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INOD

InnodataD
Nasdaq / Commercial & Professional Services
Last Price
At close
2026-07-20
View Chart
Current thesis
Primary-source support is strong for the current thesis: Q1 was a material beat, guidance was raised to approximately 40% or more growth, cash rose to $117.4 million, and the June 17 filing reaffirmed that outlook, so the bull case is that AI-data demand and customer diversification are still underappreciated [#SEC-8K-2026-05-07] [#SEC-8K-2026-06-17].
Posture
Defensive
Lead driver
Sentiment
What changed
Sentiment remains the lead driver in the composite, 7D delta +9.1.
What can break
Customer concentration remains a live risk even though management highlighted diversification, because a leading Big Tech engagement is expected to contribute approximately $51 million in 2026 revenue [#SEC-8K-2026-05-07].
Momentum
5
Value
46
Sentiment
65
Setup hits (3d)
0 · Net Neutral
AI TargetsBase $76.00 · Bull $96.00 · Bear $48.00
Data freshness
Prices
As of 2026-07-20
Fundamentals
As of 2026-07-20 • Vendor: Data Vendor v1
Scores
As of 2026-07-20 • Model: HYBRID_IC_RP
AI Memo
As of 2026-07-03 • Model: RankAlpha Sentiment Codex
Investment thesis
As of 2026-07-20
Supporting evidence
What
Grade D · Defensive
Confidence Medium · Net Neutral
Target $122.75
Why
Momentum5 · Δ7d -4.5
Value46 · Δ7d +0.6
Sentiment65 · Δ7d +9.1
So what
Weak posture (Net Neutral). Prioritize risk control and patience.
Lead driver: Sentiment · See AI snapshot
Momentum
5
26% active weight
Current posture
7d trendFlat
Δ7d
-4.5
Δ21d
-92.8
Value
46
39% active weight
Current posture
7d trendFlat
Δ7d
+0.6
Δ21d
+1.1
Sentiment
65
34% active weight
Current posture
7d trendImproving
Δ7d
+9.1
Δ21d
-10.4
Why this grade

Composite grade D. Momentum 4.6 / Value 45.7 / Sentiment 64.6

Fundamentals (TTM)
As of 2026-07-20
Market Cap
$2.03B
Beta
2.42
Shares Out
32.66M
P/E (TTM)
61.6
P/S (TTM)
8.29
P/FCF (TTM)
48.55
Rev YoY
+54.4%
EPS YoY
+88.0%
Gross Margin
+40.7%
Op Margin
+17.4%
Net Debt
-$77.8M
Current Ratio
2.48
As of 2026-07-20 • Updated nightlySource: Internal modelMethodology