AI QUALITY COMMAND CENTER · v0 Demo April 2026
SYNCA — AI Quality Command Center
Enterprise software teams lose 2–4 days per sprint to regression cycles. Every code change is a blind bet: unknown dependencies, unknown impact. Add AI-generated code — and you add untracked semantic risk.
2–4 days
Regression cycle per sprint — legacy Java/COBOL enterprise
JP/KR enterprise avg. — legacy system complexity (10–20yr codebases)
Every team pays this. Every sprint. Regardless of how small the change.
15–25%
AI-generated patches that pass all tests but are semantically wrong
Source: Xia et al. APR survey 2023, confirmed on QuixBugs & Defects4J benchmarks
Pass tests. Break behavior. Surface only in production incidents.
3 → 1
Tools needed today for CIA + APR + Validation — SYNCA unifies all three
Market survey Apr 2026: no single product delivers this combination
No competitor combines all three in one session. SYNCA closes the integration gap.
2025
Japan DX government deadline — enterprise software budgets now open
METI DX Report 2023 + Japan Government Digital Agency DX 2025 policy
Regulatory pressure + AI maturity + zero commercial APR SaaS = 12–18 month entry window
3 Core Value Propositions — JP Enterprise
CIA
Change impact analysis — legacy codebases fly blind
JP legacy Java/COBOL systems (10–20yr): one module change breaks 3–8 downstream modules on average. Regression test cycle: 2–4 days/sprint — every time, every team.
Competitor: Lattix/NDepend $3K–15K/seat/yr, batch-mode only, no AI, no real-time feedback
SYNCA BFS on live call graph: blast radius in <1s, feeds directly into APR pipeline
SEMANTIC GUARD
AI patches that pass tests but break semantics
15–25% of APR/AI-generated patches pass the full test suite but contain semantic errors: logic inversions, hidden state mutations, boundary violations. Static tests cannot catch this category.
Source: Xia et al. APR survey 2023, confirmed on QuixBugs & Defects4J benchmarks
SYNCA Patch Validator: invariant engine catches semantic overfitting — no competing product offers this (as of Mar 2026)
COMPLIANCE
METI 2024 audit trail — hard procurement gate
METI AI governance guidelines (Sep 2024) require logging: model ID, input hash, confidence score, human override decision — per AI action in critical systems. Zero existing tools cover all required fields.
Also applies: ISO 27001:2022 + DORA for financial systems. Without this log, AI adoption in critical systems cannot expand.
SYNCA write-once append log stores all required fields + PDF export in METI format. Zero config.
WHY NOW
3 triggers converging in Q1–Q2 2026
① TECHClaude Opus 4.6 → 80.8% SWE-Bench (Apr 2026): production APR is viable for the first time
② MARKET0 commercial APR SaaS products — 12–18 month window before Azure/AWS ship competing features
③ REGULATIONJapan DX 2025 deadline + METI AI guidelines = enterprise budgets unlocked and compliance pressure real
TRINITY
Fabbi AI Trinity
SYNCA is the link that transforms Trinity from "3 AI products" into "1 AI software factory pipeline."
FARE
Reverse Engineering
How does the running system work? → Architecture spec, domain model
→
spec + docs
spec + docs
NEXA
Code Generation
How to develop / modernize faster? → Generated / refactored code
→
generated code
generated code
SYNCA
Quality Gate
Is the generated code correct and safe? → Validated patches, audit log
SYNCA in the Software Lifecycle
| SDLC Phase | Pain | SYNCA Role | Why This Phase |
|---|
MARKET PAIN
Market Pain — JP Enterprise
COMPETITION
Competitor Matrix
⚠️ Amazon CodeGuru is the closest competitor — CIA + code review for Java/Python, native AWS JP region. Must be addressed in any JP enterprise pitch.
Why SYNCA Wins
PRODUCT
Modules & Actors
Actors (8)
Module Status (16 Modules)
ROADMAP
Product Roadmap v0 → v3
Milestones
STATUS
Current Status — April 2026
Feature Progress
E2E Compliance Note
~72% feature completion ≠ E2E compliance. Critical path: TIP-109 (CIA API) → TIP-110 (APR endpoint) → TIP-111 (Patch Validator) → TIP-114 (Demo UI). E2E ~20%.
ARCHITECTURE
7-Layer Architecture
Data Flow
HITL Loop — Human-in-the-Loop
Approve
Confidence ≥ 85%. Patch auto-suggested. Engineer reviews diff + audit entry, then approves merge.
Reject
Confidence < 85% or invariant failed. Rejection reason feeds back into APR prompt for next iteration.
Modify
Engineer edits patch in code viewer. Modified patch re-runs through Patch Validator before merge.
DEEP TECH
7 Cutting-Edge Technologies
RISKS
Risk Register
CTO REVIEW
JP Enterprise CTO Assessment
💬 Persona: CTO of a 500-person JP financial software firm. 15 years enterprise Java. Has rejected 3 AI vendor proposals this year.