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Docusign and Elastic are addressing different layers of the same enterprise problem: important information remains trapped in contracts and other unstructured documents. Docusign’s Intelligent Agreement Management strategy aims to turn agreements into structured, actionable business data. Elastic provides search and retrieval infrastructure—combining keyword, BM25, vector, filtered and hybrid methods—that can supply reliable context to generative-AI applications.
Those directions were discussed at VentureBeat Transform 2024, but the event coverage does not announce a new joint Docusign–Elastic product. Docusign is also presented by Elastic as a customer using Elasticsearch for e-signature search. The practical connection is therefore complementary, not evidence of a generally available combined platform.
What happened at VB Transform 2024
At VentureBeat Transform in San Francisco on July 11, 2024, Elastic CEO Ash Kulkarni and Docusign Chief Product Officer Dmitri Krakovsky discussed enterprise search, generative AI, contracts, agents, security, model choice and inference cost. VentureBeat published its account on July 13, 2024: event coverage of the discussion.
That distinction matters. The source is conference reporting and executive commentary, not a formal announcement of a Docusign-Elastic integration, partnership or combined commercial offering.
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- Understand how contract provisions work
- Adapt reliable drafting precedents
- Avoid drafting errors, omissions, and ambiguities
- Make contracts more user-friendly
- Build flexibility into contracts without compromising precision
Why contracts are unusually difficult for generative AI
Electronic signatures make execution digital, but they do not automatically make agreement information usable. Contracts commonly arrive as PDFs, scans or semi-structured files distributed among legal, procurement, sales, finance and outside vendors. The same obligation may be expressed differently across agreements, amendments and order forms.
A useful system must connect language to business facts: parties, prices, renewal dates, notice periods, service levels, territories, exceptions and approval responsibilities. It must also preserve which version is effective and who is allowed to see it.
- Exact wording matters: a negation or defined term can reverse the practical meaning of a clause.
- Important information may be in scanned exhibits, tables or attachments.
- An amendment can override the original agreement, while a draft must not be treated as signed.
- Questions often span several documents, such as a master agreement, statement of work and data-processing addendum.
Docusign’s Intelligent Agreement Management approach
Docusign’s stated vision extends beyond signing. Its Intelligent Agreement Management (IAM) model treats an agreement as data that can support preparation, negotiation, execution and post-signature work. The 2024 discussion identified three components:
Maestro
Workflow and orchestration capabilities intended to move information and approvals through business processes.
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Navigator
Agreement intelligence and search for finding contract content and related information.
App Center
Connections to surrounding applications and services.
In a typical lifecycle, the scope runs from templates and data collection to redlining and approvals, electronic signature, obligation tracking, renewals, compliance monitoring and cross-contract analysis. Docusign’s current contract-lifecycle-management product scope is described at Docusign CLM; names, editions and included AI features can change, so buyers should confirm the current package.
The longer-term vision discussed at the event included contract insights, ambiguity detection, workflow recommendations and AI assistance during negotiation. That is an emerging direction, not proof that autonomous, legally reliable negotiation is generally available.
What Elastic contributes to enterprise search and RAG
Elastic addresses the retrieval layer. Its current enterprise-search positioning covers structured and unstructured data, text and vector search, analytics and AI applications: Elastic enterprise search.
Lexical retrieval
Keyword search and BM25 ranking are valuable for exact contract numbers, clause labels, defined terms, names and rare legal phrases.
Semantic and vector retrieval
Embeddings represent text numerically so a query can find conceptually similar wording even when it does not use the same terms.
Hybrid retrieval
Hybrid search combines lexical precision with semantic recall. Filters and facets can narrow results by supplier, agreement type, region, date or status. Reranking can reorder an initial result set before an answer is generated.
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In a RAG system, retrieved passages are supplied to a language model as context. The model can then summarize, compare or answer questions using enterprise material rather than relying only on its training data. Authorization filters must be applied before generation, not merely hidden in the user interface.
Elastic also documents semantic-text capabilities at its semantic-text reference. Deployment, model and feature availability vary by edition and architecture.
How the two directions fit together
A conceptual contract-AI architecture could look like this:
- Ingest: Import executed agreements, drafts, amendments, PDFs, Word files and metadata.
- Process: OCR scans and extract clauses, dates, parties, amounts, obligations and relationships.
- Normalize: Map inconsistent language to shared fields and business concepts.
- Index: Store text, metadata, embeddings, version information and permissions.
- Retrieve: Apply exact, semantic, hybrid, filtered and reranked searches.
- Generate: Provide selected passages to a model for comparison, summarization or question answering.
- Act: Route approvals, create alerts, update systems or request human review.
- Audit: Retain source passages, user identity, permissions, model and prompt details, and the final decision.
This is an analytical architecture, not a claim that Docusign and Elastic jointly deliver every step as one product.
The reported business case—and what it does not prove
VentureBeat reported a Docusign executive’s example involving roughly 70 system-integrator contracts with inconsistent terms. Analysis reportedly identified more than $100 million in savings. The customer was unnamed, and the account was not an independently audited case study.
The figure should therefore be treated as an attributed example, not a forecast. The report does not disclose the baseline, time period, implementation cost, attribution method or how much resulted from AI rather than procurement action, renegotiation or other interventions.
Other examples mentioned
- Cisco was described as using Elastic technology to improve internal customer-support processes and automate work previously handled by multiple engineers.
- An unnamed Fortune 100 bank was described as changing how wealth managers interact with clients.
- Elastic’s current enterprise-search page features Docusign as a customer and says it powers millions of e-signature searches daily with Elasticsearch. That is an Elastic customer-reference claim, not evidence of a new joint product.
What can go wrong
Contract interpretation failures
- Negation or exception errors, such as confusing “may not terminate” with “may terminate.”
- Scope mistakes involving a subsidiary, geography, product or order form.
- Mixing superseded drafts, signed versions and amendments.
- Ignoring special definitions, tables, schedules or cross-document dependencies.
- Incorrect date arithmetic for notice and renewal windows.
Search and RAG failures
- Vector-only retrieval misses exact identifiers or defined terms; keyword-only retrieval misses paraphrases.
- Poor chunking separates a condition from its exception or a definition from its use.
- Stale indexes omit recently signed or amended agreements.
- Weak authorization controls expose snippets or context from restricted documents.
- A fluent model combines clauses from different contracts without showing evidence.
- Large retrieval windows and repeated model calls create unpredictable inference costs.
Mitigations include clause-level citations, version and amendment tracking, structured fields, pre-generation authorization filters, curated evaluation questions, human approval for legal or financial decisions, and logs covering retrieval, prompts, model versions and actions.
Choosing a packaged CLM system or a search foundation
| Buying problem | Likely fit | Principal trade-off |
|---|---|---|
| Agreement preparation, approvals, signing, obligations and renewals in a governed workflow | Docusign CLM/IAM | Packaged process coverage, but migration, integration and change management are still required |
| Custom search or RAG across contracts plus support, product or knowledge data | Elastic/Elasticsearch | Flexible retrieval and deployment, but the buyer must build ingestion, taxonomy, evaluation, security and workflow controls |
| Only occasional electronic signatures | A simpler e-signature or document-management service | Full CLM may add unnecessary cost and process complexity |
Docusign is generally the more direct fit when the central problem is agreement lifecycle management. Elastic is the more natural foundation when engineers need a reusable search and RAG layer across many data sources. A combined design should be considered only after confirming data flows, identity boundaries, integration support and commercial terms.
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Evaluation checklist for buyers
For Docusign CLM or IAM
- Agreement volume, complexity, repositories and metadata quality.
- Need for clause extraction, obligations, renewals, compliance alerts and cross-contract analysis.
- CRM, ERP, procurement, storage, identity and workflow integrations.
- Data residency, sector requirements and human-review controls.
- Measures for cycle time, leakage, missed renewals, risk and verified savings.
- Which AI features are included in the selected edition and which cost extra.
For Elastic
- Corpus size, ingestion rate, latency and existing Elasticsearch skills.
- Hybrid retrieval, facets, document- or field-level permissions and reranking needs.
- Embedding model hosting, cloud, serverless, hosted or self-managed deployment.
- Observability for retrieval quality, hallucinations, latency and token use.
- Indexing, storage, compute, inference, backup, retention and egress costs.
Elastic offers several deployment paths and trial entry points, but there is no dependable single flat price. Consult Elastic pricing for a dated plan and region.
Alternatives to compare
For contract lifecycle management, evaluation candidates include Icertis, Ironclad, Agiloft, Conga CLM and Sirion. Microsoft-oriented organizations may combine SharePoint, Purview, Power Automate, Azure AI Search and Azure OpenAI. For search and RAG, alternatives include OpenSearch, Azure AI Search, Amazon OpenSearch Service, Google Vertex AI Search, Pinecone, Weaviate, Milvus and PostgreSQL with vector extensions. These are comparison options, not rankings; current features, integrations and prices require separate verification.
Bottom line
The important idea is not that generative AI replaces contract professionals or search engineers. Docusign is pursuing agreement intelligence and controlled lifecycle workflows; Elastic supplies a flexible way to retrieve enterprise context for search and AI applications. Reliable outcomes depend on clean documents, hybrid retrieval, strict permissions, version logic, evaluation and human approval. The 2024 conversation supports that complementary interpretation—not a claim of a newly launched joint platform or autonomous contract negotiation.
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