Every RevOps operator has lived through some version of the same nightmare. A lead comes in from a form — first name lowercase, email misspelled, title missing, phone number blank. Somewhere upstream, a vendor sold you a list of three thousand contacts, half of which already exist in your CRM as duplicates. Your enrichment tool fills in a few fields but formats them differently than the last enrichment tool you used. And now your executives are asking why revenue isn't up-leveling faster when you've added AI to the top of the stack.
Here's the uncomfortable truth: AI doesn't fix bad data. It amplifies it. If you're feeding a fragmented, unstandardized, siloed mess into an AI model, you're paying token costs to generate confident-sounding garbage. The problem was never that you needed more tools. The problem is that nobody solved for the layer underneath them.
That's the problem Openprise was built to address. In this episode of RevOps Demos That Don't Suck, we sat down with Laura Marzola, a member of the solutions engineering team at Openprise, to walk through how the platform works — and why, after six and a half years on the pre-sale and technical scoping side, she still thinks this is one of the genuinely unsolved problems in go-to-market operations.
Openprise is an AI and data orchestration platform built specifically for go-to-market operations teams. It sits between your data sources — CRMs, marketing automation platforms, data warehouses, ABM tools, list vendors, webhooks — and your systems of record, ensuring that data is cleaned, enriched, deduplicated, standardized, and routed before it ever reaches a rep or triggers a workflow.
The positioning matters: Openprise is not a data provider, not an ETL tool, and not an iPaaS point solution. It's a no-code automation platform purpose-built for RevOps, MarketingOps, and SalesOps teams who need to move data through a GTM stack without losing fidelity, security, or control at every handoff.
"We are a fully no-code automated platform that unifies data silos and can automate entire go-to-market processes — and that really is for ops teams. There's other technology out there that does it well for IT, but this truly is for ops." — Laura Marzola
If you've ever wrestled with 12 common CRM and MAP issues — duplicate records, mismatched field formats, list loads that break your lifecycle stage logic — you've already felt the underlying problem Openprise targets. The Franken-architecture problem, as Laura called it, is structural: data lives in too many places, in too many formats, with too much inconsistency to be actionable without heavy manual intervention.
Add AI into that environment and things get worse before they get better. As Laura put it, AI is only as good as the data you feed it — and if you have 20 different pieces of software to manage, where do you even put the AI layer? The result is compounding costs, compounding errors, and an executive mandate to "use AI" that nobody can execute cleanly.
Openprise positions itself as the answer to what Laura calls the "last mile" problem: after your AI tools, your enrichment providers, and your signals platforms have done their work, someone still has to take that output, deduplicate it, standardize it, and route it to the right place. That's where Openprise operates.
The core of Openprise is what Laura calls the automated pipeline — a configurable sequence of no-code "recipes" that process records from the moment they enter the system to the moment they reach a rep. Each recipe handles a specific operation: cleansing, normalization, enrichment, deduplication, matching against existing CRM records, segmentation, scoring, and routing.
These recipes are bundled into "bots" — scheduled automations that can run in real time or on a defined interval. During the demo, Laura walked through a live record that arrived with a lowercase name, a misspelled email, missing title and phone fields, and a comment field containing unstructured text about product interest. By the time that record exited the automated pipeline, the email was corrected, the name was normalized, the missing fields were enriched from multiple vendors, the unstructured comment was parsed by an embedded AI model to extract product interest, and the record was routed to the appropriate rep — with the AI-extracted context included.
This is the kind of operational lift that gets lead routing right at scale — not by adding headcount, but by automating every transformation step between raw input and CRM-ready output.
One of Openprise's most widely used capabilities is its list loading application — a self-service drag-and-drop portal that allows operations teams to upload purchased or sourced contact lists, have them auto-mapped, deduplicated against existing CRM records, validated, and processed through the full recipe pipeline before anything touches production.
Adobe processes over 700 lists per month through this capability, saving an estimated $250,000 a year in manual labor costs. Palo Alto Networks saved over 2,400 hours annually by eliminating the manual list loading process and the point solution they'd previously used to manage it.
The practical value for RevOps teams: anyone on the ops team can drag and drop a list, the system flags junk records, rejects lists that don't meet minimum quality thresholds, and only sends clean, CRM-matched records downstream. As Laura noted, it also gives teams visibility into which list vendors are consistently delivering bad data — a quiet ROI argument for renegotiating those contracts.
Openprise supports waterfall enrichment across multiple providers — Cognism, Dun & Bradstreet, Sales Intel, and others — through a single contract rather than requiring teams to manage separate vendor relationships and API keys. The platform handles the formatting differences between providers, tagging and normalizing output so that regardless of which vendor fills a given field, the data lands in the CRM in the correct format.
The only exception today is ZoomInfo, which Openprise does not resell directly. Teams that retain ZoomInfo can connect it via API and place it at the beginning of the waterfall, with Openprise supplementing from other vendors downstream.
For use cases that require going out to the web — for example, a healthcare client that needs to know how many physical locations a given medical provider operates — Openprise connects to clients' existing OpenAI models or, soon, Claude. For use cases that don't require a grounded model (language translation, unstructured text extraction, format normalization), Openprise offers its own embedded AI model that doesn't send data to the web and doesn't learn from client data — a meaningful consideration for security-conscious organizations.
A newer capability Laura highlighted is prompt injection detection — identifying malicious or manipulative text that's sometimes submitted through web forms to influence AI systems downstream. The platform flags these records and quarantines them before they enter the automated pipeline, protecting downstream AI models from adversarial inputs.
This is the kind of operational governance that tends to be invisible until something goes wrong — and the kind of thing RevOps teams increasingly need to own as AI gets woven into more GTM workflows.
Implementation typically runs six to eight weeks per solution, with some quick wins delivered within the first four weeks depending on complexity. Openprise's solutions engineering team handles pre-sale scoping, and a dedicated client services team (usually two to three people) takes over at handoff and stays engaged throughout the contract term with weekly or monthly calls.
On the integrations side, Openprise offers bi-directional connectors to Salesforce, Marketo, Eloqua, Freshworks, HubSpot, Dynamics, Snowflake, Redshift, AWS, Box, Dropbox, Google Sheets, SharePoint, and more. API and webhook capabilities cover additional use cases, and new connectors can be built on request — Monday.com was a recent example.
Openprise is best suited for mid-market to enterprise organizations with complex, high-volume data operations:
Pricing is structured around three components:
A self-serve trial capability is in development and expected to launch later this year. Currently, the recommended first step is a direct demo with the solutions engineering team, with the option for a custom proof of concept against the client's own data and logic.
The ROI patterns vary by client, but the common thread is time and cost recovered from manual operations:
As teams mature on the platform, the ROI story tends to shift from time savings to data quality and GTM execution — cleaner pipeline, more accurate routing, AI models that produce better outputs because the inputs are finally trustworthy.
The Franken-architecture problem is real, and it's getting more expensive as AI gets added on top of it. Every AI tool in your stack is only as good as the data you feed it — and if that data is living across a dozen siloed systems in inconsistent formats, you're paying for AI outputs you can't trust and routing on signals you can't verify.
Openprise sits at a layer most RevOps teams have historically tried to solve with point solutions, manual cleanup, or sheer willpower. The fact that it handles cleansing, enrichment, deduplication, scoring, routing, and now AI orchestration in a single no-code platform — without per-seat pricing and without requiring you to manage five separate vendor contracts — makes it worth a serious look for any team that's honest about how much operational overhead their current approach is actually costing them.
Request a personalized demo or learn more on the Openprise website.
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