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AgileThought, Inc (AGILQ) Fair Value & Analysis

Technology · US · Market cap $5.2K

AI AgileThought, Inc logo AgileThought, Inc AGILQ · US
Price$0.0001
Fair Value$0.0001
Upside+0.0%
Quality48/100
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Evidence: Low Range $0.0001 – $0.0001

Fair value as of: Jun 26, 2026

From 2 valuation models · updated 7 days ago

Fair value updated Jun 26, 2026 — revised from $4.58 to $0.0001 (−100.0%) since Jun 24, 2026.

Price vs Fair Value (12 months)

$0.0002 $0.0001 Fair Value $0.0001 Jul 2025 Jul 2026

12‑month range $0.0001 – $0.0002 · the $0.0001 price screens below the $0.0001 fair value. As of Jun 26, 2026.

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Analysis

AgileThought, Inc (AGILQ) currently trades at $0.0001, while our model-based Fair Value estimate is $0.0001 — implying the stock looks roughly 0.0% undervalued today. We read business quality at 48/100 (below-average quality), in the Technology sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: low) — always confirm before acting.

Over the trailing twelve months, AgileThought, Inc generated revenue of $167M at a net margin of -41.2%. Revenue declined 17.0% year over year. Net debt stands at $68.1M. Fundamentals as of Jun 26, 2026

Key figures & financial health

Revenue (TTM) $167M
Revenue growth (YoY) -17.0%
Net margin -41.2%
Return on equity -118%
Free cash flow −$9.3M FY2022
Operating margin -18.6%
More key figures
EPS (TTM) $-1.44
Net debt $68.1M FY2022

Figures from reported company fundamentals (EODHD) · as of Jun 26, 2026. TTM = trailing twelve months.

About the company

AgileThought, Inc. provides digital transformation services in the United States and Latin America. It provides organizational transformations, training and certifications, and product management services. The company offers application engineering and DEVOPS, cloud architecture and migration, data analytics, automation, application modernization, UX/UI design, artificial intelligence, and machine learning services. In addition, it offers lifecycle management support and multi cloud services. The company serves professional services, healthcare, pharmacy, financial services, energy, and utilities industries. AgileThought, Inc. was founded in 2000 and is headquartered in Tampa, Florida.

Revenue & earnings trend

FY2020 – FY2022 · reported fiscal years

AgileThought, Inc reported revenue of $177M in FY2022 versus $164M in FY2020, a compound +3.8%/yr. Reported net income was −$20.2M in FY2022.

Revenue +3.8%/yr
FY20 $164M
FY21 $159M
FY22 $177M
Net income
FY20 −$26.2M
FY21 −$20.1M
FY22 −$20.2M

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Frequently asked questions

Is AgileThought, Inc (AGILQ) undervalued?
As of Jun 26, 2026, our model estimates a fair value of $0.0001 versus a price of $0.0001 — about +0% (undervalued). Model-based estimate, not financial advice.
What is the fair value of AGILQ?
Our model-based fair value for AgileThought, Inc is $0.0001 (as of Jun 26, 2026), built from audited fundamentals. The current price is $0.0001.
What is the quality score of AGILQ?
AgileThought, Inc has a Quality Score of 48/100, measuring profitability, growth and balance-sheet strength from non-valuation factors.
What is the revenue of AgileThought, Inc (AGILQ)?
AgileThought, Inc reported trailing-twelve-month revenue of about $167M (latest available figure, as of Jun 26, 2026).
What is the net profit margin of AGILQ?
The net profit margin of AgileThought, Inc is about -41.2%, meaning it is currently running at a net loss. Based on the latest reported figures.

How we calculate Fair Value

Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.

Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.