ContractAI (App Orchid): Vendor Analysis — Solution overview for contract analytics and negotiation, ContractAI competitors, roadmap, customer feedback, analyst insights
02/03/2021
This Spend Matters PRO Vendor Analysis provides an overview of ContractAI and its solution for long-tail negotiations.
To say that artificial intelligence has become a hot topic in contract management would be an understatement. Whereas even two years ago AI was more of an aspirational goal among contract lifecycle management (CLM) vendors, today many of the best-known technology providers have either internally developed or partnered to bring some set of AI capabilities into their solutions — whether simply to accelerate contract review or to more deeply understand the semantic nuances of their previously executed agreements.
ContractAI, however, comes to the CLM world from the opposite path.
It is a standalone product built on an enterprise AI platform (App Orchid), meaning its foundations lie in engines for unstructured data processing, knowledge graph construction and the like, rather than in CLM workflow management or facilitating negotiation version control. This has allowed ContractAI, in a comparatively short period, to develop a competitive solution for legacy contract analysis and supplier negotiation — with an analytical depth that some of its peers may envy.
Of course, ContractAI is also a young vendor, and one currently approaching CLM from a set of highly tailored use cases. Those best-fit cases — for procurement, for enterprise-wide CLM deployments, and for certain steps in the contract management lifecycle — are what we will explore in this Vendor Analysis.
We also will delve into the concept behind ContractAI and look at its platform, application and supporting services. This post also has a verified customer reference analysis, a competitive market analysis and key analyst takeaways.
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EPRO02/08/2017
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EPRO08/26/2020
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CLM03/03/2021
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EPRO P2P01/26/2018
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AP/I2P EPRO06/07/2019
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EPRO02/08/2017
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EPRO08/26/2020
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CLM03/03/2021
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EPRO P2P01/26/2018
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AP/I2P EPRO06/07/2019