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Built for the Anthropic + Claude Code era
AccelerateAI

From idea to pull request — autonomously.

Anthropic-powered execution with Claude Code orchestrating context, planning, implementation, validation, and pull request creation across real codebases.

Claude Code
Explore the framework See how it works
Claude Partner Network
Claude Partner Network

Qubika is a Preferred Partner in the Claude Partner Network Services Track. Our team includes Claude Certified Architects, AI Engineers, and Forward-Deployed Engineers.

Our Claude-powered AccelerateAI framework combines proven engineering workflows, AI-driven methodologies, and enterprise guardrails to transform software delivery.

At its core is Qubika's next-generation autonomous delivery system, where Claude orchestrates end-to-end workflows - from ticket to pull request - with minimal human intervention while maintaining security, quality, and governance.

Operator model

Four stages from repository to reviewed pull request.

AccelerateAI follows a structured operator loop: understand the codebase, formalize the spec, execute autonomously, and hand back to the engineer only when it matters.

Anthropic + Claude Code Operator model
1
Repository context
Claude Code reads the codebase, architecture, dependencies, and conventions.
2
Spec formalization
Rough product ideas become structured, spec-driven development tickets.
3
Autonomous execution
The framework implements, tests, validates, documents, and prepares the PR.
4
Human oversight
When ambiguity is high, the loop returns to the engineer instead of guessing.

Why now

Software development is moving from AI assistance to AI execution.

Before
AI assists.

The engineer still performs the workflow end to end.

After
AI executes.

The engineer reviews, approves, and intervenes where judgment is required.

The real cost of delivery is not only in writing code. It lives in the handoffs between product, engineering, security, QA, and operations. The opportunity is to compress those transitions into one continuous, context-aware execution system.

The solution

A framework that adapts to the repository, not the other way around.

AccelerateAI analyzes the codebase, creates project-aware agents and skills, turns vague requests into implementation-ready specs, and carries work forward through testing, validation, documentation, and PR creation.

Stack-aware from day one

The framework scans the repository, detects the tech stack, architecture patterns, and coding conventions, then configures project-specific agents and skills around the codebase it finds.

Built for each codebase

A React app, a Swift iOS product, and a multi-service backend do not need the same behavior. The framework adapts to the project instead of forcing one generic workflow.

Human-in-the-loop by design

When the system cannot confidently infer intent or implementation details, it stops and asks. Engineers stay in control while repetitive execution is delegated.

How it works

One continuous process. Three connected workflows.

01

Project Initialization

Once per project
  • Scans the repository, files, dependencies, and structure
  • Detects stack, architecture patterns, and coding conventions
  • Builds framework context with specialized agents
  • Generates project-specific skills, agents, and configuration
02

Create SDD Ticket

Per feature
  • Transforms a rough idea, Jira ticket, or prompt into a clear spec
  • Documents current state, desired behavior, affected areas, and implementation plan
  • Includes testing guidance when relevant
  • Turns ambiguous requests into execution-ready work
03

Implement Ticket

Per feature
  • Loads context and requirements from the SDD
  • Plans implementation steps based on stack and project conventions
  • Applies code changes, generates tests, validates results, and retries when needed
  • Prepares documentation and opens the pull request for engineer review

Execution model

Structured flow, flexible decisions.

Deterministic where it should be

The framework follows a clear order of operations so execution remains auditable and predictable across features.

Adaptive where it matters

Decisions still respond to the stack, architecture, conventions, and ambiguity of the specific project under implementation.

Implementation cycle

Impact

Redefining the role of the engineer in the delivery loop.

Qubika Anthropic certified Forward-Deployed Engineers transform & accelerate SDLCs with Claude

Spec-driven development requires first defining the problem & writing the specifications. We make sure you’re travelling in the right direction with emphasis on discovery to create a single-source of truth. This sets the stage for rapid AI-driven development.

  • Discovery

    Discovery

    Align business goals & identify high-impact opportunities.

  • Architecture Design

    Architecture
    Design

    Build modern, secure & scalable data foundations.

  • Build

    Build

    Develop & deploy AI solutions tailored to your business.

  • Maintain & Future Backlog

    Maintain &
    Future Backlog

    Operate, optimize & scale for lasting impact.

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Learn more about Qubika FDEs

Closing perspective

The question is no longer whether AI can help development.

The real question is how much of the software delivery workflow can be confidently handed off while preserving context, quality, and engineering control. AccelerateAI answers that with a repository-aware, Claude Code-centered execution model.

Outcome
From rough input to production-ready pull request
Autonomous where possible. Human-guided where necessary.

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