Draft:Ensis AI
Submission declined on 2 July 2025 by Jlwoodwa (talk).
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
| ![]() |
Company type | Private |
---|---|
Industry | Software · Artificial intelligence |
Founded | 2023 |
Founders | Ben Lewis; Jon Sockell |
Key people | Ben Lewis (CEO); Jon Sockell (COO) |
Products | Proposal-management software (web, Microsoft Word & Excel add-ins) |
Website | www |
Ensis AI is an American software company that develops artificial-intelligence (AI) tools for automating proposal and questionnaire creation. Its cloud platform and Microsoft Office add-ins use large-language-model techniques to “shred’’ requests for proposals (RFPs), generate first-draft responses, and track requirement compliance for enterprise and government contractors.[1]
History
[edit]The company was founded in 2023 by former proposal consultants Ben Lewis and Jon Sockell.[2] In March 2024 Ensis secured a US$4 million seed round led by NextGen Venture Partners and Tau Ventures, with participation from Grep VC, Innovation Global and Gaingels.[1] [3]
Products and technology
[edit]Ensis AI’s flagship product combines retrieval-augmented generation with a private knowledge base that customers host in their own cloud environments. The system extracts structured requirements from RFPs, RFIs and due-diligence questionnaires, matches them to previously vetted answers, and produces draft narratives that can be revised inside the Ensis web app or directly within Word and Excel via Microsoft add-ins.[4] According to the Association of Proposal Management Professionals (APMP), the software is positioned as “the only proposal AI that works where you do”, emphasizing integration over standalone workflows.[5]
Ensis states that customer data are siloed per tenant and can be deployed in customers’ own Azure or AWS environments, a feature aimed at large U.S. government contractors that must meet strict security requirements.[6]
Reception
[edit]Trade publications have highlighted Ensis as part of a wave of generative-AI vendors targeting the “GovCon” (government contracting) sector.[2] Review site Futurepedia described the product as “indispensable” for organisations seeking to automate and personalise proposal responses.[7]
Funding
[edit]- Seed – US$4 million (March 2024): NextGen Venture Partners, Tau Ventures, Grep VC, Innovation Global, Gaingels.[1]
See also
[edit]- Proposal software
- Generative artificial intelligence
References
[edit]- ^ a b c "Ensis Raises $4M in Seed Funding". FinSMEs. 28 March 2024. Retrieved 2 July 2025.
- ^ a b Lewis, Ben (22 April 2024). "Ensis". CEOCFO Magazine. Retrieved 2 July 2025.
{{cite web}}
: Italic or bold markup not allowed in:|website=
(help) - ^ "Ensis Raises $4M in Seed Funding to Revolutionize Government Proposal Writing with AI". Feed the AI. 29 March 2024. Retrieved 2 July 2025.
- ^ "Ensis – Microsoft 365 App Certification". Microsoft Learn. April 2025. Retrieved 2 July 2025.
- ^ "RFP and Proposal Software". APMP. Retrieved 2 July 2025.
- ^ "Platform Overview". Ensis AI. Retrieved 2 July 2025.
- ^ "Ensis AI Reviews: Use Cases, Pricing & Alternatives". Futurepedia. Retrieved 2 July 2025.
External links
[edit]Category:Artificial intelligence companies of the United States Category:Software companies based in the San Francisco Bay Area Category:Software companies established in 2023 Category:American companies established in 2023 ```
- Promotional tone, editorializing and other words to watch
- Vague, generic, and speculative statements extrapolated from similar subjects
- Essay-like writing
- Hallucinations (plausible-sounding, but false information) and non-existent references
- Close paraphrasing
Please address these issues. The best way to do it is usually to read reliable sources and summarize them, instead of using a large language model. See our help page on large language models.