Asmin Jismun

# DeploySift — Every clue. One diagnosis. **DeploySift is a multimodal AI-powered deployment investigation platform that helps developers understand why applications fail in production by connecting evidence scattered across project configuration, application logs, hosting environments, and browser network errors.** A deployment failure rarely tells the whole story. A project may work perfectly in development but fail after deployment because of incompatible runtime versions, environment configuration, database connectivity, or frontend build issues. Developers often have to inspect multiple files, logs, and hosting settings manually to discover how these problems are connected. DeploySift addresses this challenge through an evidence-first investigation workflow. Developers can upload deployment evidence, including project configuration files, application logs, hosting information, and browser network diagnostics. The platform processes these inputs, extracts relevant technical observations, and organizes them into a structured investigation. Using Meta Muse models for supported multimodal analysis, DeploySift helps interpret evidence, identify relevant relationships, and explain potential deployment problems. Rather than treating isolated errors as definitive root causes, it distinguishes observed facts from hypotheses, highlights missing information, and connects diagnostic conclusions to their supporting evidence. Its key capabilities include: - **Multisource Evidence Ingestion:** Collect and organize deployment-related files and diagnostic information in one investigation. - **Multimodal AI Analysis:** Apply Meta Muse capabilities to interpret supported evidence and extract meaningful observations. - **Cross-Evidence Correlation:** Connect project requirements, hosting configurations, application logs, and browser errors to identify potential inconsistencies. - **Evidence-Backed Diagnosis:** Present findings with traceable supporting evidence instead of relying solely on generic AI-generated suggestions. - **Transparent Investigation:** Separate confirmed observations, potential causes, and missing evidence to reduce unsupported conclusions. - **Actionable Remediation:** Help developers understand what to investigate and what corrective steps may be appropriate. The initial implementation focuses on Laravel and PHP deployment environments, with practical scenarios including runtime compatibility mismatches, database connection failures, and frontend build issues. DeploySift aims to make production troubleshooting more systematic, explainable, and efficient. By bringing scattered technical clues into one evidence-driven workflow, it helps developers move from guesswork toward clearer, more informed deployment decisions. **Every clue. One diagnosis.**