Commercial Operations that Runs Itself | Poka Labs

What

Poka Labs is an AI software platform for industrial B2B commercial operations. It is designed for teams that need to set prices, respond to RFQs, generate quotes, and support sales decisions using data that is often scattered across email, spreadsheets, ERP systems, and product documents.

The product appears positioned as a layer on top of existing commercial systems rather than a replacement for them. Its core workflow covers price setting, quote automation, and guided selling, with AI agents handling unstructured inputs, applying business rules, and keeping humans in the loop for review and approval.

Features

• AI-driven price setting: Tracks cost trends, flags repricing priorities, and helps set catalog prices to support more consistent margin management.
• RFQ and quote automation: Parses messy RFQ emails and attachments, routes requests, applies pricing rules, and drafts quotes in minutes.
• Guided selling support: Provides TCO analysis, margin defense points, and cross-sell recommendations to help sales teams respond more effectively.
• Unstructured data ingestion: Works with Excel, CSV, PDF, scanned documents, email text, and vague product descriptions to identify SKUs, grades, quantities, and related product details.
• Business-rule enforcement: Applies customer-specific rates, discount logic, margin thresholds, and approval workflows based on the company’s existing playbook.
• Human-in-the-loop controls: Lets teams review AI outputs, approve higher-value decisions, override recommendations, and sync results back to core systems.

Helpful Tips

• Validate rule coverage early: For products like this, the success of automation depends heavily on how completely pricing logic, approval thresholds, and product substitution rules are captured.
• Start with a narrow workflow: A focused rollout around one quoting process, product line, or sales segment is often the clearest way to test accuracy and adoption.
• Check data-handling edge cases: If your business receives highly inconsistent RFQs, verify how well the system handles ambiguous descriptions, legacy SKUs, and attachment-heavy requests.
• Assess review design, not just automation speed: Human approval steps are important in margin-sensitive industrial sales, so it is worth reviewing how exceptions and overrides are surfaced.
• Map system dependencies upfront: Although the site says no rip-and-replace is required, buyers should still confirm the depth of ERP, CRM, and spreadsheet connectivity for their specific environment.

OpenClaw Skills

Within the OpenClaw ecosystem, Poka Labs could likely serve as a commercial intelligence and execution layer for industrial sales workflows. Based on the page, likely OpenClaw skills could include agents that monitor inboxes for incoming RFQs, classify request types, assemble quote-ready product and pricing context, and route approvals to the right manager or rep. This is a likely use case rather than a confirmed native integration, since the page mentions connectivity to ERP, CRM, email, and spreadsheets but does not describe OpenClaw directly.

A broader OpenClaw workflow could combine Poka Labs with procurement, account research, and post-quote follow-up agents. For example, an industrial distributor or manufacturer could run a coordinated process where one agent extracts RFQ details, another checks account history and margin thresholds, another prepares seller talking points, and another tracks quote status and next actions. In likely practice, that could reduce manual quoting overhead and make commercial teams operate more like continuously monitored, policy-driven systems rather than email-based handoffs.