Our Solutions

ReqFlow removes ambiguity from raw requirements and converts them into structured outputs such as user stories, acceptance criteria, test cases, and PRDs. It also fills gaps left by traditional requirements tools by adding AI assistance, governance, and traceability.

It uses a five-phase workflow - Extraction, Questions, Refinement, Structuring, and Generation - to break down raw requirements and rebuild them into precise, actionable specifications. The platform combines a web app with a core system of specialized AI agents, commands, and skills, governed by mandatory validation gates and traceability rules.

ReqFlow improves requirements quality by making them clear, feasible, testable, and implementation-ready before development begins. It also delivers major efficiency gains, including a measured 90% reduction in the time needed to produce clear requirements, user stories, and test cases on real projects.

ArchFlow transforms raw requirements, a PRD, or an existing codebase and architecture into structured architecture outputs such as an Architecture Overview Document, a Full Software Architecture Document, and an Implementation Plan. It also produces specialized sub-agents that support execution of tasks across backend, frontend, infrastructure, security, integration, and code review. In brownfield scenarios, it can scan the current repository and architecture landscape, generate the standard ArchFlow deliverables from that analysis, and produce a modernization proposal based on the current-state assessment.

It runs architecture development through concurrent work streams, continuous governance, and AI-assisted validation to keep all design, security, and infrastructure artifacts aligned. For existing systems, it analyzes the current repository, architecture, and implementation patterns to reconstruct the architecture baseline, identify gaps and improvement opportunities, and use those findings as input for modernization planning. In its later phases, it invokes GitHub Spec Kit for task execution and implementation, with role-specific agents using the most appropriate LLM for each job.

ArchFlow speeds up the design cycle, improves feedback loops, and increases consistency through AI-driven quality checks and mandatory governance. It also helps organizations assess and modernize existing systems in a structured way, while ensuring compliance with key standards and supports Architecture-as-Code, making architecture easier to govern, reuse, and implement reliably.

FeatureFlow helps teams ship new features more reliably by turning feature work into a gated process with explicit research, planning, validation, and implementation steps. It improves on default agent loops by adding stronger structure, falsifiable planning, and domain-specific controls.

It uses a staged workflow derived from QRSPI, with six commands, multiple specialized agents, layered research, declarations, anti-pattern checks, and blocking validators. The framework keeps context focused, applies phase-specific skills, and enforces semantic and process gates before implementation can proceed.

FeatureFlow reduces ambiguity, prevents invalid plans from reaching implementation, and improves code quality in complex existing codebases. It also makes AI-assisted feature development more portable, testable, and scalable than unstructured agent workflows.