Vera vs Salesforge & Agent Frank.

Compare Vera and Salesforge / Agent Frank across outbound, research, company context, workflows, connected tools, controls, and pricing.

Comparison methodology

Published by GrowthEffect, maker of Vera. Official public sources reviewed 2026-09-16. This is not a hands-on benchmark. Recheck linked sources before purchasing.

What is Salesforge / Agent Frank?

Salesforge is the sales execution environment; Agent Frank is its AI SDR offering. Evaluate the agent package separately from the lower-priced software plans. Frank describes ongoing prospecting, account research, email and LinkedIn outreach, a knowledge base, and feedback-informed memories. Co-pilot and auto-pilot offer different supervision levels. Infrastructure can be an add-on. Confirm active-contact capacity and the billing term: the current public pages contain both $499 and $416 annual-display amounts in different configurations.

What is Vera?

An AI sales agent for outbound and the research, signals, pipeline, and recurring work around sales. Company Brain holds shared business context; skills capture reusable working methods. Capacity is shared across agent work. Research and complex automations consume credits too; estimated prospects reached are not guaranteed output. Connected actions depend on plan, account setup, permissions, and approvals.

A sales execution stack and an agent-led workspace.

Separate Salesforge software from the Agent Frank service before comparing either with Vera. Frank’s published knowledge base and feedback-informed memories overlap with the idea of learning from work. Ask whether the retained information improves only outreach or also the other jobs you need, and how your team reviews and corrects it.

Context & learning

Frank’s page explicitly describes a company knowledge base and memories shaped by feedback. Learning is not an exclusive Vera capability; compare scope, reviewability, and reuse.

Workflows & automation

Continuous prospecting and conditional outreach sequences are described. Ask for a demonstration of research or CRM workflows beyond the outbound motion.

Control & approvals

Co-pilot requests review before sending; auto-pilot reduces supervision. Confirm sender restrictions and connected-write authority separately.

Pricing

The reviewed pages advertise Agent Frank at $499/mo, with a $416 annual configuration also visible. Treat this as configuration-dependent and request a confirmed quote. Do not compare the $48 software entry plan with a full AI SDR package. Starter $299, Growth $499, Scale $999 per month; Enterprise custom. Annual billing saves 20%. Compare credits, senders, seats, and integration access, not prospect estimates alone.

Define success before the demo.

Choose a real segment and a small, representative account set. Write down what counts as a qualified company, which roles matter, what evidence must support the approach, and which actions must wait for approval. Make existing customers, open opportunities, opt-outs, and duplicate records part of the brief. Give each vendor the same inputs and enough context to do a fair job. This is a process test, not a race to produce the largest spreadsheet.

Inspect the work, not just the result.

Review the sources behind account research, the reason for qualification, the message draft, and the proposed CRM action. Ask to see the difference between a generated draft, an approved action, an enrolled sequence, and an actual send. If the workflow fails halfway through, find out what is retried and whether the next run can duplicate an external action. Record the amount of human editing needed, not just whether the vendor can produce an output.

Test a correction and a second run.

Change one assumption after the first demonstration. For example, enterprise accounts may need a different positioning angle or a longer inactivity window. Ask where the correction is stored, who can review it, what future work it affects, and how to undo it. A product remembering a conversation is not automatically the same as a governed working method. Compare the behavior you observe and avoid assuming that any one vendor owns the idea of learning.

Compare the operating commitment.

A trial should end with a clear owner for infrastructure, deliverability, data quality, permissions, and exception handling. Request a written estimate for your intended workload, including any required connected services. Review the security material, data-processing terms, cancellation timing, and available support. If the team cannot operate the chosen motion reliably, a long feature checklist will not compensate. These are evaluation criteria, not performance findings about the products on this page.