The only tool that audits your filing for weakness. SpaceLizit flags logical inconsistencies, missing evidence, and vague language — so your petition is bulletproof before it reaches the USCIS officer's desk.
Law firms using SpaceLizit's pre-filing AI audit consistently see USCIS RFE rates drop from 20–30% to under 3%.
AI trained on thousands of real USCIS denials catches mismatched dates, A-Number typos, and vague specialty occupation language instantly.
SpaceLizit's RFE engine is updated weekly with new USCIS adjudication trends from premium processing data — always current.
Verifies every required initial evidence document is present before filing — support letters, LCA, educational credentials, employer records, and itineraries.
AI scores your specialty occupation support letter for clarity, persuasiveness, and USCIS-required elements — flags vague or weak language before filing.
If you receive an RFE, SpaceLizit analyzes the officer's query and drafts a targeted response with a precise supporting documentation checklist.
USCIS issues a Request for Evidence when an officer determines that the petition does not yet establish eligibility. Understanding the most common triggers is the first step to preventing them.
The most common H-1B RFE trigger. Officers flag job descriptions that don't clearly require a specific bachelor's degree or higher in a related specialty. Generic IT or consulting roles are especially vulnerable.
For third-party placement and consulting roles, USCIS requires detailed evidence that the petitioner controls and directs the beneficiary's work. Missing MSAs, SOWs, or end-client letters trigger RFEs.
H-1B petitions for consulting or multi-location work must include a valid itinerary of services. Missing start/end dates, locations, or client details will generate an immediate RFE.
Mismatches between the LCA wage level, SOC code, work location, or employment period and the I-129 petition are a frequent source of RFEs and can result in denial.
Support letters that don't explicitly connect each job duty to the beneficiary's specific degree and specialty are heavily scrutinized. Generic descriptions borrowed from job boards don't meet the USCIS standard.
Any gap in the beneficiary's employment or immigration history that isn't explained in the petition will be flagged. Officers look for continuous status and employer authorization.
Degrees from foreign universities must be evaluated for US equivalency. Missing evaluations, insufficient combination of degree + experience calculations, or unaccredited evaluators all generate RFEs.
Traditional petition review relies on attorney experience and memory. AI-powered pre-filing audits apply consistent, comprehensive checks across every case — every time.
| Check | Manual Attorney Review | SpaceLizit AI Audit |
|---|---|---|
| Specialty occupation analysis | Subjective — varies by attorney experience | ✅ Scored against 10,000+ USCIS adjudications |
| LCA vs I-129 consistency check | Manual cross-referencing — error-prone | ✅ Automated field-by-field comparison |
| Support letter strength scoring | Qualitative — no benchmark | ✅ AI scores clarity, persuasiveness, USCIS criteria |
| Document completeness verification | Checklist-based — easily missed | ✅ Automated against current USCIS initial evidence list |
| Itinerary validity check | Manual review if remembered | ✅ Checks dates, locations, client details automatically |
| Degree equivalency flag | Depends on attorney knowledge | ✅ Flags missing evaluations and unaccredited evaluators |
| Employment gap detection | Requires careful reading | ✅ Scans entire employment history automatically |
| Updated USCIS trend awareness | Ad hoc — based on recent cases handled | ✅ Updated weekly from premium processing trends |
| Time per petition | 45–90 minutes of attorney review | ✅ Under 5 minutes — automated |
| Consistency across all cases | Varies by reviewer and workload | ✅ Identical rigorous check every time |
A 5-step pre-filing audit that runs automatically on every petition before your firm submits to USCIS:
Attorney uploads the draft I-129, support letter, LCA, educational credentials, and supporting documents to SpaceLizit.
SpaceLizit's OCR and NLP engine reads every document — extracting job duties, wage data, degree fields, employment dates, and employer details automatically.
The structured petition data is compared against 10,000+ USCIS RFE and denial patterns across H-1B, L-1, and O-1 categories. Risks are scored and ranked by severity.
SpaceLizit generates a plain-English audit report: each flagged issue, its RFE risk level, the specific USCIS policy it violates, and the exact fix recommended.
Attorney resolves flagged items — SpaceLizit can regenerate the support letter or specific sections. Re-run the audit to confirm all issues are resolved before filing.
SpaceLizit's AI is trained on USCIS policy memoranda, the USCIS Policy Manual, AAO decisions, and federal court rulings on H-1B specialty occupation. Key guidelines the AI monitors:
Specialty occupation definition — requires at least a bachelor's degree or equivalent in a specific specialty. SpaceLizit verifies the support letter explicitly maps every job duty to this standard.
H-1B employer-employee relationship requirements. SpaceLizit checks that petitioners for consulting/staffing roles include sufficient end-client evidence, SOWs, and itineraries.
Material change doctrine for H-1B amendments. SpaceLizit flags situations where a new LCA may be required due to worksite changes.
USCIS's own published guidance on frequent RFE triggers. SpaceLizit's audit checklist mirrors this list exactly and is updated when USCIS publishes policy changes.
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