Contact

Lilongwe, Malawi +265 999 201 886

Our Approach

Intelligent process automation, decision and scoring engines, document intelligence and domain-grounded AI assistants embedded in core systems.

Intelligent Process Automation

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We automate the processes that move work through an institution - applications, claims, approvals, renewals - orchestrating across core banking, ERP, CRM and registry systems. Rules handle the deterministic path and models handle the judgement calls. Anything that cannot be decided automatically drops into an exception queue with the reason attached, and every action is written to an audit trail that can be reconstructed later.

Decision and Scoring Engines

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We build scoring engines for credit, risk, eligibility and prioritisation that run against configurable thresholds rather than hardcoded logic, so a risk officer can adjust parameters without waiting for a code release. Each decision records the inputs, the rule version and the score that produced it, which is what makes an outcome defensible to a regulator or on appeal.

Intelligent Document Processing

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We turn the paper an institution runs on - applications, permits, invoices, contracts, clinical records - into structured, validated data. Extraction combines OCR, layout models and field-level validation, with confidence thresholds that route uncertain documents to a person instead of guessing. Extracted values are checked against the source system before anything is committed.

Retrieval-Augmented Assistants

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We build assistants grounded in an institution's own policy manuals, regulations and archives rather than general web knowledge. Answers carry citations back to the source paragraph, access follows the user's existing permissions, and the assistant hands over to a human when confidence is low or the question falls outside its corpus.

Anomaly and Fraud Detection

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We monitor transactions, claims and field submissions continuously, flagging outliers against learned behaviour rather than fixed rules alone. Alerts are ranked so investigators work the highest-value cases first, and confirmed outcomes feed back into the model so detection sharpens with each cycle.

Model Operations and Governance

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We treat models as production assets: versioned, monitored for drift and evaluated against a held-out set before promotion. Every deployment has a rollback path, and high-impact decisions keep a human in the loop. Performance is reported against the documented baseline so a degrading model is caught before it affects outcomes