Generative AI is rapidly transforming work. With tools like ChatGPT and others specifically designed to write copy, develop code, create graphics, and do hundreds of other tasks, teams across industries and disciplines are now accomplishing more with less and boosting efficiency and productivity without increasing budget. One group that has mostly been left out of the loop is RevOps.
Unlike other teams that are free to use whatever public model they want without risk, RevOps is bound by its obligation to protect the sensitive customer data under its charge. It is also typically saddled with a complex technology infrastructure that includes a hundred or more individual applications and software that are not easily integrated with a generative AI tool without heavy IT involvement or investment.
To fill the void, many data providers have jumped into the market with quick fixes that use "copilots" or a similar tool within their user interface. Unfortunately, these solutions offer little more than window dressing, as the models aren't optimized for your particular RevOps context and requirements, and the results require additional coding and/or processing to be integrated into an operations workstream.
Up to now, if RevOps wanted to fully leverage generative AI in their workstream, they either had to get clearance from their infosec to connect to a public service via API (and risk exposing sensitive customer data to the public) or tap into an existing secure network service already embedded into their company's architecture. Even if you are one of the lucky few to have one of these options, neither is a slam dunk, as both solutions still need to be securely integrated into your RevOps stack and buffered with controls to limit access and constrain costs.
What AI agents are already doing for RevOps teams
The case for bringing AI agents into RevOps isn't theoretical anymore. According to Openprise's Executive Imperative for AI report, companies that have embedded AI into their revenue operations workflows are seeing measurable, verifiable results — not just productivity gains, but pipeline and conversion impact:
- 100% improvement in inquiry-to-open-opportunity conversion rate — CrowdStrike, using AI-based behavior scoring
- 98%+ accuracy in persona assignments using an AI-based LLM for segmentation — eliminating manual maintenance and enabling precise global targeting
- 90%+ of all leads successfully classified by AI at Denodo, resolving complex job title exceptions that older methods consistently missed
These aren't edge cases. They're what happens when AI agents for RevOps are given clean data to work with, embedded into existing workflows, and governed by the right controls. The gap between teams seeing results like these and teams still stuck in pilot mode almost always comes down to infrastructure — not model quality.
Introducing AI-Agent Factory
Openprise has a long history of securely and seamlessly making tech easy for Ops. The launch of the platform's new AI-Agent Factory builds on that success. While finally giving RevOps the green light to build with AI is an extraordinary breakthrough, the launch is just another example of how we provide operations with the most powerful tools available in a simple, secure way any non-technical user or builder can easily take advantage of.
Like all Openprise RDA Cloud capabilities, AI-Agent Factory is embedded into the platform and can be injected into any existing RevOps process or use case instead of creating new processes. Prebuilt task templates and prompts make it easy to quickly build a job for any process or purpose, whether it involves data quality, multi-vendor data enrichment (MVE), data orchestration, funnel automation, account administration, data engagement, analytics, or attribution.
Secure integration and guardrails ensure compliance with even the most stringent security and compliance requirements while restricting usage and access to limit the negative impact on your budget. Adding AI-Agent Factory to your workstream is completely turnkey, as it doesn't require you to ever worry about the underlying infrastructure.
Before we dive more into how it works, let's take a deeper look at the technology and features that make it distinct from the "copilot" solutions common in today's market.
Secure and compliant
Hosted models: AI models are hosted within the Openprise platform, ensuring that sensitive customer data never leaves the secure environment.
Data control: Enterprises retain full control over their data, mitigating the risk of unauthorized access or exposure. Openprise never uses customer data to train its own models.
Policy guardrails: Openprise implements robust policy guardrails to govern data usage and prevent any breaches of compliance regulations.
Embedded
AI-Agent templates: Pre-built templates are available for a wide range of RevOps use cases, streamlining the process of integrating AI into existing workflows. These templates provide the same functionality as the OpenAI interface, allowing users to automate any task that can be performed manually.
Model selection: Users can choose from various AI models, including Openprise's embedded model, to suit their specific needs.
Vertical integration: Openprise integrates with core RevOps processes, ensuring a cohesive and efficient operational flow.
Turnkey
Pre-built AI stack: Openprise provides a comprehensive AI stack that eliminates the need for complex configurations or managing infrastructure.
No additional licenses: Users can access the full range of AI capabilities without incurring extra costs for software licenses.
Data automation: Openprise automates the end-to-end data process, from preparation and quality checks to transformation, operationalization, and democratization.
Why AI agents and RevOps need AI orchestration
AI-Agent Factory gives RevOps teams the ability to build and deploy agents — but building an agent is only half the equation. The other half is making sure it operates reliably, securely, and within boundaries your infosec team will actually approve.
That's where AI orchestration comes in. AI orchestration is the governance layer that sits above individual agents — controlling which data they can access, enforcing usage policies, ensuring outputs meet compliance requirements, and managing costs across all agent activity. Without it, even a well-built AI agent can drift, overspend, or surface outputs that your team can't trust enough to act on.
Openprise's approach to AI agents and RevOps is built on this orchestration-first principle. Every agent built in AI-Agent Factory operates inside the platform's governance layer, which means:
- Agents access only the data fields and systems they're explicitly permissioned for
- All outputs are logged, auditable, and reversible
- Usage is capped and monitored so AI costs don't spiral outside your budget
- Models never train on your customer data
For a deeper look at how AI orchestration works and why it's becoming the prerequisite for scaling AI in RevOps, read the AI orchestration white paper.
How AI-Agent Factory works
Unlike other data platforms that try to be all things to all people, the RDA Cloud platform's core goal is to make go-to-market (GTM) technology infrastructure easy to use for RevOps. Absent our platform, a RevOps user or builder would have to piece together a minimum of 10 to 15 different technologies and have a team of skilled Python, SQL, and other language programmers at their disposal to even think about adding a tool like generative AI. The full-stack infrastructure and no-code nature of the platform make it possible for users to set aside concerns about how to move, store, integrate, cleanse, normalize, segment, and otherwise manage the data pulled from and used by GTM apps. Instead, the RDA Cloud allows them to focus on solving operational and strategic challenges, like how to get the most insight out of their unstructured first-party data or classify and summarize engagement data to accelerate GTM performance.
The use of embedded services to help users build no-code RevOps applications has long been a core feature of the RDA Cloud. The platform is loaded with multiple pre-built task templates that allow builders to tap into services like web search to surface, for example, specialized, hard-to-come-by franchise location data. The new AI-Agent Factory solution, instead of using a simple search or lookup service, offers new prebuilt templates to connect directly to a generative AI service and use a prompt to perform a more exact search or a sophisticated job, like analyzing an email dataset for sentiment or interest level that can be automated at scale and injected into your GTM processes. Let's take a quick look at an example use case to see how this actually works.
Select a data service
While all generative AI services use roughly the same technology to process your prompt and return a response, subtle differences in their code and training make some better suited for RevOps tasks than others. While Openprise can connect to and embed any publicly available service, we've pre-selected a few models ideally suited for translation, converting unstructured data, and other jobs common to RevOps processes. Starting with a model ideally suited for RevOps gives you a leg up over a generic service. Still, the platform has the flexibility and native integration to connect easily with a wide selection of the most popular public models if you so choose.
Once you've decided on the models or services you want to include, picking the one you want to use for a task is as simple as selecting it from the graphical menu.
While selecting a data service is easy and seamless, the technical work to make that possible is anything but. We've spent hundreds of developer hours building the 300+ integrations and infrastructure with the requisite data access controls, automation, and governance, so you don't have to configure and maintain this.
Choose your task template
Until recently, managing and manipulating data was the job of data engineers and data scientists. Even if a task, such as cleaning titles, didn't require a full-blown dev project, it at least required enough programming knowledge to write and execute a custom spreadsheet macro or function. With the speed of today's business, waiting on central IT or a coworker who can code is no longer an option. Openprise's no-code task templates abstract the complexity from such jobs. Each template is pre-built with the code necessary to accomplish the task at hand, whether it's to deduplicate a record, convert leads to contacts, or a multitude of other processes. Our AI-Agent task templates work in the same way, but instead of a fixed set of fields, they allow you to enter a text prompt describing your job and what you want the service to do.
Configure your prompt
Once a template is chosen, you can use the prompts to configure and specify how you want the service to transform or extract a result from a specific data set. This prompt replaces a fixed set of parameters and instead directs the AI to interpret or analyze the data in a particular way, whether it's generating structured data from an unstructured input, translating information, or performing targeted lookups for categorization.
Operationalize results
Operationalization is the final step, where results are applied to the user's specific business case. For example, a prompt could be set to translate job titles from various languages, returning those translations into Salesforce contact records. This step ensures that AI-generated data directly contributes to actionable insights and RevOps processes, such as enriching lead data or qualifying prospects based on interest indicators.
How can AI-Agent Factory help?
Large language models were first developed to aid and improve language translation. Since the first public model's release, the number and type of uses have grown exponentially. While our imagination ultimately bounds its applications, here are a few of the primary uses we've found valuable to RevOps teams and have created prebuilt templates to solve.
- Data cleansing: cleaning up unstructured data, such as standardizing job titles
- Translation: translating content, particularly for Asian languages, to facilitate global collaboration and communication
- Data extraction: extracting structured data from unstructured sources, including machine logs and email threads
- Engagement data analysis: classifying and evaluating engagement data to identify trends and patterns
- Summarization: summarizing meeting transcripts, survey results, and open-text input to extract key insights
- Sentiment analysis: analyzing meeting transcripts, social media data, and support tickets to gauge customer sentiment
- Web search and scraping: integrating AI with web search and scraping tools to gather relevant data from online sources
- Fuzzy matching: enhancing data matching accuracy by incorporating business context, such as distinguishing between "Oracle" and "Oracle Japan"
- Lead segmentation: categorizing leads based on specific criteria, such as job titles and engagement patterns
- Opportunity management: identifying potential opportunities and prioritizing sales based on AI-driven insights
- Customer success enhancement: analyzing customer interactions to identify areas for improvement and enhance customer satisfaction
Go ahead and build
RevOps leaders and practitioners are a creative lot and are constantly surprising us with the innovative things they build and accomplish with our platform. Watching them sidelined while other teams take advantage of generative AI was a big motivating factor for the development and release of our new AI-Agent Factory. While others have been quick to release AI copilots that tell you exactly what you should do even though they don't know your business context, we allow you to build your own AI solution to fit your needs. We believe secure and compliant, embedded, and turnkey AI will drive your GTM measurably forward.
Not sure where to start?
The Ops AI Prompts Handbook is a library of AI prompts built specifically for RevOps, Marketing Ops, and Sales Ops teams — covering use cases from data cleansing and segmentation to lead scoring and engagement analysis. It's a practical starting point for any team that wants to see what AI agents can do in a RevOps context before building out a full workflow. And if you're ready to understand the executive case for scaling AI from pilot to production, the Executive Imperative for AI report lays out exactly what it takes — and what's at stake if you wait.
But don't take our word for it. Schedule a demo to see it at work for yourself. And if you're an existing Openprise customer, speak with your account executive to enable it in your instance today.
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