When the regulator asks "explain your AI" we've already prepared your answer.

Compliant AI Workflow Design

Your AI deployment needs to survive more than go-live. It needs to survive an audit.

Most AI vendors design for production. Foundry designs for the examination room.

We architect AI workflows with explainability, governance, and audit-readiness as structural requirements — not retrofits. When a regulator, a board, or an external auditor asks "show me how this decision was made", your system already has the answer.

• Explainability architecture — LIME/SHAP outputs mapped to regulator-readable language, not raw probability scores
• Model governance documentation: model cards, validation reports, change management logs, and version histories
• AI risk assessments aligned to FATF Guidance on AI (2021), applicable central bank AI circulars, and sector-specific regulatory frameworks
• Pre-deployment regulatory review — testing model output against known failure modes and regulatory expectations before the examiner does
• Continuous monitoring pipelines for model drift, false positive rate creep, and population shift

Custom Model Design & Calibration

Every model reflects the assumptions baked into the data it was trained on. When your population, your risk environment, and your regulatory context were not part of those assumptions, the model fails — sometimes quietly, always expensively.

Off-the-shelf models carry off-the-shelf failure modes. AML rule libraries calibrated to European banking patterns produce 60–80% false positive rates in African institutions. Credit models built where thin-file customers are the exception create discriminatory outcomes where they are the norm. LLMs deployed without output validation generate confident, well-formatted text that is factually wrong — and in a regulated submission, that is liability.

These are not edge cases. They are predictable consequences of deploying models that were not designed for where you operate.

Foundry designs and calibrates models to your data population, your sector, and the performance standards your regulatory environment requires — whether that model detects financial crime, scores creditworthiness, processes claims, powers an LLM workflow, or surfaces risk intelligence that a board will eventually be asked to sign off on.

• Detection model design: AML typology development, fraud pattern modelling, and anomaly detection calibrated to your transaction network — not imported baselines
• Behavioural baseline modelling against your historical data; false positive rate engineering that reduces operational noise without reducing coverage
• Predictive model design: credit scoring, underwriting, churn, and propensity models with fair lending compliance, thin-file handling, and adverse action explainability
• LLM deployment design: open-weight model selection (Llama, Mistral, Qwen) for on-premises inference, fine-tuning governance, and RAG architecture for document-intensive workflows
• Prompt governance and output validation for any LLM-generated content used in regulated submissions — SAR narratives, audit reports, regulatory correspondence, clinical documentation
• Hallucination mitigation: guardrails, output grounding, and human-in-the-loop review pipelines for any generative output that carries regulatory or operational consequence
• Sensitivity analysis, threshold calibration, and performance tuning across all model types and deployment contexts

Infrastructure Architecture & Deployment

The choice between on-premises, hybrid, and cloud is not a cost decision. It is a data sovereignty decision.

For a regulated financial institution in Africa, where your data lives determines what you can legally do with it. The Kenya Data Protection Act 2019, POPIA in South Africa, and the NDPR in Nigeria all impose specific requirements on the location and transfer of personal data. Your cloud choice is a compliance choice first.

Foundry designs and deploys infrastructure that satisfies your jurisdiction's data residency obligations without sacrificing the operational performance your compliance team needs to work at speed.

Core Banking & Ecosystem Integration

The hardest part of any compliance AI deployment is not the model. It is getting clean, real-time data out of a core banking system built in 2003.

Every African institution's compliance infrastructure is only as good as the data pipeline feeding it. Fragmented customer records, batch-only CBS exports, undocumented API schemas, and legacy transaction codes that mean different things in different branches — these are the real integration problems. Foundry has solved them before.

We design integration layers that extract, normalise, and enrich transaction data from whatever core platform you are running, and deliver it into your compliance systems with the latency your monitoring obligations require.

• Temenos T24 / Temenos Transact — direct API and batch extraction
• Oracle Flexcube — FCUBS REST and legacy MDB integration
• Finacle (Infosys) — CIF and transaction data normalisation
• Mambu, i-flex, and bespoke core banking platforms
• Real-time streaming via Kafka where CBS supports event publishing
• ETL pipeline design for institutions with batch-only data extraction
• Customer identity reconciliation across fragmented data sources
• Webhook architecture for alert routing to existing case management tools

Data Residency, Sovereignty & Cross-Border Compliance

A deployment that complies with Kenya's DPA 2019 may not comply with POPIA, NDPR, or your central bank's data handling circular.

African financial institutions increasingly operate across multiple jurisdictions with diverging and sometimes contradictory data protection frameworks. A cross-border payment processed in Nairobi, cleared through Johannesburg, and reported to Kampala touches three regulatory regimes simultaneously. Most vendors design for one jurisdiction and hope the others don't notice.

Foundry designs for multi-jurisdiction compliance from architecture — not as a legal opinion added after the system is built.

• Jurisdiction mapping: a written inventory of which data assets are subject to which national framework
• Data residency enforcement: technical controls (encryption key locality, region-locked storage, network policy) that ensure PII remains in-country
• Cross-border transfer safeguards: standard contractual clauses, adequacy decisions, and localisation exemptions documented and maintained
• Regulator-ready data lineage documentation: provenance, transformation, and access logs for every sensitive data element
• FATF cross-border typology coverage: 22 high-risk jurisdiction monitoring with country-specific transaction patterns
• Multi-jurisdiction regulatory reporting: STR/CTR/SAR formatting for FRC (Kenya), FIC (South Africa), NFIU (Nigeria), and others