Synlets

Product Introduction

1. Definition: Synlets is an autonomous AI agent platform for software development, operating within the AI-powered DevOps and workflow automation category. It connects to existing tools (Jira, GitHub, GitLab, Confluence) to transform high-level task descriptions into technically implemented pull requests (PRs) with minimal human intervention.
2. Core Value Proposition: Synlets exists to eliminate engineering bottlenecks by automating the full development cycle – from ticket creation and technical implementation to PR generation and preliminary code review. Its primary function is to act as an autonomous AI team member, significantly increasing engineering output without additional headcount by handling routine coding tasks.

Main Features

1. Autonomous Ticket Implementation:
   • How it works: Users create tickets in connected systems (Jira, Asana, Linear) or directly in Synlets using plain language. AI agents ingest the ticket, analyze linked code repositories (GitHub/GitLab), reference documentation (Confluence/Notion), and generate functional code changes. Agents create a GitHub/GitLab Pull Request with the implemented solution.
   • Technology: Utilizes Claude models (Haiku, Opus) for task comprehension, planning, and code generation. Operates on an ACU (AI Compute Unit) credit system, with consumption based on task complexity. Code access is ephemeral – retrieved at runtime and discarded post-task.
2. Context-Aware Ticket Creation:
   • How it works: Allows non-technical users (PMs, Execs) to draft tickets. Agents automatically enrich ticket descriptions by pulling context from the linked codebase, documentation, and historical tickets, reducing ambiguity and back-and-forth. Creates technically sound tickets ready for assignment to AI or human engineers.
   • Technology: Leverages RAG (Retrieval-Augmented Generation) over connected knowledge sources (Confluence, Notion, Google Docs) and codebase analysis to provide relevant context to the AI model during ticket drafting.
3. Automated PR Review & Management:
   • How it works: AI agents perform preliminary code reviews on human-created PRs or iterate on their own PRs based on human feedback. Reviews enforce coding standards, security policies, and architectural guidelines. Agents can autonomously push fixes and update PRs in response to review comments.
   • Technology: Combines static code analysis principles with LLM-based semantic review (Claude Opus for complex reasoning). Integrates directly with GitHub/GitLab PR workflows.
4. Insights & Reporting (Beta):
   • How it works: Generates self-serve reports on project status, code quality (e.g., potential tech debt), security posture, and team velocity (e.g., PR merge rate, ACU consumption). Provides AI-generated recommendations for improvements.
   • Technology: Aggregates data from connected development tools and agent activities, analyzed by Claude models to generate actionable insights and summaries.

Problems Solved

1. Pain Point: Severely constrained engineering bandwidth causing delays (e.g., "2 weeks for a button fix"), backlog accumulation, and blocked product teams ("73% time blocked").
   • Target Audience: Engineering Leads, CTOs, Product Managers, Founders of startups to mid-sized companies.
   • Use Case: Automating bug fixes, minor feature implementations, and refactoring tasks frees senior engineers for complex problems, accelerating overall delivery.
2. Pain Point: Inefficient ticket lifecycle involving hours of ticket writing, days of clarification, and dependency on specific engineers ("∞ back-and-forth", "Vague tickets").
   • Target Audience: Product Managers, Non-Technical Executives, Operations Teams.
   • Use Case: Enabling PMs/Execs to create well-defined, technically contextualized tickets in minutes and assign directly to AI agents for same-day implementation, bypassing the engineering queue.
3. Pain Point: Lack of visibility into engineering progress and quality ("Engineering progress is a black box", "0 self-serve insights").
   • Target Audience: Executives, CTOs, Security/Compliance Officers.
   • Use Case: Providing automated, real-time reports on project health, code quality metrics, security compliance status, and delivery velocity without requiring manual updates from engineers.
4. Pain Point: PR review bottlenecks slowing down developer iteration.
   • Target Audience: Software Developers, Engineering Managers.
   • Use Case: AI agents providing thorough preliminary PR reviews 24/7, catching basic issues and ensuring standards compliance before human review, reducing iteration time.

Unique Advantages

1. Differentiation:
   • vs. Coding Assistants (Copilot/Cursor): Synlets is not an in-IDE helper; it's an autonomous agent handling the entire workflow (ticket -> code -> PR) externally.
   • vs. Agentic Dev Tools (Devin): Focuses on integrating seamlessly with existing enterprise tools (Jira, GitHub, Confluence) and team workflows, acting as a team member augmenter rather than a standalone developer. Emphasizes security (ephemeral code access) and team control (PR gating).
   • vs. Task Bots: Handles complex technical implementation, not just simple automations or script generation.
2. Key Innovation:
   • ACU-Based Orchestration: A predictable consumption model for AI agent tasks, scaling cost with actual output (code changes, reviews, insights) rather than per-seat licensing.
   • Ephemeral, Secure Code Processing: Code is never stored; agents retrieve it transiently during task execution, addressing critical enterprise security concerns around IP leakage.
   • Deep Toolchain Integration: Native, bi-directional sync with industry-standard project management, code hosting, and knowledge base tools creates a closed-loop automation system within existing workflows.

Frequently Asked Questions (FAQ)

1. What AI models power Synlets agents and when should I use them?
   Synlets primarily uses Anthropic's Claude models: Claude Haiku for fast, cost-effective tasks (most implementations/reviews), and Claude Opus (4.5/4.6) for complex reasoning, architectural decisions, and high-stakes reviews. Users can select the model per project or task based on needs and ACU budget.
2. How does Synlets integrate with my existing Jira and GitHub workflow?
   Synlets connects via OAuth to your Jira/Asana/Linear instance to read and update tickets. It connects to GitHub/GitLab to read repositories, create branches, push code, and open/manage PRs. Status changes in Synlets (e.g., "PR Created") automatically sync back to your Jira ticket, maintaining workflow continuity.
3. Is my source code secure when using Synlets?
   Yes. Synlets employs a zero-persistence architecture for your code. Agents retrieve code directly from GitHub/GitLab only when needed for a specific task, process it in-memory within isolated environments, and discard all code artifacts immediately after task completion. Your source code never resides in Synlets' databases.
4. What happens if the AI agent makes a mistake in the code?
   All code changes are proposed via Pull Requests (PRs) in GitHub/GitLab. Human engineers must review and approve before merging. Synlets' PR Review agents also automatically analyze the PR for errors and standards compliance. If reviewers leave feedback, the agent iteratively updates the code and pushes new commits to the PR.
5. How is billing calculated with the ACU system?
   You purchase a monthly ACU (AI Compute Unit) allowance based on your plan. Each agent task (implementing a ticket, reviewing a PR, generating a report) consumes a predictable number of ACUs determined by the complexity of the task and the AI model used (Haiku = lower ACU, Opus = higher ACU). You only pay for the compute used during active tasks.