ARC IaC
Infrastructure as Code
Terraform blueprints designed for AI/ML-ready, secure, multi-cloud deployments. Automates the setup of compute, storage, observability, and compliance from day one, not retrofitted before go-live.
ExploreTen accelerators and agents that compound with every engagement.
Open-source ARC accelerators. Purpose-built AI agents. AWS-native agentic tooling. The system behind how SourceFuse builds, migrates, tests, and operates, deployed in every engagement, refined by every project.
Built for speed.
Hardened by reuse.
Powered by AWS.
Three categories of compounding IP and tooling, each earning its place by getting reused, refined, and re-deployed in the next engagement.
Accelerators
Battle-tested, reusable engineering components. Open-source where possible, deeply patterned where not. ARC IaC, ARC API, ARC UI/UX, ARC SaaS, each one eliminates a category of rebuild work on every project.
ARC API
Reusable Backend Microservices
A set of reusable, easy-to-deploy, pre-built microservices that power the backend APIs of applications. Standardises authentication, notifications, auditability, and multi-tenant data access across SaaS applications, integrated with AWS RDS, DynamoDB, Lambda, and API Gateway.
ExploreARC UI/UX
Frontend Boilerplates
Quick-start frontend boilerplates that give a smooth, consistent user experience. Pre-built components for admin apps, tenant portals, and product UIs, cloud-native, configurable, and aligned with ARC API on the backend.
ExploreARC SaaS
SaaS Control-Plane Accelerator
Production-ready SaaS control plane aligned to the AWS SaaS Builder Toolkit (SBT). Automates tenant onboarding, provisioning, lifecycle upgrades, identity, metering, and billing using Amazon EKS/ECS, Cognito, EventBridge, and AWS Marketplace Metering and Entitlements.
ExploreAgents
Purpose-built AI agents that work alongside SourceFuse delivery teams, and operate in clients production environments. NYX for AIOps and incident orchestration. PROTEUS for deterministic database migrations. ARGUS for QA automation and agentic testing.
NYX
AIOps Agent
Always-on intelligent ops layer powering SourceFuse's AI-led MSP. NYX is a multi-agent AI platform, built on LangGraph and LangChain that connects to Jira, GitHub, Confluence, Kubernetes, security scanners, cloud billing, and observability tools, reasoning across them to deliver natural-language RCA, evidence, and remediation guidance.
ExplorePROTEUS
Database Migration Agent
Deterministic AI-powered database migration agent designed specifically for SQL Server and Oracle → PostgreSQL modernisation. Replaces manual, error-prone processes with controlled, transparent, enterprise-ready conversion across assessment, conversion, and validation with 100% code conversion consistency and zero hallucinations.
ExploreARGUS
QA Automation Agent
Agentic test-case generation, continuous regression, and automated quality gates. ARGUS combines an AI-driven test design layer with SourceFuse's in-house Robot Framework + MCP automation engine, validating across web, mobile, API, ETL pipelines, and security wired into CI/CD for shift-left quality enforcement from the first sprint.
ExplorePartner Tooling
This is a new category not present in the v1 draft. AWS Agentic Tooling covers the AWS-native AI tools that SourceFuse embeds within its delivery model and deploys inside client AWS environments. These are not SourceFuse-built assets, they are Anthropic and AWS tools that SourceFuse has deep operational expertise with. Including them on this page signals AWS alignment depth and differentiates SourceFuse from partners who merely claim AWS expertise.
AWS Kiro
Agentic Software Development
AWS Kiro is an agentic IDE that takes software development from specification to implementation autonomously. Kiro uses spec-driven development, translating requirements into system design documents, task lists, and working code, with human review at defined checkpoints. SourceFuse uses Kiro within governed delivery workflows where spec-to-code velocity is required.
ExploreAWS Transform
Legacy Discovery & Modernisation Acceleration
AWS Transform is an agentic code transformation service that automates the discovery, analysis, and refactoring of legacy codebases. SourceFuse uses AWS Transform in the Discovery phase of legacy modernisation engagements to generate architecture documentation, extract development priorities, and compress Phase 1 from weeks to days.
ExploreHow They Compound
Every engagement makes the next one faster
Our accelerators and agents aren’t static products, they’re an operating system that improves with each project. Four mechanisms drive the compounding.
Open-Source Community
ARC is public on GitHub with 3.1M+ NPM downloads. Community PRs, real-world stress-tests, and external contributions sharpen the codebase between SF engagements.
Internal Dogfooding
NYX runs SF’s own operations. ARGUS tests SF’s own code. PROTEUS handles SF’s own database migrations. We use what we sell. Every defect we surface improves what clients get next.
Cross-Engagement Reuse
Patterns that prove themselves on one project get harvested into the next. Auth flows from a healthcare SaaS build land in a BFSI tenant portal three months later.
Client-Derived Sharpening
Every production deployment surfaces edge cases the lab can’t simulate. PROTEUS handles 5 new stored-procedure variants this month because last month’s migration exposed them.
When the stack doesn’t have an answer?
We build new ones
When a client engagement reveals a reusable pattern we don’t already have, we build it. Custom accelerators and agents, engineered for the engagement, then folded into the broader stack. The system gets sharper. Future clients inherit the work.
Lifecycle Mapping
Across the full lifecycle
Each stage of a SourceFuse engagement is powered by specific accelerators, agents, and AWS agentic tools. Here’s the mapping.
01. Understand
Architecture Documentation
Legacy discovery 02. Discover
Backlog Generation, Dependency Mapping 
DB Assessment API contracts, HLD documentation
03. Build
04. Intelligence
05. Run
Want to see them in action?
Start with a Discovery Sprint — 2 to 4 weeks. You’ll see the stack working on your problem, not on a slide.