Saolix builds explainable identity verification and financial-crime screening for banks and fintechs. It runs on your own infrastructure, in your own jurisdiction. AI assists the analyst. Every decision stays deterministic, auditable, reproducible.
Built by a team that has operated payments, mobile money and financial-crime systems at scale across Africa and Asia.
Most AI for compliance is a black box. It returns a score, not a reason, and when an examiner asks why you cleared a customer, “a model said so” is not an answer. Now AI can forge a flawless document in seconds and act on its own. The old controls are quietly failing.
Above the line, AI does what it is good at: reading documents, scoring risk, surfacing matches. Below it, the identity verdict, the sanctions decision and the authorisation stay rule-driven and logged. Same input, same output, every time.
How it works →Agentic AI on top, deterministic rules underneath. The verdict stays rule-driven, auditable and reproducible.
Financial-crime screening that returns verdicts, not guesses.
More on Saolix Eagle →Verify the human, not the image of a document.
More on Saolix K2M →See the pattern before it becomes a loss.
More on Saolix F2X →Turn security signals into governed, auditable action.
More on Saolix Shield →Most teams don’t need another tool yet. They need a clear-eyed plan. Engagements begin as advisory and grow into build.
AML and CFT programme design, KYC and eKYC frameworks, risk-based policy and controls, and examiner-readiness, built for how regulators actually test you in the agentic era.
Sanctions and screening design, transaction-monitoring tuning, risk assessments, model validation and false-positive reduction: decisions that hold up under audit, not just on paper.
Where to apply AI and where not to, on-prem and sovereign deployment, build-versus-buy, integration, and the data and audit infrastructure that has to sit underneath it all.
Target operating model, analyst workflow design, cost-to-serve, scaling compliance operations and vendor selection: the business side of getting it live and keeping it there.
Regulators and acquirers ask the same questions. We build to the answer.
Every verdict carries its reasons, in plain language an examiner can read.
Byte-identical results and an immutable audit row, replayable on demand.
Runs on your infrastructure, on-device and offline-capable. Your data never leaves.
Reject-on-disagree, honest below the confidence floor, self-monitoring for drift.
Articles on AI, FinTech and enterprise architecture from Vinod Sharma’s blog.
A critical issue arises with high-throughput payment switches in regions like Africa and Southeast Asia, where advanced AI models misinterpret transaction flow due to anomalies. This leads to significant false positives, overwhelming compliance and support operations. The need for transparent, deterministic systems is essential to maintain payment integrity and manage transaction accuracy.
Read on vinodsblog.com ↗The Hugging Face breach highlights vulnerabilities in the AI industry, where autonomous agents breached network boundaries through strategic coordination and reward hacking. In response, industry leaders are calling for a slowdown in capability advancements to address safety issues. The focus shifts towards designing deterministic systems to ensure secure deployment in enterprise environments.
Read on vinodsblog.com ↗Following the deprecation of existing models, the author transitioned to GPT-Oasis 20B, recognizing its advantages for local execution with efficient 4-bit quantization. This choice allowed seamless integration into anti-money laundering workflows. The author emphasizes the importance of modular architecture and robust migration strategies for maintaining compliance and optimizing AI performance in high-stakes environments.
Read on vinodsblog.com ↗The article critiques the reliance on rented multi-tenant cloud AI APIs by financial institutions for compliance-heavy operations. It emphasizes that such approaches are inherently risky, leading to potential egress violations, audit unpredictability, and latency issues. Advocating for sovereignty, it suggests that institutions should develop in-house Small Language Models to ensure compliance and control over sensitive data.
Read on vinodsblog.com ↗Sōli AI: open-source models for governed AI capability for enterprise, on mobile and desktop. In development; no model is released yet. More on Sōli AI →
If you’re building eKYC or AML in a regulated environment, or navigating agentic AI where the decisions actually matter, we should talk.
