questions most often raised on architecture, measurement and security.
Technical Questions
Does TradeComplize depend on a single AI model?
No. TradeComplize is designed with a model-agnostic architecture. The document-processing, machine-learning and generative AI components best suited to each task can be used together, and components can be replaced over time.
How does it differ from general OCR or LLM tools?
TradeComplize uses those technologies as raw tools. Examining documents presented under an LC requires relating hundreds of document structures, many case variations, international rules such as UCP 600 and ISBP 821, and institutional practice. A production-grade decision support system emerges when domain expertise, rule assessment, multiple components and systematic measurement come together.
Does the system make the final decision?
No. The system produces explainable findings and decision support; the discrepancy assessment and the final decision under institutional procedure rest with the authorised expert.
Questions About Measurement
How is accuracy measured?
The system is evaluated regularly on a ground-truth data set with task-specific metrics such as accuracy, precision, recall and F1. Published results are presented together with the scope of the data set and the infrastructure conditions.
How are false positives and false negatives tracked?
The two error types are tracked separately. An unnecessary discrepancy alert creates extra operational work, while a missed finding creates risk; precision and recall are therefore assessed together and compared release by release.
When do you publish benchmark results?
A result is published only when it is reproducible and when the test-set scope, measurement date, release information and methodology can be disclosed alongside it. A figure whose methodology cannot be shared does not appear on this page.
Security and Data Questions
Can the system operate without an internet connection?
Yes. In an on-premise deployment the core components can run in the institution's isolated server environment, and the system can be used without an internet connection. The scope is defined during technical design, according to the chosen deployment model.
Are documents and transaction data stored outside the institution?
In an on-premise scenario the retention policy is configured to the institution's requirements. Depending on the institution's choice, transaction data may be retained for a defined period or not kept after processing.
Does the deployment fit the institution's existing infrastructure?
Operating system, security policies, network segregation and hardware requirements are handled according to the institution's technical standards. Security testing, approved software lists and institution-specific requirements are assessed separately for every deployment.
Let's Assess Your LC Workflow Together
Share your institution's document types, examination flow, security requirements and integration needs; our team will contact you with the right solution and evaluation scenario.
Request a DemoWhat the call covers
- Solution scope for your institution
- Deployment and data-residency options
- Integration and implementation steps