RevOps data and AI automation platform eliminates LLM-based data preparation, delivering up to 30% savings across general AI workloads, 40-60% on data-intensive workflows, and up to 80% on agentic use cases
SAN MATEO, Calif. — June 25, 2026 — Openprise, the leader in RevOps data automation, today announced that enterprises deploying AI across their go-to-market (GTM) workflows can cut AI token costs by up to 30% on general AI workloads, 40-60% on data-intensive workflows, and by up to 80% on agentic use cases such as AI SDR outreach, automated account research, and AI-powered lead scoring, by using Openprise to handle data preparation deterministically before data reaches the large language model (LLM).
As enterprise AI spend climbs and finance leaders scrutinize every line on the AI budget, Openprise is making a simple case: the cheapest token is the one you never spent. By replacing expensive LLM-based data preparation — classification, enrichment, deduplication, and normalization — with deterministic, rules-based automation, Openprise shrinks both the work AI has to do and the context it needs to do it.
"AI is only as cheap as your data is clean," said Ed King, CEO of Openprise. "Most enterprises are paying their AI models to do work that should be done deterministically upstream. Asking a frontier model to figure out a contact's seniority, infer a company's industry, or dedupe records at runtime is expensive, error-prone, and completely avoidable. Openprise does that work once, deterministically, for a fraction of a cent — so your AI tokens can be spent on what AI is actually good at."
How Openprise Reduces AI Token Costs
Openprise reduces AI token consumption through four core mechanisms:
Eliminating LLM-based data preparation. Classification, enrichment, segmentation, and deduplication done by deterministic rules in Openprise removes those tasks from the AI's job entirely — taking millions of tokens out of every workflow run.
Compressing prompt context. Clean, normalized, structured data is more token-dense than raw, redundant strings — meaning smaller prompts, fewer few-shot examples, and more useful information per context window.
Reducing retries and hallucination loops. Cleaner inputs produce cleaner outputs the first time, cutting the retry overhead that compounds token costs across multi-step agent workflows.
Replacing inference with enrichment. Pre-enriched firmographic, technographic, and intent data on every record means the AI never has to look up or infer what Openprise has already appended.
What Customers Can Realistically Expect
Openprise customers can anchor their expected AI token savings to three tiers, based on the depth of data preparation Openprise is replacing in their AI workflows:
Conservative, broadly applicable: up to 30% reduction in AI token costs across general GTM AI workloads. Applies to virtually any AI workload that consumes customer, prospect, or account data — even without targeted workflow redesign. This range captures the foundational gains from cleaner, normalized inputs: smaller prompts, denser context, and fewer retries triggered by dirty data.
Mid-range, for data-intensive workflows: 40-60% reduction in AI token costs. Applies to AI use cases that depend on GTM data — including lead scoring, segmentation, content personalization, account-based marketing orchestration, and conversational AI grounded in CRM data. This tier reflects the combined effect of eliminated classification work, pre-applied enrichment, and significantly lower retry rates.
High-value, agentic workflows: up to 80% reduction in AI token costs. Applies to workflows where Openprise replaces multiple steps of LLM-based data preparation in a single run — most notably AI SDR agents, automated account research, agentic personalization, and AI-powered lead scoring pipelines. In these workflows, deterministic rules are not just supplementing the LLM; they are replacing entire LLM steps.
By the Numbers: AI SDR Agent for 10,000 Contacts per Month
For a representative AI SDR workflow processing 10,000 contacts per month:
Without Openprise, a typical agent consumes approximately 23 million tokens per month — roughly 3M on persona and seniority classification, 4M on company industry and size inference, 5M on duplicate matching against prior outreach, 8M on personalized message generation, and 3M on retries caused by dirty data inputs.
With Openprise, the same workflow consumes approximately 6.3 million tokens per month. Classification, enrichment, and deduplication are handled deterministically in Openprise before data reaches the LLM, message generation runs on shorter and cleaner prompts, and retry overhead drops from roughly 15% to under 5%.
That is a 73% reduction in token consumption on a single high-volume workflow, with measurable improvements in output accuracy and agent reliability as a direct corollary.
Strategic Implications for RevOps and IT Leaders
The result is a structural shift in how enterprises should think about AI cost management. Tuning prompts and switching to cheaper models only goes so far. The largest controllable variable in enterprise AI spend is the quality of the data feeding the model — and that is a problem Openprise has solved for more than a decade.
"Every revenue and IT leader I've spoken with in the last six months is wrestling with AI spend that is outpacing the ROI they expected," said Ed King, CEO of Openprise. "What they often miss is that 50 to 80% of those tokens are being spent on grunt work — sorting, matching, classifying, filling in blanks — that doesn't require AI at all. Openprise lets you reserve your AI budget for the work only AI can do."
Availability
Openprise’s AI token reduction capabilities are available today as part of its RevOps Data and AI Automation Platform. RevOps teams can request a demo at openprisetech.com to see how Openprise reduces AI token costs for their specific GTM workflows.
About Openprise
Openprise makes your GTM data smarter with AI and automation. As the only data and AI orchestration platform built for modern go-to-market teams, Openprise automates your processes, unifies your data silos, and consolidates point solutions so Ops leaders can build smarter GTM data — your data, your way, your timeline. Fortune 500 companies and high-growth enterprises alike rely on Openprise to unlock cleaner data, more efficient operations, and AI-ready pipelines. See how Openprise makes your GTM data smarter at www.openprisetech.com and follow us on LinkedIn.