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AIXI

Xiao-IC
Nasdaq / Software & Services
Last Price
At close
2026-06-02
View Chart
Current thesis
The best bull read is that the company did complete the delayed filing within the extension window, ended 2025 with higher cash than 2024, retains a sizable patent estate, and still has litigation/IP optionality after the Apple-validity ruling. If financing pressure eases and IP-backed enterprise AI commercialization improves, the stock can remain highly reflexive from a very small market cap.
Posture
Mixed
Lead driver
Momentum
What changed
Momentum remains the lead driver in the composite, 7D delta -6.2.
What can break
Customer concentration is meaningful: the top three customers represented 31.9%, 14.8%, and 11.8% of 2025 revenue, increasing execution risk if any program slips. [#20F-2026-05-15]
Momentum
87
Value
35
Sentiment
36
Setup hits (3d)
0 · Net Neutral
AI TargetsBase $10.00 · Bull $16.00 · Bear $6.00
Data freshness
Prices
As of 2026-06-02
Fundamentals
As of 2026-06-01 • Vendor: Data Vendor v1
Scores
As of 2026-06-02 • Model: HYBRID_IC_RP
AI Memo
As of 2026-05-16 • Model: RankAlpha Sentiment Codex
Investment thesis
As of 2026-06-02
Supporting evidence
What
Grade C · Mixed
Confidence Medium · Net Neutral
Target $21.60
Why
Momentum87 · Δ7d -6.2
Value35 · Δ7d -
Sentiment36 · Δ7d +17.4
So what
Balanced signals (Net Neutral). Wait for confirmation before sizing up.
Lead driver: Momentum · See technicals
Momentum
87
34% active weight
Current posture
7d trendSoftening
Δ7d
-6.2
Δ21d
-11.5
Value
35
32% active weight
Current posture
7d trendTrend unavailable
Trend unavailable
Δ7d
-
Δ21d
-
Sentiment
36
33% active weight
Current posture
7d trendImproving
Δ7d
+17.4
Δ21d
-
Why this grade

Composite grade C. Momentum 87.0 / Value 35.0 / Sentiment 36.0

Fundamentals (TTM)
As of 2026-06-01
Market Cap
$11.6M
Beta
-
Shares Out
31.95M
P/E (TTM) · Derived
-0.2
P/S (TTM)
0.08
P/FCF (TTM)
-
Rev YoY
+14.3%
EPS YoY
+125.3%
Gross Margin
+68.3%
Op Margin
-18.3%
Net Debt
$43.48M
Current Ratio
0.88
As of 2026-06-02 • Updated nightlySource: Internal modelMethodology