OUR SOLUTIONS

Turn fragmented data into trusted, decision-ready intelligence

AIQA improves the quality, completeness, usability and governance of data across critical business processes.

From regulatory reporting and financial-crime compliance to risk, commercial operations, data platforms and legacy estates, AIQA brings together structured and unstructured information; applies validation and enrichment; highlights exceptions and guides users through potential fixes; and provides the traceability needed to act with confidence.

WHY AIQA

A controlled foundation for critical data

Robust, auditable foundation

Core outputs are produced by controlled, reproducible logic rather than probabilistic LLM behaviour meaning consistent results, traceability, easier model risk and audit assurance

Predictable operation, not token-driven economics

As AIQA’s core data-quality workflows are not dependent on per-token LLM consumption, our customers can run critical mapping, validation and remediation processes without unpredictable token costs or operational reliance on a single external model provider.

LLM-independent by design

Core logic and workflow execution remain under your control. AIQA can be deployed to meet your security, residency and governance requirements—on-premise or in the cloud.

Start with proven domain content—not a blank platform

AIQA's Domain Library provides pre-configured data-quality assets that give teams a practical starting point: reusable rules, validation patterns, mappings, data models and workflow templates. Your experts refine what matters for your organisation rather than building every control from scratch.

Turn business rules into data controls

Write or import a rule in business language. AIQA interprets its meaning, identifies the relevant fields in your data model and proposes the mapping and validation logic—ready for expert review and approval.

The outcome is more than cleaner data. It is data that is ready to report, ready to analyse, ready to automate and ready to support better decisions.

Regulatory Reporting and MIS Readiness

Prepare trusted data for supervisory, prudential and management reporting

Regulatory and management reporting depend on data that is accurate, complete, timely and explainable. Yet the underlying information is often distributed across multiple operational systems, data stores, spreadsheets and manual processes.

AIQA creates a controlled data supply chain for regulatory submissions and management information. It profiles fragmented source data, identifies quality issues early, standardises key attributes and supports validation before information enters downstream reporting processes.

This reduces the burden of late-cycle reconciliation and manual correction, giving reporting teams clearer evidence of where figures came from, how they were transformed and whether they are fit for purpose.

Mitigate the cost associated with regulatory change with AIQA’s auto-maintenance features which automatically identify candidates for both new and redundant mappings when new data requirements are mandated by the regulator or there are changes to source systems

How AIQA helps

Profile and assess data quality across fragmented source systems.
Identify incomplete, inconsistent, duplicate or anomalous reporting data.
Standardise and validate critical attributes before they enter reporting workflows.
Provides users with recommended fixes and confidence ratings
Accelerate mapping from source data to common regulatory data models.
Automate maintenance of changes to source data and target data models
Support more granular reporting-data requirements, including initiatives such as iReF and BIRD.
Improve lineage and traceability from reported values back to source data and transformations.
Reuse governed data across regulatory reporting, risk, finance and management information.

Business outcomes

Faster reporting-data preparation.
Minimised effort involved in regulatory and source data change
Reduced manual reconciliation and manual intervention.
Stronger confidence in regulatory submissions.
Improved transparency for internal review and audit.
More reusable data foundations for future regulatory change.

Accelerate regulatory data readiness

Start with reusable regulatory-reporting data-quality patterns, mapping structures and control templates—then adapt them to your products, source systems and governance model.

AIQA Regulatory Data Library

Pre-built control patterns, mapping assets and workflow templates that reduce the effort to move from fragmented source data to defensible reporting outputs.

From regulatory instruction to implementable control

AIQA helps translate policy and reporting rules into data-quality controls by identifying the relevant attributes, source fields and validation logic across your data model.

Compliance, KYC and AML

Create a more complete and reliable view of customers, entities and risk

KYC and AML decisions rely on information from a range of, usually siloed, sources: customer records, onboarding systems, screening platforms, transaction systems, third-party sources, documents, emails, PDFs and scanned images.

When that information is incomplete, inconsistent or disconnected, compliance teams must spend valuable time assembling evidence manually. Important signals can be missed or detected too late, customer risk assessments can be harder to defend and investigations can take longer than necessary.

AIQA helps bring structured records and unstructured evidence into a more connected, validated and auditable information foundation. It supports data extraction, enrichment, entity matching, quality validation and exception management, helping teams make better-informed decisions while retaining appropriate controls and human oversight.

How AIQA helps

Extract information from structured systems and unstructured inputs such as PDFs, scanned documents, emails and JPEGs.
Enrich customer and entity information with relevant internal and external context; research workflows accessing firm databases and also public and paid information sources
Validate completeness, consistency, plausibility and timeliness of KYC data.
Identify duplicate, conflicting or incomplete customer and counterparty records.
Support entity resolution and the creation of clearer customer, ownership and relationship views.
Collate weak signals from siloed data sources to support ongoing risk sensing and alerts before transfer activity.
Improve visibility of the data, checks and exceptions supporting sensitive decisions.

Business outcomes

More complete customer and counterparty information.
Faster, more consistent onboarding and periodic-review processes.
Improved quality of data used in screening and investigation workflows.
Anticipate criminal transfer of funds
Reduced manual evidence gathering.
Better auditability and control over compliance decisions.

Risk, Finance and Treasury Data Quality

Build a trusted data foundation for critical financial decisions

Risk, finance and treasury teams often rely on overlapping data from the same systems, yet work with different extracts, calculations, definitions and local adjustments.

The result can be repeated reconciliation, inconsistent numbers and delayed decisions. Teams may spend more time debating whether a number is correct than understanding what the number means.

AIQA helps organisations apply consistent data-quality controls across finance, risk and treasury workflows. It supports earlier validation of key inputs, reusable rules, clear exception handling and ongoing visibility into data fitness.

The result is not simply cleaner data. It is information that is ready for action and ready to support decision-making.

How AIQA helps

Profile and monitor critical finance, risk and treasury datasets.
Apply common validation rules across multiple business workflows.
Identify poor-quality, incomplete, inconsistent or late-arriving inputs early.
Standardise data definitions and attributes across functions.
Highlight exceptions and suggest potential root causes for remediation.
Support clear data ownership, quality metrics and remediation tracking.
Improve confidence in data used for forecasts, liquidity analysis, capital management, risk measurement and management reporting.

Business outcomes

Reduced time spent reconciling finance, risk and treasury data.
Greater consistency across key measures and reports.
Earlier detection of data issues before they affect downstream calculations and analysis.
Faster, more confident management decisions.
Stronger control and accountability for critical data.

Operational and Commercial Intelligence

Make customer, market and pricing information more actionable

Commercial and operational teams need a complete, current and consistent view of customers, prospects, products, markets and pricing. In practice, that information is often spread across CRM platforms, spreadsheets, pricing tools, analyst reports, prospectuses, documents and operational systems.

This creates duplicate records, incomplete account profiles, inconsistent entity hierarchies and valuable intelligence that remains trapped in unstructured content.

AIQA helps organisations unify, enrich and maintain customer, market, pricing and entity data. It can ingest both structured and unstructured information, extract relevant facts, validate records and link information to the right customer, legal entity, product or opportunity.

How AIQA helps

Unify CRM, customer, pricing, market and entity data across multiple environments.
Identify duplicate, incomplete, stale or inconsistent records.
Validate names, identifiers and classifications against internal and external libraries
Extract and interpret information from PDFs, analyst reports, prospectuses, presentations, scanned documents and JPEG images.
Enrich account and opportunity records with relevant market and entity intelligence.
Link extracted information to the correct customer, legal entity, product or opportunity.
Share governed data across sales, marketing, onboarding, customer success, finance and operations.

Business outcomes

More accurate CRM and account information.
Better prospect prioritisation and account planning.
Improved visibility of customer relationships and buying groups.
Faster access to relevant market and competitive intelligence.
Less time spent searching across systems, documents and spreadsheets.
More consistent commercial and operational decisions.

Data Warehouse Quality and Governance

Accelerate onboarding and make data more reliable, governable and reusable

A data warehouse can centralise information from across the enterprise, but centralisation alone does not make that information trusted or easy to reuse.

Without effective controls, poor-quality source data can enter the warehouse, inconsistent definitions can proliferate and users can struggle to understand a dataset’s origin, meaning, limitations or suitability for a particular use case.

AIQA supports a data quality led approach to source-to-warehouse onboarding and ongoing governance. It helps teams understand incoming data earlier, identify issues before they become embedded in downstream models and continuously monitor data quality after onboarding.

How AIQA helps

Profile source data before warehouse onboarding.
Identify missing values, structural issues, unexpected formats, duplicates and anomalies.
Support mapping, standardisation and validation between source and target data models.
Apply reusable data-quality rules across multiple data sources and pipelines.
Monitor completeness, consistency, validity and timeliness on an ongoing basis.
Highlight exceptions and support remediation workflows.
Improve visibility of data definitions, quality outcomes, lineage and ownership.
Help users identify data that is fit for reporting, analytics, automation and AI initiatives.

Business outcomes

Faster source-to-warehouse onboarding.
Reduced downstream rework and remediation.
Higher-quality data for reporting, analytics and AI.
Greater confidence in warehouse datasets.
Stronger data governance and lineage.
More reusable enterprise data assets.

Legacy Solution Enablement

Improve data quality and automation without immediate core-system replacement

Legacy platforms often remain central to business operations, but can be difficult, costly or risky to change. Their data may be valuable, yet hard to extract, validate, integrate and reuse across modern reporting, workflow, analytics and automation environments.

This can create manual workarounds, spreadsheet-based reconciliations, rekeying, exception handling and unnecessary operational risk.

AIQA provides a practical way to improve the data and processes surrounding legacy systems without requiring immediate re-engineering or replacement. It can extract, profile, validate, enrich and transform data from the existing estate, helping organisations make it more accessible, controlled and automation-ready.

How AIQA helps

Extract and process data from legacy applications, files, reports and documents.
Profile, cleanse, standardise and validate legacy data before downstream use.
Connect legacy outputs to modern data warehouses, reporting platforms, workflow tools and analytics environments.
Introduce quality controls and exception handling around existing data flows.
Reduce manual rekeying, reconciliation and spreadsheet intervention.
Support greater straight-through processing and automation.
Provide a controlled bridge between the current technology estate and a longer-term modernisation programme.

Business outcomes

Improved data quality without immediate core-system replacement.
Reduced implementation time and transformation risk.
Lower levels of manual intervention.
Reduced operational risk and improved resilience.
Increased automation potential.
More flexibility to modernise on a phased, prioritised basis.

Reconciliation and Data Quality

Reduce recurring breaks by addressing their root cause

Reconciliation is a vital control across finance, risk, treasury, operations and regulatory reporting. But recurring reconciliation breaks are often symptoms of upstream data-quality issues: missing identifiers, duplicate records, inconsistent formats, incorrect values, timing differences, mapping errors and disconnected data flows.

AIQA helps organisations move beyond manual break resolution and towards a more preventative, data-quality-led reconciliation model.

By profiling and validating data earlier, standardising key attributes, improving matching context, identifying recurring causes and preserving clear lineage, AIQA helps teams reduce avoidable exceptions and investigate genuine discrepancies faster.

How AIQA helps

Profile reconciliation inputs to identify completeness, consistency and structural issues.
Validate critical data before it reaches downstream reconciliation workflows.
Standardise identifiers, formats, reference data and key matching attributes.
Detect duplicate, missing, invalid and anomalous records.
Enrich records to improve matching accuracy and reduce false exceptions.
Categorise breaks by likely root cause, including timing, mapping, source-data and transformation issues.
Identify recurring exceptions and the systems, attributes or processes generating them.
Provide lineage and audit evidence from source data through transformation, matching and remediation.
Route issues to accountable data owners and track resolution.
Feed reconciliation outcomes back into continuous data-quality improvement.

Business outcomes

Fewer recurring reconciliation breaks.
Reduced manual investigation, rekeying and spreadsheet intervention.
Faster resolution of genuine exceptions.
Improved accuracy, completeness and consistency of critical data.
Stronger auditability, ownership and control evidence.
Lower operational risk across finance, risk, treasury, operations and reporting.
Greater potential for straight-through processing and automation.

Ready to improve the quality of critical business data?

Whether the priority is regulatory reporting, KYC, risk, commercial intelligence, data-platform governance or legacy-system enablement, AIQA can help identify where poor-quality data creates cost, risk and delay—and establish a practical route to improvement.

Talk to us about how AIQA can help turn fragmented data into governed, validated and decision-ready intelligence.