Code Assurance · Compliance Automation
The World Has Enough Smart AI.
We Built a Proof Machine.
World's First Quantum Multi-Phase Deterministic Assurance Model for enterprise code security and compliance automation.
AI Today · Quantum Tomorrow · Governed Always
SusangaAI™ does not guess. It does not estimate. It does not hallucinate. It proves. Every code finding is a mathematical proof, every compliance verdict is governed by formal authority, every result is signed into a tamper-evident evidence chain.
What We Built
The world's first quantum-assured, deterministic code assurance and compliance platform.
For a decade, code security has been a choice between speed, accuracy, and compliance — pick two. AI made it worse, not better. Probabilistic AI tools hallucinate at 30–40% false positive rates. Rigorous static analysis blocks developer velocity for hours. Manual compliance audits cost millions and cannot keep pace with modern release cycles.
SusangaAI™ ends the trade-off. Built on a deterministic execution substrate, governed by Bounded Authority Action, and architected for quantum hardware integration, the platform delivers mathematical certainty at production speed across 21 compliance frameworks — with every finding signed into an auditor-defensible evidence chain.
Other tools tell you what they think might be wrong. SusangaAI™ tells you what it has proven is wrong — and gives you the evidence to prove it to anyone.
The Problem
Every Security Leader Faces an Impossible Choice — The Trilemma.
CISOs have always been told: pick two. Fast AI tools are quick — but hallucinate at 30–40% false positive rates. Rigorous static analysis is accurate — but blocks developer velocity for hours. Manual compliance audits are thorough — but cost millions. SusangaAI™ delivers all three.
Accuracy
Legacy tools flood teams with 60–90% false positives.
Mathematically proven, <2% false positive rate.
Speed
Traditional analysis takes hours to days per scan.
Real-time verification at production speed.
Compliance
Manual compliance mapping is error-prone and slow.
Automated mapping across 21 frameworks.
SusangaAI™ breaks the legacy accuracy/velocity trade-off by separating probabilistic intelligence from deterministic verification. Positions are illustrative; specific industry benchmarks vary by tool and codebase.
Comparison
Beyond Scanning, into Mathematical Certainty.
The code security market is filled with tools that scan. SusangaAI™ is the only platform that proves.
| Capability | SusangaAI™ | Traditional SAST | AI Code Assistants |
|---|---|---|---|
| Mathematical Verification | Every output proven | None | None |
| False Positive Rate | <2% | 30–40% | Unknown |
| Quantum Optimization | Patent application published | None | None |
| Compliance Automation | 21 frameworks | Manual mapping | None |
| Reproducibility | 100% deterministic | Variable | Non-deterministic |
| Cryptographic Audit Trail | Tamper-evident | None | None |
| Air-Gapped Deployment | Full capability | Limited | None |
| IDE Integration | VS Code, IntelliJ | Varies | Native |
Not Claims. Proof.
Validated on NIST, OWASP, PCI DSS, and SARD — alongside 39M+ lines of code.
5 million lines of real production code combined with 34 million lines of synthetic code from Kaggle research datasets. Production-verified April 19, 2026.
Industry average: 30–40%. ~27× better.
Every output formally proven. Unique — no equivalent in market.
39M+ LOC validated (5M real + 34M synthetic). Production-verified.
Byte-identical findings across independent runs. SHA-256 verified.
NIST, PCI DSS, HIPAA, GDPR, EU AI Act, ISO 27001, FedRAMP…
3–4 year competitive lock potential upon grant. PCT-eligible.
Detection rate plotted across six independent validation corpora. SusangaAI™ maintains 98%+ detection across NIST Juliet, OWASP, SARD, PCI DSS v4.0, the 5M-LOC real production corpus, and the 34M-LOC Kaggle synthetic corpus. Zero observed false positives across 825 findings in five production runs.
Who Are You?
Choose your path forward.
For Investors
SusangaAI™ is positioning a Seed round of $2.5M–$4M for deep-tech investors with regulatory-tech, quantum, or DevSecOps thesis depth. The architecture, IP position, and EU AI Act commercial wedge are independently verifiable under NDA.
Request the Investor Deck (NDA) Design Partner · Q2 2026For Enterprises
Regulated enterprises in financial services, healthcare, government, and AI-native technology can now apply for the design partner program. Limited slots ahead of the EU AI Act enforcement window — 02 August 2026.
Apply as Design PartnerFrequently Asked
The questions that come up first.
Why does SusangaAI™ claim to be the world’s first system of its kind?+
SusangaAI™ is not a generic “AI security tool.” It is, to our knowledge, the first system that combines: a deterministic multi-phase code assurance pipeline, an automated multi-framework compliance lattice spanning 21 frameworks, and a quantum-assured verification architecture where quantum-grade techniques govern the analysis itself, not just encryption. This combination is described in our published patent application and has been validated on 39M lines of code in production-grade runs, with zero observed false positives across 825 findings as of 19 April 2026. We use “world’s first” to describe this specific architecture and capability set, not AI security in general.
What does "deterministic" actually mean in practice?+
The same code, analyzed twice with the same configuration, produces byte-identical findings — verified via SHA-256 cryptographic comparison across independent runs. A regulator asking "can you reproduce this finding?" receives a cryptographically verifiable answer, not a probabilistic narrative.
What is "quantum-assured" — are you using a real quantum computer?+
SusangaAI™ applies quantum-grade techniques where they govern the analysis itself, not just the encryption around it. In production today, those techniques run primarily on classical infrastructure as quantum-inspired and error-mitigated optimization steps, accounting for roughly 30% of the analysis pipeline, with the remaining ~70% driven by specialist AI models and deterministic verification. We are preparing the architecture for real quantum hardware integration as commercial access becomes practical. Crucially, the lattice geometry, governance kernel, and evidence chain remain stable whether the execution substrate is classical or quantum. Quantum is additive, never load-bearing.
Can SusangaAI™ be deployed air-gapped?+
Yes. Full capability is preserved across three deployment modes: air-gapped (zero external connectivity, customer-controlled infrastructure), sovereign cloud (EU, U.S., U.K., GCC, India jurisdictions), and standard SaaS. All three modes preserve byte-identical determinism and complete evidence chain integrity.
How is SusangaAI™ different from AI code assistants like Copilot or Cursor?+
AI code assistants generate code suggestions. SusangaAI™ proves whether code is correct against regulatory and security invariants — and produces a cryptographically signed evidence chain for every finding. The same code analyzed twice produces byte-identical findings; AI assistants are non-deterministic. We are not in the same category.
How does SusangaAI™ achieve a <2% false positive rate when industry standard is 30–40%?+
Every finding is formally verified against the Invariant Registry — a structured representation of the operational and regulatory constraints the system must preserve. Findings that fail verification are not delivered. In validation runs to date, SusangaAI™ has shown zero observed false positives across 825 findings in five production runs on the 39M-LOC validation corpus.
Which compliance frameworks are covered?+
21 frameworks across six regulatory domains: AI & Data Governance (EU AI Act Annex IV, NIST AI RMF, ISO 42001); AppSec & Secure SDLC (NIST SSDF 1.1, OWASP ASVS 4.0.3, CIS Controls v8); Financial Services (DORA, EBA, EIOPA, SAMA); Healthcare & Data Protection (GDPR, HIPAA Security Rule); Standards & Audit (ISO 27001:2022, ISO 27003, SOC 2 Type II, COBIT 2019); and Government & Critical Infrastructure (NIST RMF 800-53 Rev 5, NIST CSF v1.1, FedRAMP Moderate, NIS2, PCI DSS v4.0.1). Every code finding is automatically mapped to all applicable framework controls.
What is the IP position?+
An Indian patent application has been published and is under examination, covering the combination of quantum-enhanced deterministic multi-phase analysis, multi-framework compliance mapping, formal verification, and bounded authority governance. The filing is PCT-eligible, supported by a broader trade-secret catalog covering the CP–SP–SSP taxonomy and implementation parameters. Together, this positions SusangaAI™ for a potential 3–5 year competitive advantage upon grant, depending on jurisdiction and claim scope.
How does pricing work for the Design Partner Program?+
Design partner pricing is locked through 2027 and offered at a material discount to standard enterprise pricing. Final pricing depends on deployment model (air-gapped, sovereign cloud, or SaaS), scope of codebase coverage, integration requirements, and framework coverage priorities. Program benefits include quarterly architecture reviews with the founding team, priority support, and direct input on roadmap sequencing.
How long does a pilot take?+
Typical design-partner pilots are scoped in 1–2 weeks, deployed in 4–6 weeks, and evaluated over 90 days. Scope depends on deployment mode, framework priorities, and whether the pilot targets code assurance, compliance reporting, or both.
What evidence do customers actually receive?+
Customers receive deterministic findings, framework mappings, and a cryptographically signed evidence chain that can be replayed and independently verified. In regulated use cases, this can be packaged into audit-ready artifacts aligned to internal governance and external compliance requirements.
Can I see the technical architecture under NDA?+
Yes. Architectural detail — including governance kernel internals, operating modes, parity windows, and the hardware-readiness interface — is shared under NDA with serious technical evaluators and investors. Use the contact form to request access.
The window is closing.
The EU AI Act high-risk provisions become enforceable on 02 August 2026. 65,000+ AI systems operating in the European Union will need to produce documented, demonstrable, reproducible governance — or face penalties up to €35M or 7% of global turnover.
There is one platform that produces those artifacts as a structural property of execution, not as documentation effort.
Platform Architecture
Built for proof, not for guessing.
SusangaAI™ is engineered on a foundational principle — AI proposes; the system proves. Every action that affects state passes through a formal verification layer before execution.
Architecture Overview
Three coordinated layers. One trust boundary.
SusangaAI™'s architecture enforces a strict separation between probabilistic reasoning and deterministic execution. AI is permitted to propose; only the verification layer is permitted to decide.
Contextual Intelligence
Specialist AI models read code, identify candidate findings, and propose compliance verdicts. Layer 1 outputs are explicitly classified as untrusted suggestions — they enter the system as proposals, never as authorizations. This is where the AI does what AI does well: pattern recognition, contextual interpretation, candidate generation.
Continuous Learning
Specialist models continuously refine their analysis through validated benchmark feedback loops. NIST Juliet, OWASP Benchmark, SARD, and proprietary enterprise corpora drive ongoing model calibration. Layer 2 is where SusangaAI™ gets sharper over time without sacrificing the deterministic guarantees of Layer 3.
Verification Backbone
The deterministic governance and proof layer. Every Layer 1 proposal is verified against the Invariant Registry — a structured representation of every regulatory and operational constraint the system must preserve. Findings are formally proven, mathematically reproducible, and signed into an evidence chain. This is the trust boundary. Nothing crosses it without verification.
Governance Architecture
Three layers of governance. One trust boundary.
Pre-Action Governance
Governance starts before the first line of code is read. Enterprise policy, data-handling rules, and tenant boundaries are enforced at the perimeter — uniformly across cloud, sovereign, and air-gapped deployments. What enters the analysis pipeline has already cleared the policy gate.
Bounded Authority Action Governance
The deterministic verification core. SusangaAI™ treats every AI-layer output as an untrusted proposal — never as an authorization. The probabilistic layer cannot bypass a governance decision, by design. This is the trust boundary, and it is the layer that produces the cryptographically signed evidence chain.
QEN v2.0 — Quantum-Optimization Governance
Quantum-assisted analysis carries its own governance envelope. Quantum never compromises determinism or admissibility. When a quantum-influenced result fails its admissibility checks, the system completes the work on the deterministic classical path — without semantic disruption to the customer.
"Where competitors offer AI guardrails, SusangaAI™ offers AI consequences — and the difference is what makes the difference defensible to a regulator."
Governance kernel internals — including pre/post-gate logic, operating modes, and telemetry structure — are shared under NDA with serious technical evaluators.
Safety Interlock
A read-only safety interlock between engine and report.
SusangaAI™ includes a pre-delivery quality gate that operates in read mode only. The gate inspects the engine's proposed findings against verified patterns and confidence thresholds, and can halt or mark outputs that fall outside the allowed error budget — acting as a safety interlock, not a generation step.
Inspects, never modifies
The gate inspects findings; it does not modify, rewrite, or generate content. Auditor-facing reports contain exactly what the deterministic verification layer produced — never anything synthesized at the gate.
Verified patterns, not heuristics
The gate's checks are against versioned, registered patterns and explicit confidence thresholds — not heuristic judgments.
Bounded by error budget
Findings outside the allowed budget are halted or marked. The budget is itself part of the configuration registry, versioned, and audit-accessible.
Audit-defensible by construction
A regulator examining a SusangaAI™ compliance report sees only deterministically verified findings, presented in regulator-ready format, with verification provenance traceable end to end.
Quantum Architecture
Not post-quantum cryptography. A quantum-assured platform.
SusangaAI™ applies quantum-grade techniques where they govern the analysis itself, not just the encryption around it. Every quantum-influenced result is verified against its classical counterpart before acceptance — no silent propagation, no untestable claims.
When a check fails parity, fails health bounds, or operates outside acceptable error budgets, the system completes the analysis on the deterministic classical path without semantic disruption to the customer. Quantum is additive, never load-bearing.
The architectural invariant matters most: the lattice geometry, governance kernel, and evidence chain are execution-backend agnostic. When quantum hardware execution scales, control identifiers do not change. The evidence chain does not change. Replayable traces remain replayable.
Architectural detail — including the operating modes, parity windows, and hardware-readiness interface — is shared under NDA with serious technical evaluators.
Quantum Trajectory
~30% quantum today. Native quantum tomorrow.
SusangaAI™ is operating today on a hybrid execution model: approximately 30% quantum-grade execution and 70% AI-driven analysis in production. Real quantum hardware execution is planned to scale within the next year.
The architectural invariant: the lattice geometry, governance kernel, and evidence chain are execution-backend agnostic. When quantum hardware execution scales, SSP_IDs do not change. The evidence chain does not change. Replayable traces remain replayable.
What Makes SusangaAI™ Unique
Same code. Same configuration. Same findings — every time.
Determinism is not a property SusangaAI™ tests for. It is a property the architecture produces by design. When the same code is analyzed twice with the same configuration, SusangaAI™ produces byte-identical findings — verified against SHA-256 cryptographic comparison across independent runs.
Audit-Defensible
A regulator who asks "can you reproduce this finding?" receives a cryptographically verifiable answer, not a probabilistic narrative.
Pipeline-Stable
CI/CD systems can rely on stable, repeatable analysis output. No flaky tests. No version drift. No "but it worked yesterday."
Replayable Governance
Every decision the system makes can be replayed from the evidence chain and verified independently.
Audit Trail
Every finding signed. Every decision traceable. Every claim independently verifiable.
SusangaAI™ extends RSA-SHA256 cryptographic signatures across every governance checkpoint. From the moment a finding is generated through every transformation, verification, and compliance mapping, the result is incorporated into a tamper-evident chain of custody.
Mathematically reproducible findings
Cryptographically signed evidence
RSA-SHA256 chain integrity
CRYSTALS-Kyber forward-compatible
Code Assurance
Every finding mathematically proven. Every fix traceable. Every commit accountable.
SusangaAI™ is code review redefined as deterministic proof. The same code, analyzed twice with the same configuration, produces byte-identical findings — every time, cryptographically verifiable.
Why Existing Tools Fail
Three categories of tool. Three categories of failure.
The code review market has fragmented into three categories — each solving one problem while creating others. SusangaAI™ exists because regulated enterprises cannot accept any of the three trade-offs.
AI Code Assistants
PromiseFast suggestions, contextual reasoning, natural-language explanations.
Failure
Probabilistic outputs that cannot be reproduced. The same prompt can produce different results on different runs. Hallucinated CVE references. No formal verification. No evidence chain.
A regulator asking "can you reproduce this finding?" receives a probabilistic answer, not a cryptographic one.
Traditional SAST
PromiseComprehensive code coverage, rule-based detection, established vendor relationships.
Failure
30–40% false positive rates. Hours-to-days scan times. Manual triage required for every finding. No compliance mapping. Findings cannot be cryptographically attested.
Developer velocity collapses under the noise. Every false positive eats engineering hours.
Manual Code Review
PromiseHuman judgment, contextual understanding, defensible findings.
Failure
Cannot scale to modern release cycles. Inconsistent across reviewers. No reproducible artifact. Cost prohibitive at enterprise codebase scale.
A 50-person engineering team cannot manually review 50,000 commits per quarter.
SusangaAI™ replaces all three categories with a single deterministic substrate that proves what is wrong, signs the proof, and maps every finding to its applicable regulatory controls — in one execution.
The Deterministic Substrate
AI proposes. The system proves.
SusangaAI™'s code assurance pipeline operates on a foundational principle: probabilistic intelligence is governed by mathematical authority — by design. AI-layer outputs are explicitly classified as untrusted proposals until verified against the Invariant Registry.
Deterministic Finding Generation
The same code analyzed twice with the same configuration produces byte-identical findings, verified via SHA-256 cryptographic comparison across independent runs. No flaky tests. No version drift. No "but it worked yesterday."
Formally Proven Verdicts
Every finding is formally proven against the Invariant Registry — a structured representation of every operational and regulatory constraint the system must preserve. Less than 2% false positive rate, with zero observed FPs across 825 findings in five production runs.
Cryptographically Signed Evidence Per Commit
RSA-SHA256 signatures extend across every governance checkpoint. Every finding, every transformation, every verification is part of a tamper-evident chain of custody — independently verifiable offline, forward-compatible with CRYSTALS-Kyber post-quantum cryptography.
IDE-Native Integration
Native plugins for VS Code and IntelliJ. CI/CD pipeline integration via GitHub Actions, GitLab CI, Jenkins, and Azure DevOps. Findings appear in the developer's existing workflow — verified, signed, and explained.
See It In Action
Code review, in flight.
Watch SusangaAI™ analyze production code in real time — proposing findings, verifying them against the Invariant Registry, and signing each result into the evidence chain.
Deterministic finding generation
The full code-assurance pipeline running on a real codebase — from candidate finding through formal verification through evidence chain signature.
One Finding. Many Citations.
Every code finding is also a compliance finding — automatically.
SusangaAI™ does not treat code review and compliance as two separate workflows. They are one inseparable system. Every finding that emerges from the verification layer is, by construction, mapped through the CP–SP–SSP compliance lattice to every applicable regulatory control across all 21 frameworks.
A single memory-management issue in a cryptographic implementation becomes simultaneously a NIST FIPS finding, a PCI DSS Section 4 finding, a HIPAA finding, and an EU AI Act Article 15 finding — automatically, deterministically, cryptographically signed.
One finding. Multiple regulatory citations. Zero manual mapping.
At a Glance
The four differences that matter most.
The full capability comparison sits on the home page. The four below come up first in every engineering-leader evaluation:
| Outcome | SusangaAI™ | Traditional SAST | AI Code Assistants |
|---|---|---|---|
| Reproducible findings | 100% deterministic | Variable | Non-deterministic |
| False positive rate | <2% | 30–40% | Unknown |
| Cryptographic audit trail | Tamper-evident | None | None |
| Compliance mapping | Automated across 21 frameworks | Manual | None |
The full architecture sits on the Platform page. The full compliance lattice sits on the Compliance page.
Ready to see code assurance in action?
Request a private demonstration. Architecture under NDA available for serious technical evaluation. SusangaAI™ runs on your codebase, in your environment — air-gapped, sovereign cloud, or standard SaaS.
Compliance Coverage
21 frameworks. One platform. Zero manual mapping.
SusangaAI™'s compliance lattice spans 21 regulatory frameworks across Financial Services, Healthcare, Government, and AI-native technology — with automated mapping, deterministic findings, and audit-defensible evidence at every checkpoint.
Full Coverage
Every regulated domain that matters.
As of platform v5.4, the compliance lattice contains 10,000+ populated controls and is scaling to ~1.35M instantiated controls. Organized by regulatory domain, not by badge wall.
AI & Data Governance
AppSec & Secure SDLC
Financial Services
Healthcare & Data Protection
Standards & Audit
Government & Critical Infrastructure
Regulatory Urgency
02 August 2026. The countdown matters.
On 02 August 2026, the EU AI Act's high-risk system provisions become enforceable. 65,000+ AI systems must produce documented, demonstrable, reproducible governance — or face penalties up to €35M or 7% of global turnover. Articles 9, 12, 13, 14, and 17 require demonstrable, reproducible, and traceable governance — not narrative documentation. SusangaAI™ is engineered against this exact regulatory specification.
For your CCO
Demonstrable governance artifacts mapped automatically to EU AI Act articles. No manual mapping. No interpretation gaps.
For your Board
Risk-quantified compliance posture. Deterministic findings. Audit-defensible at the speed of release cycles.
Automated Mapping
From code finding to regulatory citation. Automatically.
A single memory management issue in a cryptographic implementation is simultaneously mapped to four frameworks — all automated, all deterministic, all signed:
NIST FIPS
Cryptographic implementation correctness.
PCI DSS Section 4
Cryptographic security of cardholder data.
HIPAA
Encryption of ePHI in transit.
EU AI Act Art. 15
High-risk system robustness.
One finding. Four framework citations. All automated. All deterministic. All signed.
See It In Action
Compliance reporting, live.
Watch the compliance lattice generate audit-defensible reports — automated framework mapping, deterministic verdicts, cryptographically signed evidence.
Automated framework mapping
From code finding to regulatory citation — the full compliance lattice working across NIST FIPS, PCI DSS, HIPAA, and EU AI Act controls in a single pass.
Economic Impact
90% cost savings in compliance and security operations.
Manual compliance audits cost regulated enterprises millions per year — and produce artifacts that cannot be reproduced if challenged. SusangaAI™'s automated mapping, deterministic verification, and audit-grade evidence chains reduce compliance operations cost by approximately 90%.
Manual audit cost
— eliminated
False positive investigation
— eliminated
Compliance mapping fees
— automated away
Reproducibility re-work
— eliminated
Operational overhead
— minimal residual
Net savings
~90%
About
Built by an inventor. Backed by architecture. Scaling with discipline.
SusangaAI™ exists because one engineer watched the same problem unsolved across a decade of regulated-enterprise audits.
Why We Built This
Enterprises don't need more unpredictable AI. They need AI they can trust.
In her work on AI systems at one of the world's largest digital engineering services firms, Nidhi Sharma kept encountering the same limitation: powerful AI, unreliable when consistency mattered most. The same prompt could generate different answers. Hallucinations were common. Outputs were based on probability rather than verifiable logic.
That made AI difficult to depend on for code review, compliance, and governance — areas where accuracy, repeatability, and accountability are essential. That insight led to SusangaAI™.
Move AI from prediction to proof.
Identical inputs produce identical outputs. Findings are traceable. Decisions are verifiable. This is not a product aspiration — it is the structural property the architecture produces by design.
Founders & Architects
Five operators. Two continents. One thesis.
Nidhi Sharma
Founder & Inventor, SusangaAI™
Aditya V
Co-Founder · Senior Business Executive · AI & Quantum Researcher
Balaji Venketeshwar
Global Security Advisor & Strategist · Visiting Prof., IIT Bombay
Dr. Kaushik Sekaran
Technical Advisor
Kumar Rajeev
Technical Architect
Operating Model
A globally distributed team, organized by function.
Engineering & Architecture
Platform engineering, deterministic verification stack, quantum-assured runtime, and the compliance lattice.
Compliance & Research
Framework mappings across 21 regulatory standards, ongoing academic and industry research collaborations, and certification preparation.
Commercial & Partnerships
Go-to-market, design partner program, investor relations, and strategic alliances across global talent hubs.
Seed Round · Open · 2026
Investing in the architecture that makes AI accountable.
SusangaAI™ is positioning a Seed round of $2.5M–$4M for deep-tech investors with regulatory-tech, quantum, or DevSecOps thesis depth.
Why This, Why Now
Three asymmetric forces, one window.
Regulatory enforcement is binary
The EU AI Act becomes enforceable on 02 August 2026. Penalties up to €35M or 7% of global turnover force enterprise procurement decisions in 2026. No existing AI governance approach produces compliant artifacts as a structural property of execution.
The architecture cannot be retrofitted
Probabilistic AI tools cannot be made deterministic by adding a wrapper. SusangaAI™ was architected from the start as a deterministic substrate with AI as governed input. Competitors face multi-year reconstruction projects to match what we ship today.
The IP position compounds
Indian patent application published — examination pending — covering quantum-enhanced deterministic multi-phase analysis, multi-framework compliance, formal verification, and bounded authority governance. PCT-eligible. Three to five year competitive lock potential upon grant.
The Ask
$2.5M–$4M Seed · 18-month runway · Lead investor sought.
Engineering
HF training pipeline, QPU integration prep, LoRA adapter activation, production scale-out, ML and platform engineer hires.
Go-to-Market
BFSI sales lead, design partner conversion, SI channel build, first 5–10 enterprise contracts before 02 Aug 2026.
Compliance & Legal
SOC 2 Type I + ISO 27001 readiness, EU AI Act regulatory legal review, PCT patent prosecution.
Trajectory: $14M–$16M post-money valuation target · Series A 2027–28
What's Already Done
Architecture verified. IP filed. Window timed.
Patent application published
Independent third-party novelty assessment of the combined architecture.
Determinism formally proven
Five independent production runs produce byte-identical findings, RSA-SHA256 signed.
Production-verified April 19, 2026
Validated on 5M LOC real production code + 34M LOC Kaggle synthetic corpus.
21 compliance frameworks mapped
10,000+ populated controls today, scaling to ~1.35M lattice.
Three-layer governance
Pre-action + BAA + QEN v2.0, all production-deployed.
Hybrid quantum execution today
~30% quantum / ~70% AI in production.
Founders bootstrapped
Founder-bootstrapped to production-verified state with AWS Activate credits and zero external capital raised.
Burn discipline
$3K–$3.5K/month, founders unsalaried, sustainable indefinitely.
Intellectual Property
Architecture-level patent. Trade-secret catalog. Defensible moat.
Patent application published
Indian patent application covers the combination of quantum-enhanced deterministic multi-phase analysis, multi-framework compliance, formal verification, and bounded authority governance. PCT-eligible (filing window open). Three to five year competitive lock potential upon grant.
Trade-secret catalog
The structured CP–SP–SSP control taxonomy spanning 43 standards, 21 frameworks, and the specific PQC + classical compliance parameter mappings that drive automated framework coverage.
Copyright-protected implementation
Source code, evidence chain implementations, governance kernel internals, and deployment artifacts. All standard copyright protections apply.
Other tools tell you what they think might be wrong. SusangaAI™ tells you what it has proven is wrong — and gives you the evidence to prove it to anyone.
Get In Touch
One request. One team. Direct access.
Share the context for your request. The SusangaAI™ team will route it to the right founder-led workflow directly.
Direct Email
When forms aren't enough.
How We Operate
A globally distributed team, operating across global talent hubs.
SusangaAI™ is built across engineering, compliance, and commercial functions — coordinated asynchronously across time zones. Serious inquiries from enterprises, investors, and the press receive a response within 24 hours.
Legal & Privacy
Legal documentation in preparation.
SusangaAI™ is finalizing its formal Privacy Policy, Terms of Service, and Cookie Policy with external counsel. Interim notices are below.
Privacy
Privacy notice (interim)
SusangaAI™ does not collect tracking analytics, advertising cookies, or third-party fingerprinting on this site. Contact-form submissions are transmitted directly to SusangaAI™ through an authenticated endpoint and are used only to respond to your enquiry. A counsel-reviewed Privacy Policy will replace this interim notice on launch.
For specific data-protection requests (access, rectification, erasure, or portability), or to ask about the current draft documentation, contact contact@susangacode.io.
Terms
Terms of use (interim)
All content on this site — including written copy, diagrams, the SusangaAI™ brand, the SusangaAI™ architecture descriptions, and the visual identity — is the intellectual property of SusangaAI™ and its founders. Patent matter is identified as “Patent application published — examination pending.” No statement on this site constitutes a contract, warranty, or binding commitment. A counsel-reviewed Terms of Service will replace this interim notice on launch.
Cookies
Cookie notice (interim)
This site does not set advertising cookies and does not run third-party analytics on the SusangaAI™ brand site. Strictly-necessary technical state may be held in browser memory only for the duration of a session (for example, to remember which view you were last on). A counsel-reviewed Cookie Policy will replace this interim notice on launch.
Contact — Legal
Questions about this site, our documentation, or our IP position.
All correspondence on legal, privacy, IP, and regulatory matters goes to contact@susangacode.io. Investor-relations matters go to investor@susangacode.io.
Governance
Safety is a geometric property of execution, not a prediction about models.
SusangaAI™ governs every AI action through six layers of formally specified, mathematically verified controls. Governance here is not a policy lookup, a rule table, or a confidence threshold — it is a mathematical proof that every executed action stays inside a declared boundary. No model output reaches the external world without passing this proof.
The Problem
Probabilistic reasoning. Deterministic execution.
SusangaAI's AI engine is probabilistic. It reasons about code quality, security vulnerabilities, and regulatory compliance. But reasoning is not authority. The actions it triggers — accepting or rejecting code, generating compliance records, updating evidence chains, issuing findings — must be governed deterministically, with zero ambiguity about what is and is not permitted.
Without structural governance, probabilistic model outputs have an implicit blast radius — anything the model can reach, it can affect. SusangaAI replaces that implicit authority with a formally specified containment requirement: every action must declare its scope, the system must prove that the executed effect stays inside that scope, and unless the proof succeeds the action does not run. Where other platforms apply a rule, SusangaAI requires a proof. This is what makes the architecture defensible to regulators on first principles rather than on observed behaviour.
Other platforms ask the model to behave. SusangaAI proves that the action stays inside its declared boundary before the action is allowed to run. The difference is the difference between a policy and a proof. SusangaAI Architecture Principle
End-to-End Governance
Six layers. Every action. No exceptions.
Governance is not a single entry gate. It is a continuous control fabric woven across every external data entry point, every pipeline phase transition, every model output, every AI agent action, every compliance document, and every policy operation. At every layer the same mathematical invariant applies: the executed action must remain inside its declared boundary. A boundary exit at any layer triggers recovery — no cross-layer exception, no confidence-based override.
Every governed action passes through the relevant layers in sequence. No action bypasses the layer that applies to it. No probabilistic output overrides a containment failure at any layer.
Layer 1 — Pre-Action
Before anything runs.
Three controls fire before a single analysis phase executes.
AI Input Sanitization
Every response from an external AI tool is sanitized before it reaches the analysis engine. Known prompt injection patterns are stripped and replaced with explicit markers. External data is structurally separated from system instructions at the protocol level — the model cannot be deceived into treating external content as internal directives.
Network-Level Hardening
Every HTTP request passes through a security header layer (CSP, HSTS, X-Frame-Options), origin enforcement, and rate limiting before it reaches any API endpoint. Malformed, unauthorized, or abusive requests are blocked at the network boundary before any processing begins.
Pre-Execution Authority Gate
Before the analysis pipeline runs, the governance kernel evaluates the proposed action against the invariant registry. The gate verifies the proposed effect is well-formed, that the action carries valid cryptographic authority for the scope it claims, and that the declared scope is non-empty. Any one of these failing returns immediately — no phase executes, no result is partially assembled. The gate decision is logged regardless of outcome.
Authority in SusangaAI is not an access-control list — it is a cryptographic proof that travels with every action and can only be narrowed by downstream components, never widened. A proposed action that claims a scope wider than its authority chain allows fails verification unconditionally. Capability Authority Design
Layer 2 — In-Pipeline
Four checkpoints across the analysis pipeline.
Governance is enforced continuously during pipeline execution. Four control points fire at key phase boundaries, ensuring that a problem introduced at any phase cannot silently propagate to the result.
Fast-Path
Confidence
Quantum Health
Heavy-Path
Post-Fast-Path Gate
Fires after the initial analysis phases complete. If the fast path introduced an unsafe context, execution stops here and returns a partial result — it does not proceed to the resource-intensive phases.
Low-Confidence Finding Filter
Applied between finding generation and score calculation. Low-confidence findings matching known false-positive patterns are removed before scoring. The score is recalculated from surviving findings only. Auditor reports are never modified by this gate.
Quantum Health Gate
Governs the quantum optimization phase with multiple operational modes. Automatic fallback to classical-only if health thresholds are not met. Quantum outputs are never promoted to production without verified classical parity. Every quantum run is permanently logged.
Post-Heavy-Path Gate
Fires after the remaining analysis phases complete. Guards against a partially-corrupted result being promoted. A failure here stops promotion regardless of how many prior phases passed cleanly.
Layer 3 — Post-Action
The final decision. Fail-closed by design.
Once all pipeline phases complete, the governance kernel runs the formal admissibility check: for every invariant in the registry, the kernel evaluates whether the proposed action keeps the system inside that invariant. This is a geometric containment test, not a confidence threshold. The verdict — Admit, Advisory, or Block — is a deterministic function of this test. A single invariant exit produces a Block verdict. There is no partial admission.
The kernel evaluates multiple categories of blocking invariants plus one advisory check, with fail-closed default if the invariant registry is unavailable. An empty registry is not a pass. An unreachable registry is not a pass. The containment test requires a populated, reachable registry to return an Admit verdict.
The admissibility test admits no partial satisfaction. Either every invariant holds for the proposed action, or the action is inadmissible. An empty registry, an unreachable registry, or a single invariant exit all produce the same structural outcome — recovery executes. Fail-Closed Architecture Principle
Layer 4 — AI Agent Governance
Every agent action logged. Write-blocked at the database.
When AI agents call external tools as part of analysis workflows, every tool invocation is logged with full input, sanitized output, injection-detection status, and timestamp. The agent operates with read-only authority by construction: read isolation is enforced at the database engine level, not the application layer, making write access structurally impossible regardless of application-layer state. This provides immutable SOC 2 CC6.1 / CC7.2 evidence for every agent operation.
Immutable Agent Audit Log
Every tool call made by the AI agent is permanently recorded before the response is processed. The log is append-only and cannot be retroactively modified. Available for security, compliance, and audit purposes at any time.
Database-Level Write Block
The AI agent operates under a database account with read-only permissions — enforced at the database engine level, not the application layer. Even in the event of an agent compromise, the agent cannot write to, update, or delete any database record.
The AI agent cannot write to the database. Not because we trust the application layer to stop it — but because the database engine itself will not allow it. Structural Isolation Principle
Layer 5 — Policy & Data
Signed policies. Versioned. Rollback-safe.
Administrative operations — policy deployment, data retention, training data intake, and IDE privacy — are themselves governed artifacts, not privileged backdoors.
Signed Policy Bundles
Policies are cryptographically signed before deployment. Signature verification is mandatory before any bundle takes effect. Rollback to a previous version is available with automatic backup and a full audit trail for every deployment and rollback event.
Retention Management
Configurable retention periods per data type with cryptographically signed job execution. Each retention run produces a verifiable signature. Compliance audit artifacts are retained for a minimum of 7 years. Supported actions: delete, archive, anonymize, notify.
IDE Privacy Controls
Server-enforced policies govern IDE plugin behavior: local-only analysis mode, telemetry opt-out, and network access restrictions. GDPR-compliant data minimisation and right-to-erasure options are enforced at the device level with compliance monitoring.
Training Data IP Clearance
Every training sample requires a completed legal clearance workflow before use. Samples move through a tracked lifecycle from pending through cleared, rejected, or expired. Provenance tracking and license validation enforced for all training data.
Performance Baseline Freeze
After each release, performance baselines are captured and frozen. Deviations beyond defined thresholds trigger escalating responses. Deviations above the highest threshold trigger a formal incident review requiring sign-off before release proceeds.
Layer 6 — Compliance Output
Validated before it reaches an auditor.
Three validators sit on the export path. No compliance document is generated or delivered without passing all three.
Routing Validation
Multi-stage validation applied to model routing decisions before they affect compliance records. Routing decisions that do not meet confidence thresholds are flagged and logged before any downstream effect.
Compliance Report Validator
Applied at the export layer before compliance reports are generated. Prevents low-confidence findings from appearing as confirmed findings in auditor-facing documents. Partial or unconfirmed results are blocked from the compliance output path.
Analysis Integrity Validator
Ensures compliance report data is complete, arithmetically consistent, and fully provenanced. Validates all pipeline phases are represented, scoring rubrics are internally consistent, and provenance fields are populated for every finding.
Threat Model
Structural threats. Structural mitigations.
These are not behavioral safeguards — they are structural constraints enforced at execution time.
| Threat | Layer | How Governance Addresses It |
|---|---|---|
| Prompt injection via external tools | Layer 1 | AI responses sanitized and boundary-marked before reaching the model |
| Adversarial input / supply chain | Layers 1 & 3 | Network controls at boundary; effect bounds constrain blast radius regardless of input |
| Compliance drift | Layers 2 & 6 | Invariant re-validation at every gate; output validators block incomplete evidence |
| AI agent privilege escalation | Layer 4 | Database-level write block at the engine; every action logged before processing |
| Policy tampering | Layer 5 | Cryptographically signed bundles; signature verification mandatory before deployment |
| Silent autonomy drift | Layer 3 | Invariants checked at every promotion gate; fail-closed if registry unavailable |
Rollout Control
Advisory for pilots. Hard block for production.
Governance enforcement is configurable per tenant, enabling gradual adoption without sacrificing auditability. Both modes produce complete, immutable governance records.
Advisory Mode
Governance verification runs fully on every action. Violations are logged to the governance audit trail but do not block execution. Teams observe, investigate, and remediate before enforcement goes live.
- Pilot environments
- New tenant onboarding
- Gradual governance rollout
- Compliance baseline establishment
Hard Block Mode
Governance verification failures trigger full recovery actions — merge block, rollback, safe termination. No exceptions, no silent pass-throughs, no model-layer bypass.
- Production environments
- Regulated deployments (HIPAA, FedRAMP, PCI DSS)
- High-security repositories
- Compliance-mandated enforcement
Both modes produce complete, immutable governance records. Advisory does not mean ungoverned.
Deterministic Recovery
Five fallbacks. Atomic. Bypass-proof.
Every governance manifest specifies a recovery action. If any governance check fails, the recovery executes deterministically, atomically, and without possibility of bypass by the model layer.
Block Merge
PR or action marked as failed in CI. Merge blocked until governance passes.
For: Code changes
Rollback Commit
Reverts applied changes.
For: Auto-generated fixes exceeding declared scope
Notify Admins
Alert raised for manual review.
For: Low-risk actions needing human confirmation
Circuit Breaker
Similar actions disabled during cooldown after repeated failures.
For: Systemic violation patterns
Safe Terminate
Execution loop halted with structured error record in audit trail.
For: Runtime sandbox violations
Recovery is atomic. It cannot be partially executed. It cannot be bypassed by the model layer under any circumstances.
Regulatory Coverage
21 frameworks. 10,000+ populated controls.
The governance layer enforces invariants drawn from 21 regulatory frameworks. Every governed action is checked against the relevant framework invariants for the tenant's declared compliance context — as a runtime enforcement condition, not a post-hoc report. Full coverage details are available on the Compliance page.
Performance & Audit
Governance that does not cost you throughput.
Governance enforcement runs within tight latency bounds at every layer. Every governance decision is logged to an immutable, append-only audit trail. Auditors can reconstruct the complete authorization history for any action — which layer decided, which invariants were checked, what verdict was returned, and what recovery was triggered.
- Every gate decision logged — immutable, append-only
- Full manifest metadata and context in every record
- 7-year retention for compliance audit artifacts
- Byte-identical analysis outputs across runs under enforcement
Architectural Honesty
What this architecture does. And what it does not.
We build for what can be formally specified and enforced. Everything beyond that boundary is labelled clearly.
What it enforces
- Every action is structurally constrained in what it can change — bounded at execution time, not estimated
- Deterministic recovery triggers on every governance failure without exception
- Every governance decision is logged to a tamper-evident, immutable audit trail
- Fail-closed semantics: the system never silently passes with unavailable governance
What it does not claim
- Does not guarantee semantic correctness of model outputs
- Does not eliminate long-horizon cumulative risk
- Does not solve open-world completeness
- Invariant coverage is formally defined and deliberately finite — not unbounded
We do not claim governance solves everything. We claim it makes the boundaries visible, verifiable, and impossible to cross silently. SusangaAI Governance Philosophy
Governance is not a feature. It is the substrate.
Speak with our technical team about deploying SusangaAI™ governance in your environment.