GoodLads vs Hexis: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of GoodLads and Hexis — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

GoodLads

Paid

AI growth manager for Google Ads that turns account performance into testable hypotheses and ships each one only on your approval.

Hypothesis Feed: Daily analysis of search terms, keyword quality, geography, and audiences produces a ranked list of ideas, each naming the campaign and the spend at risk.
One-Click Shipping with Approval Gate: Any proposed change is applied in a single click but never without explicit owner approval, and live ads are not edited directly.
Kanban Verdict Board: Hypotheses move through Proposed, Scheduled, Live, and Completed so every test ends with a measured verdict rather than being forgotten.
Account Treemap Overview: Campaign spend, conversions, and ROAS roll into one visual overview sized by spend and coloured against the account average.
Least-Risky Lever Selection: Recommendations favour reversible mechanisms such as 50/50 RSA experiments, stepped target CPA changes, and new paused assets.
Predicted vs Measured Reporting: Each completed experiment compares the predicted lift against the actual result, with budget shifting to the winner.
Claude Code and Codex Integration: The same workflows can be driven from Claude Code or Codex for teams that work from a coding agent.

Performance Review: Get a single overview of how every campaign is doing on spend, conversions, and ROAS without building reports by hand.
Wasted Spend Discovery: Surface negative keyword opportunities, poor keyword-ad combinations, and geography issues that are draining budget.
Budget-Capped Campaigns: Identify campaigns limited by budget and lower target CPA in reversible steps to buy cheaper conversions at the same spend.
Ad Copy Testing: Run benefit-led versus price-led headline experiments as 50/50 splits instead of editing live ads.
Seasonal Campaign Prep: Stage seasonal copy and sitelink assets in advance, ready for one-click approval when demand spikes.
Agency Account Management: Manage optimisation hypotheses across multiple client accounts from one board with a shared approval workflow.

Bevelites GmbH

Freemium

Open-source git-backed control plane for enterprise AI agents — context, skills, tools and permissions as files in your own repo, served over MCP.

Git-backed source of truth: Context, skills, tools, permissions, and agent identities live as Markdown/YAML files in your own repository — reviewed, branched, and diffed like any other code.
Typed knowledge with provenance: Every fact is a typed node with source, owner, and last verification date, compiled into a graph you can traverse, mass-update, and dashboard.
Markdown skills, not prompt fragments: Procedures live as readable Markdown files that domain owners can review, instead of prompt snippets buried in a vendor config.
Tool manifests with vaulted secrets: Declare each tool once, hold secrets in a vault, and set file-level access rules that say which agent may read which file and call which endpoint.
Per-agent identity: Each agent is a named actor with its own credentials and scope — no shared service accounts, and every action is attributable to a specific agent.
Any runtime via MCP or UTCP: Serves the same governed surface to Claude Code, Cursor, ChatGPT, opencode, background agents, in-platform agents, and self-hosted models.
Open-source Apache-friendly release: Hexis is the OSS core; Bevel (paid) adds hosted services, integrations, and enterprise support on top.

Enterprises running AI agents across multiple vendors (Claude, Cursor, ChatGPT) who want context and skills defined once in their own infrastructure instead of re-uploaded per vendor.
Procurement, GTM, and RFI teams building specialised agents (e.g., tender desks, campaign agents) on top of a shared reusable knowledge layer with provenance.
Platform teams that need per-agent identities and file-level access controls so every tool call is attributable and destructive endpoints are gated by review.
Companies wary of vendor lock-in who want a portable spec (MCP/UTCP) so they can switch agent runtimes without rebuilding context, skills, and tool wiring.
Engineering leaders who want the AI 'operating manual' reviewed in git diffs and change requests instead of edited inside a black-box vendor console.
Open-source-first teams evaluating a control plane locally with Hexis before committing to Bevel's hosted platform for larger deployments.