Ironclad Review: Testing out Ironclad for a week

For each, I tracked live metrics and compared Ironclad to at least one modern, AI-native competitor, with user feedback surfaced in shaded blocks for clarity.

Related Article: Looking for Ironclad Alternative? Check this AI CLM out

Days 1–3: Setup, Onboarding & First Impressions

Account Activation & Onboarding

The Reality:

Sign-up was self-serve and relatively fast (sandbox ready in 45 minutes), but true implementation was much more nuanced.

• “Easy to get started—the login and main dashboard look clean. But for metadata setup, expect a learning curve unless you’re importing their pre-made templates.”
• Self-onboarding resources were present, but several training materials were “high-level and out of date,” a sentiment echoed in multiple reviews.
• Initial workflow creation was smooth for vanilla NDAs, but more complex business terms revealed a knowledge gap.

Sandbox & Trial

The sandbox mode provided a reasonably isolated playground. Uploading my first contract was simple, but mapping custom properties to the company taxonomy was manual and required a few attempts.

• Some users in G2 and Reddit claim hours went to staring at “where do I set X property?” or calling in support after hitting edge cases.

Account Setup Friction:

Authentication and multi-user invite links worked, though initial collaborators (e.g., a mock sales VP and procurement) weren’t notified automatically that workflows had been assigned to them, requiring separate emails/Slack.

First UX Impressions:

• The dashboard is modern, filterable by contract stage (submitted, in review, out for signature, executed). Threaded comments are decent, but not as streamlined as point-in-time collaboration à la Google Docs.

Related Article: Preparing for Successful CLM Implementation: Are You Ready?

Days 4–5: Importing Contracts & Testing the Repository

Bulk Upload: How Smart Is Smart Import?

Ironclad’s much-touted “Smart Import” was one of the most urgent tests. I uploaded 76 legacy contracts (NDAs, MSAs, scanned PDFs, Word files with heavy redlines).

The Results:

• Bulk upload is fast for up to 100 docs at a time, but metadata extraction accuracy is mixed.
• SmartImport property and clause collection is incomplete or wrong (20% wrong). This means 1 in 5 records needed significant manual tag correction, particularly for governing law, evergreen terms, or unusual payment triggers.
• Reporting views can be adjusted, but time columns sometimes appeared in raw milliseconds with no clear label, which was—frankly—confusing.

Search & Filtering

The contract repository’s search is multi-faceted (supports Boolean, clause tag, date, and counterparty filters). However, the relevance engine was inconsistent as data volume increased.

• While using on large contract volumes, it occasionally lags in performance.
• Search results include unrelated documents… needs optimization.

Tagging & Audit Trails:

Every change is versioned, with an audit trail accessible for compliance. However, custom tags must often be cleaned up after import, as “tag drift” is real for non-standard paper.

Pain Point: Manual cleanup turned a supposed 1-hour repo migration into a half-day project, solely due to property mapping and verification lags.

Related Article: Contract Repository: How to Set it Up Effectively?

Days 6–7: Pushing Ironclad’s AI Capabilities

Redlining, Clause Extraction & Fallbacks

Ironclad’s AI is based on OpenAI’s GPT-4. Redlining is available via “AI Assist,” promising tracked changes, fallback suggestions, and auto-extractions.

• On simple NDAs and basic MSAs, the AI offered direct markup—“replace with days,” or flag missing Limitation of Liability clauses.
• For industry-specific contracts (SaaS, HIPAA), AI accuracy dropped:
• Flagged standard terms as “high risk,”
• Missed several auto-renewal/evergreen term triggers,
• Skipped past complex indemnity language.

“AI for redlining is as basic as it gets… I still had to check every change by hand. Smarter, but not as smart as I need it.”- G2 User

Obligation Detection & Risk Flagging

Detection for net-30 clauses, renewal triggers, and GDPR terms worked if clauses matched ones in Ironclad’s pre-trained library. Custom business definitions, however, regularly required retraining or fell through the cracks.

Risk scores are generated, but their reliability falls off for complex, multi-clause “what if?” scenarios, like layered liability or compliance carve-outs.

Days 8–10: Workflow Testing and Real-World Usability

Building Multi-Step Approvals

Ironclad’s no-code Workflow Designer allows for conditional flows, multi-approver routing, and custom email notifications.

• Simple sales-to-legal-to-procurement was done in 30 minutes.
• Adding business logic (e.g., “If indemnity >$1M, trigger CFO review”) was possible, but complex routes (hybrid HR/Finance/legal flows) required trial and error:
• Onboarding new team members to design workflows… like pulling teeth.

Visibility:

Workflow statuses are clearly labeled, but granular status updates are limited (“In Review” or “Approved/Rejected”)—losing sight when 5+ stakeholders are involved in large deals.

Template Library & Reuse

Reusable templates for NDAs and MSAs are a core strength; updating clause language ripples through new workflows, but in-flight documents aren’t retroactively updated—a snag for larger enterprises.

Changes to a workflow template will not affect in-flight contracts, so you would have to take the docs out of Ironclad and go through email/Docusign, which cancels the need for Ironclad.

Collaboration Without Handholding

• Business users can trigger most agreements, but mass adoption is a training challenge.
• Notification gaps—“Sales has no idea it’s their turn to sign unless legal pings them separately”—are frequently cited in reviews.

Competitive Comparison

Ironclad shows notable gaps in AI accuracy, metadata extraction (20% error rate), and user experience, requiring manual fixes and a steep onboarding curve.

Volody addresses these issues with a powerful AI tailored for complex contracts, delivering over 95% import accuracy and advanced, context-aware redlining. Its intuitive interface and deep integrations with Salesforce, DocuSign, and MS365 speed up workflows and collaboration—no heavy IT lift needed.

Other competitors like ContractWorks and Agiloft provide solid automation but fall short on AI precision and ease of use compared to Volody’s streamlined, legal ops–focused design.

What Other Users Are Saying

• “SmartImport is incomplete… 20% wrong. Training materials out of date.” — G2
• “AI for redlining is as basic as it gets… terrible user-friendliness.” — Reddit
• “It’s okay but could be more intuitive. Threading could be better.” — Capterra
• “Easy to manage workflows ourselves, simple UI for users, intuitive.” — G2
• “No CLM has real smart import…most clients hire contractors after self-attempt fails.” — Reddit

Where the Platform Struggled (and Why It Matters)

Even with its sleek interface and growing adoption, Ironclad left me with a few red flags, especially when stacked against an AI-first legal workflow. Here’s where it stumbled and what that could cost your team:

1. Rigid Workflow Logic

No midstream edits. No exceptions. Once a workflow is launched, you can’t make changes to it, even if the negotiation is already underway. This leads to clunky, manual workarounds outside the system, especially painful for fast-moving teams who need agility.

2. AI That’s Still Just “Okay”

Decent triage, poor playbook alignment. Ironclad’s AI feels more like a decent assistant than a strategic partner.

• Suggestions are basic and miss nuance in regulated industries.
• Every markup still needs a legal review.
• Fallbacks often contradict internal playbooks — a trust gap you can’t afford in high-stakes negotiations.

3. Shallow Integration Depth

Salesforce is great. Others? Not so much. While Salesforce sync is well-built, deeper integrations with tools like Slack and DocuSign feel thin:

• Notifications often need Zapier hacks to truly work.
• Real-time collaboration feels disconnected, making cross-functional alignment harder than it should be.

4. Pricing & Support: A Mixed Bag

High sticker price, uneven service. Ironclad uses a custom quote model, with buyers reporting annual costs anywhere between $30K and $120K+, depending on features. That’s fine — if you’re getting true AI-first value. But right now:

• You pay as if it’s AI-first but get legacy-level results.
• Enterprise support is responsive. But for regular business users, query response times can stretch into days.

5. Admin Setup: More Rigid Than Flexible

Self-serve is smooth — until it’s not. The platform works well for simple, plug-and-play teams. But once you scale into complex legal ops, you hit a recurring dilemma:

• Rigidity vs flexibility.
• Too much manual admin overhead for nuanced use cases.
• Limited configurability unless you escalate to Ironclad’s own team.

Related Article: Top Contract Management Tips for Legal Professionals

The CLM That Fit My Checklist Best

After this real-world evaluation, I shifted to Volody—here’s why:

• AI-Native Architecture: 90%+ clause accuracy with fallback logic—perfect for playbooks and redlines.
• No-Code Workflows: Real drag-and-drop logic—no IT help needed, even for complex approval flows.
• Bulk Import Superiority: Migrated 400+ legacy files with under 10% errors and smart auto-tagging.
• Clear Onboarding & Pricing: Flat-rate pricing and in-house legal experts—no vague SOW surprises.
• Rapid Time to Value: Full deployment took under 7 days—test to prod without the long rollout.
• Robust Integrations: Salesforce, DocuSign, MS365—all natively integrated, not future promises.

“Switching to Volody got our team to 80% contract triage via AI with zero backlog within the first month. The difference in speed and usability was night and day.” — Legal Operations Peer

Final Thoughts

CLM success is not only about AI accuracy or a pretty dashboard—it’s about adoption, robust process automation, and real legal risk reduction. Ironclad is a sound starting point for teams standardizing on basic sales contracts and looking for Salesforce immersion, but teams prioritizing deeply accurate clause analysis, obligation detection, and faster cycle times may soon run into its hard limits.