Zero-Trust Security
Isolate agent execution so corporate credentials and code never leak externally.
Visdom: AI-Native SDLC for Banking
Deploy autonomous AI agents inside your existing SDLC with machine-speed feedback, risk-based governance, and cryptographically verifiable audit trails.
Generative AI has fundamentally reversed the economics of software delivery: code generation is now cheap, while review, verification, and governance have become the primary bottlenecks. We help you overcome them.
Risk-Based Triage
Auto-approve routine updates and reserve human review for critical systems.
Context Fabric
Agents receive architectural and ownership context before writing any code.
Audit Trails
Get regulator-ready logs of every AI action.
In financial services, every change needs a why and a who
AI agents ship fast but they don't inherit your controls, conventions, or regulatory history. That's where audit findings come from. Visdom fixes this by giving agents your institutional context:Visdom for Regulated Industries: Governed, Traceable AI Software Delivery
AI reverses SDLC economics: code generation becomes cheap, while verification, review, and governance become the bottleneck.
Artur Skowroński
Head of Application Development
in VirtusLab
From challenge to solution
How we help enterprise teams build agent-native SDLC
Your Challenge
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- Data Leakage & Shadow AI
- PR Backlog Bottleneck
- Context-Blind Code Generation
- Inadequate Audit Records
The Root Cause
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- Developers paste proprietary code into personal tools to meet deadlines.
- Code production scales exponentially while reviewer capacity stays linear.
- Agents lack access to institutional rules, architecture decisions, and tribal knowledge.
- Git commits do not record prompts, model versions, tool calls, or review depth.
The Visdom Platform Solution
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- Zero-Trust Execution: Self-hosted Kubernetes deployment with transparent proxies that strip credentials before model calls.
- Risk-Based Triage: Auto-merges low-risk diffs (Green) and routes complex changes (Red) to senior architects.
- 5-Layer Context Fabric: Compiles repositories, documentation, and history into compact, agent-ready context.
- Cryptographic AI Tracing: Generates signed, tamper-evident logs of all AI sessions ready for regulatory review.
Get the Visdom 2.0 Architectural Overview
Download the PDFOur expertise
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For a bank, introducing autonomous coding agents is an architecture, security and governance problem. Here is how Visdom addresses data residency, machine-enforced boundaries, credential security, AI tracing, audit integrity, human oversight, code quality, test verification, organizational context and cost governance.
How Banks Govern Autonomous AI in Software Development
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For a bank, introducing autonomous coding agents is an architecture, security and governance problem. Here is how Visdom addresses data residency, machine-enforced boundaries, credential security, AI tracing, audit integrity, human oversight, code quality, test verification, organizational context and cost governance.
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Most of the AI-generated code already in your codebase arrived through unsanctioned tools on personal accounts. This is Shadow AI — and like shadow banking before 2008, the risk is invisible until it isn't. Here's how to govern it with risk-based triage, traceability, and Continuous Modernization.
Shadow AI Is Already in Your Codebase - Why Banking Needs a Risk-Based Governance Model for AI-Native SDLC
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AI tools work. Adoption is above 80%. And yet measurable business value remains stubbornly low. The bottleneck is not the model — it's the software delivery system around it.
Why AI-Native Software Delivery Needs More Than a Better Model
What changes in practice
80%
Shorter lead time: from 10 days to 2 days
12 min
Time to first review across 200 PRs and 3 reviewers
<5 min
End-to-end CI: down from 45 minutes
60%
Merged without human (human review where it matters)