Moving from Community Sift to Lasso: A complete migration guide
Melissa
Copywriter
Most Community Sift migrations to Lasso take about three weeks.
This is a step-by-step guide on how to migrate.
TL;DR
- Most CS capabilities map directly to Lasso. Text moderation, image moderation, video, custom rules, review queues, and webhooks all have working equivalents.
- One genuine gap, one that works differently. CS's patented User Reputation states (Not-Trusted / Default / Trusted) don't have a direct Lasso equivalent. Predictive Moderation exists in Lasso but works on a different self-learning model. More on that below.
- A typical migration takes 2–3 weeks. Lasso's initial integration is about two days. The remainder is parallel testing and rule calibration.
- We offer free migration support. Book a call and we'll walk through your specific setup.
- A full feature-by-feature mapping table is in the second half of this guide. If you want to check whether a specific CS capability has a Lasso equivalent before reading anything else, skip straight to "Feature mapping: what you had and what replaces it."
Need help working through the feature gaps?
Book a free 30-minute call with our migration team. Bring your CS integration docs and any custom classifier logic you've built. We'll map your setup to Lasso's equivalents and flag anything that needs special handling.
Schedule your free migration callIf you're still deciding which platform to move to, the Community Sift alternatives guide covers your options honestly, including where other platforms outperform us. If you want background on what Community Sift was and what's happening to it, start with what is Community Sift.
API migration: what changes and what doesn't
Authentication
Community Sift used an API key passed as a query parameter or request header. Lasso uses Bearer token authentication. Pass your key in the Authorization header on every request:
Authorization: Bearer YOUR_API_KEYGenerate your key at https://app.lassomoderation.com/settings.
Endpoint mapping
| CS endpoint | Lasso equivalent | Notes |
|---|---|---|
| POST /content: submit text for moderation | POST /content |
Lasso's primary ingest endpoint. Accepts text, image URLs, video URLs, and audio URLs. |
| POST /image: submit image separately | POST /content with image_urls field |
Text and images go through the same content endpoint in Lasso. One less endpoint to maintain. |
| User status or profiling update | POST /user |
Update user attributes without submitting new content. |
| Player-reported content | POST /user-reports |
Reports feed into the review queue and can trigger custom rules. |
| Webhook (CS to your app) | Webhook (Lasso to your app) | Both use HTTPS POST to your configured endpoint. Payload schemas differ, so see below. |
Full reference: api-reference.lassomoderation.com
What a Lasso webhook payload looks like
When Lasso has processed a piece of content, it sends a POST to your configured webhook endpoint with a payload like this:
{
"content_id": "msg_abc123",
"user_id": "user_12345",
"actions": [
{
"action_type": "remove",
"rule_id": "rule_hate_speech_high",
"actor_id": null,
"timestamp": "2026-02-25T11:34:00Z"
}
]
}The actions array tells you what Lasso decided, which rule triggered it (or which human moderator acted, if it came from the review queue), and when. Lasso signs every webhook with HMAC-SHA256 so you can verify it genuinely came from Lasso before processing it.
Previously in Community Sift, your webhook received named event types like FLOOD, MUTE, or BAN. In Lasso, all decisions come through the same actions array structure: simpler to handle and easier to log.
Rate limits
By default, Lasso's content endpoint has a rate limit of 60 requests per minute. This can be customized based on your requirements. Check the API reference for current limits across all endpoints.
Feature mapping: what you had and what replaces it
The table below is a reference for your audit. Use it to identify what you need to rebuild, what transfers directly, and where to plan for something different.
| Community Sift feature | Lasso equivalent | Parity | Migration notes |
|---|---|---|---|
| Text moderation (19 classified topics) | Lasso AI text moderation | Direct | Lasso classifies text across hate speech, harassment, sexual content, self-harm, threats,, and more. Topic taxonomy differs slightly from CS's 19 categories. Review category coverage during parallel testing. |
| Severity scoring (sliding risk scale) | Lasso AI toxicity scoring | Direct | Both systems return a risk score per piece of content. Score ranges differ. Recalibrate your thresholds during parallel testing rather than porting CS scores directly. |
| Image moderation | Lasso image moderation | Direct | Lasso detects nudity, CSAM indicators, gore, weapons,drugs, and more in images. Contact our team for accuracy figures on your specific content types. |
| Video moderation | Lasso video moderation | Direct | Lasso supports video content scanning. Ask our team for frame rate and file size specifications relevant to your use case. |
| Custom classifiers and policy rules | Custom moderation rules | Direct | Lasso rules are condition-action based. CS custom classifiers trained on your content map to Lasso custom rules that combine AI scores with user and content attributes. Rules update in real time with no deployment required. |
| Per-feature strictness (public chat vs. private) | Custom rules with context attributes | Different approach | CS had built-in "flexible policy guides" for different social surfaces. In Lasso, you replicate this with custom rules that fire based on the category or subcategory you assign to a piece of content. |
| Human review queue | Lasso review queues | Direct | Lasso's review queue is the primary interface for human moderation decisions. Routing rules direct content to specific queues based on category, risk level, or custom criteria. |
| Webhook callbacks | Lasso webhooks | Direct | Both platforms use outbound webhooks to deliver moderation decisions to your application. Payload schemas differ. See the API section above for the mapping. |
| User profiling | User ingestion and custom attributes | Direct | Lasso tracks user history across moderation decisions. Send user metadata via the user ingestion endpoint and reference it in custom rules for behavior-based automation. |
| User reports | User reports endpoint | Direct | Lasso has a dedicated user reports API endpoint. Reports feed into the review queue and can trigger custom moderation rules. |
| Multi-language support (20 languages) | 22 native-built languages | Direct | CS supported 20 languages built by native speakers, not translation engines. Lasso supports 22 on the same basis. Confirm your specific languages are covered before migrating. |
| CSAM detection | CSAM detection | Direct | Lasso includes CSAM detection. Contact our team for details on hash-matching methodology and law enforcement reporting workflows specific to your compliance requirements. |
| Unnatural Language Processing (leet-speak, evasion patterns) | Lasso AI normalization | Different approach | CS's ULP was a patented system built specifically for gaming-style text obfuscation. Lasso's AI handles most common evasion patterns through its training data. For highly adversarial communities, stress-test your edge cases during parallel testing. |
| User Reputation states (Not-Trusted / Default / Trusted) | Not directly replicated | Gap | CS's patented three-state trust tier system was a distinctive capability. Lasso tracks user history and supports rule-based actions based on prior violations, but there is no automatic trust promotion system. User reputation scores is on Lasso’s roadmap. |
| Predictive Moderation (AI trained on your mod team's decisions) | AI Moderators (self-learning from human decisions) | Different approach | CS's Predictive Moderation trained a bespoke AI model on historical decisions to auto-triage user reports. Lasso's AI Moderators work differently: every time a human moderator takes an action (flagging a word, marking content as spam, removing a specific item), that decision is automatically incorporated into what the AI moderators filter going forward. The self-learning loop is there; it just operates decision-by-decision rather than as a batch-trained model. |
Need help working through the feature gaps?
Book a free 30-minute call with our migration team. Bring your CS integration docs and any custom classifier logic you've built. We'll map your setup to Lasso's equivalents and flag anything that needs special handling.
Schedule your free migration callMigrating your moderation rules
Your CS custom classifiers and rule configuration represent real institutional knowledge: decisions your team made, tuned over time, and validated against your community's actual behavior. It's worth migrating this carefully rather than starting from scratch.
The most common mistake we see: teams port their old severity thresholds directly into Lasso without recalibrating. CS and Lasso score content differently, so a threshold that was well-tuned in CS will often over- or under-flag in Lasso until you've run it against real content. Build in time for that.
Step 1: Export and document your CS configuration.
Pull a full export of your custom classifiers, severity thresholds, and policy guide settings. Document not just the settings but the reasoning: which rules exist because of specific incidents, which thresholds were tuned after false positive complaints, which language-specific rules your team added manually. You'll need this to rebuild intelligently.
Step 2: Categorize your rules by type.
CS rules generally fall into a few categories: keyword-based blocks, classifier threshold adjustments, per-feature strictness settings, and trust tier adjustments. Each maps differently to Lasso. Trust tier adjustments are the one category with no clean mapping. If CS's reputation states were central to your moderation strategy, flag those rules separately and plan for a custom approximation before you start rebuilding.
Keyword-based blocks translate directly to Lasso custom rules with keyword conditions. Classifier threshold adjustments become Lasso rules that fire when the AI score for a given category exceeds your calibrated threshold. Per-feature strictness maps to rules that check a context attribute in the content payload.
Step 3: Rebuild rules in Lasso's rule interface.
Lasso's custom rule builder is no-code. Conditions use a logical AND/OR structure, and actions include flag, remove, strike, priority, and tags. Any rule change goes live immediately. No deployment required.
Step 4: Test against historical content before going live.
Use Lasso's import endpoint to run historical content samples through your new rule configuration and compare outcomes against what CS would have done with the same content. This is the fastest way to find calibration gaps before they affect production. Lasso also offers offline analysis on historical content to help find gaps and tune the rule configuration.
For a full walkthrough of Lasso's rule capabilities, see the custom moderation rules documentation.
Once your rules are rebuilt and tested against historical content, you're ready to start routing live traffic. That's week two.
The 3-week migration plan
Lasso's initial integration takes about two days. The rest of the timeline is parallel testing, rule calibration, and a controlled cutover. That's where you want to spend the time.
A 3-week migration plan
Week 1: Audit, set up, and rebuild your rules
Document every CS endpoint you call, every custom rule you've configured, every language you moderate, and every webhook event type your application handles. Set up your Lasso account and API key. The initial integration takes about two days. Rebuild your CS rule logic in Lasso's rule interface and run your first historical content tests to catch obvious calibration gaps early.
Week 2: Parallel testing and threshold calibration
Route a sample of live content to both Community Sift and Lasso simultaneously. Compare moderation outcomes, false positive rates, and latency. Focus especially on the edge cases your community generates: leet-speak evasion, context-dependent content, language-specific patterns. Adjust Lasso rule thresholds based on what you find. Don't skip this step: it's where most of the real calibration work happens.
Week 3: Cutover and post-migration review
Route increasing percentages of live traffic to Lasso. Start at 20–30%, monitor your moderation metrics, then move to full cutover once confidence is high. Decommission your Community Sift integration but keep your CS credentials accessible for a few weeks in case you need to reference historical data. At the end of the week, review catch rate, false positive rate, queue volume, and moderator throughput against your CS baseline.
What to expect when you're live on Lasso
A few things will feel immediately familiar. The fundamental architecture is the same: content in via API, AI processes it, actions come back, human reviewers work a queue, rules are configurable without engineering involvement.
The dashboard is different. Lasso's UI is its own design, not a reskin of Community Sift's. Your moderation team will need a short onboarding period, but Lasso is rated 4.9/5 on G2 for ease of use, which tends to make that curve short.
Rule changes go live immediately with no deployments. Lasso's AI Moderators also self-improve over time: every flagging decision your human moderators make is automatically fed back into what the AI filters going forward. The longer your team uses it, the more accurate it becomes to your specific community.
The one genuine gap to plan around: Lasso doesn't replicate CS's User Reputation states. If your community safety strategy relied on the three-tier trust promotion system, you'll need to design an approximation with Lasso's custom rules, or decide whether that specific behavior was truly necessary for your use case.
Getting help
We've worked through this migration with enough teams to know where things get complicated. Migration support is free.
The fastest path is to book a free migration consultation. Bring your CS integration documentation and a list of any custom classifiers or rule logic you've built. We'll spend 30 minutes mapping your setup to Lasso's equivalents and flagging anything that needs special attention.
If you prefer to work async, reach us at info@lassomoderation.com. For documentation, start with the Lasso API reference and the custom moderation rules guide.
If you're still weighing your options before committing to a migration path, the Community Sift alternatives guide lays out the full landscape.
The information about Community Sift features in this article is based on the Two Hat and Community Sift public website and API documentation archived prior to Microsoft's acquisition. We may have missed details or edge cases. If something is wrong or outdated, please flag it at info@lassomoderation.com.
How Lasso Moderation Can Help
At Lasso, we believe that online moderation technology should be affordable, scalable, and easy to use. Our AI-powered moderation platform allows moderators to manage content more efficiently and at scale, ensuring safer and more positive user experiences. From detecting harmful content to filtering spam, our platform helps businesses maintain control, no matter the size of their community.
Book a demo here.
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