Every value in a quote or contract already exists in your CRM. Copying it into a Word file by hand costs time and introduces the errors that surface, expensively, after signature. We implement generation that produces the document from the record — and the tool selection that decides whether Docusign Gen is even the right engine for it.
Document generation produces a finished document by merging data from a system of record into an approved template, applying conditional logic so the content matches the transaction. Docusign offers it in two places: Docusign Gen generates documents inside Salesforce from record data, ready to send for signature; Docusign CLM generates contracts from its own template and clause library as part of the contract lifecycle.
Two licensing facts worth knowing before a procurement conversation. Docusign Gen does not include electronic signature — signature is licensed separately through eSignature. And Docusign Negotiate, which handles redlining and version comparison, does not come with a Gen purchase either. These are separate products that work together. Discovering that after budget approval is a common and avoidable annoyance.
Generation engines differ sharply in what they can express, and the gap only becomes visible on the hard documents. A one-page order form merges cleanly in almost anything. An invoice with conditional line-item tables, currency arithmetic, related records several levels deep and sections that suppress entirely when empty does not.
We have implemented generation on Docusign Gen, on Conga Composer and on Nintex, and the honest answer is that the right engine depends on your hardest document rather than your most common one. The comparison below is the one we use.
We implement all three and have no incentive to steer you. The differences that matter are document complexity, who maintains the templates afterwards, and whether generation is a step in a wider contract lifecycle.
| Docusign Gen | Conga Composer | Nintex | |
|---|---|---|---|
| Best fit | Sales documents generated in Salesforce and sent straight for signature through Docusign. | Complex, heavily conditional documents, and organisations already invested in the Conga suite. | Generation as one step inside a broader process-automation estate. |
| Document complexity | Handles standard merge and conditional sections well. Straightforward to configure. | The strongest of the three on conditional content, nested tables and calculated values — including arithmetic the others cannot perform at generation time. | Strong, with the template experience familiar to Word-centric authors. |
| Related data depth | Suits documents built from a record and its immediate children. | Query-driven, reaching several levels of related objects — the usual reason enterprises land here. | Good, driven by configured data sources. |
| Signature path | Native to Docusign eSignature, licensed separately. | Pairs with Conga Sign or Docusign; we wire generated documents into either. | Pairs with Docusign or another provider. |
| Who maintains templates | A capable Salesforce administrator, after training. | Realistically a specialist. Conga rewards investment and punishes casual maintenance. | A business author for content; configuration needs an owner. |
| Main risk | Outgrowing it when documents become genuinely complex. | Brittle queries and templates that break on edge cases when nobody owns them. | Becoming one more platform to govern if the automation estate is not already there. |
A note on the most common engagement we get in this area: it is not a new implementation. It is a Conga or Gen deployment that was built once, adopted by nobody, and has sat unused because the original queries were fragile, the templates broke on cases the author did not anticipate, or only one document type was ever built. That is a fixable state and rarely requires changing tool.
Generation exposes every gap in the data model. Fields that were optional because a human filled the gap become mandatory; values held as free text need structure. This is usually the first third of the work and it is CRM work, not template work.
Templates built so a clause appears when the transaction warrants it and vanishes cleanly when it does not — without leaving an orphan heading or an empty table. Suppression behaviour is where most template defects actually live.
Product tables that group, sort and subtotal correctly, handle a single line and a hundred, and paginate without orphaning a row from its header. Currency and rounding decided explicitly rather than inherited from whatever the engine does by default.
Where the finished document goes — filed to the record, pushed to the document store under the naming convention, and passed into the signature envelope without a human downloading and re-uploading it.
Where the same agreement is issued by several entities or in several languages, variants are handled through logic and shared components rather than through parallel template families that drift apart within a year.
The failure mode for generation is a build nobody can change. Templates are documented, the data dependencies are written down, and your team is trained to make the routine edits — because they will be asked for within a month of go-live.
Tool selection is decided by the hardest document in scope, not the most frequent one. Send us that document — the one with the conditional schedules, the nested tables and the clause that only applies in two jurisdictions — and we will tell you which engine handles it and what the template work involves.
If you already own a generation tool that is not being used, that is a different and usually cheaper conversation. We do a lot of those.
