Approach · spec-driven development

Spec-driven development, executed by agents.

I don't prompt-and-pray. Every app I build — SaaS, RAG systems, dashboards, mobile apps, automations — starts as a precise, versioned spec. Agentic coding tools then implement it task-by-task while I own the architecture and review. Specs stay the single source of truth, so speed never costs quality.
  1. Spec
  2. Architecture
  3. Tasks
  4. Agents
  5. Verification
  6. Production

The architecture engine

Eight stages. One shared spec.

Each stage adds a module to the system on the right. Agents accelerate the build; the spec, the architecture and the review stay human-owned.

  1. 01

    Discovery

    A discovery call and scope mapping: the business goal, today's workflow, the data available and where AI or automation actually pays back.

    output · Scope map

  2. 02

    Specification

    Turn the business idea into a precise, versioned spec — goals, constraints, data model, and acceptance criteria.

    output · spec.md

  3. 03

    Architecture

    Break the spec into an architecture and a technical plan the agents and I both work against, prototyped where a decision needs proof.

    output · Architecture plan

  4. 04

    Task decomposition

    Decompose the plan into small, verifiable tasks with clear inputs, outputs, and done-conditions.

    output · Task list

  5. 05

    Agentic implementation

    Agentic coding tools implement task-by-task against the spec while I steer architecture and review.

    output · Commits per task

  6. 06

    Review & verification

    Every task is validated against acceptance criteria and tested. Nothing merges because an agent says it is done.

    output · Tests + review

  7. 07

    Deployment

    Integrated, hardened and deployed as a production-ready increment through CI/CD.

    output · Release

  8. 08

    Ongoing improvement

    Launch, monitor real usage and improve: logs, dashboards and the next spec revision.

    output · Next iteration

spec/invoice-ocr.md
# Feature: Invoice OCR Pipeline## GoalExtract line items from PDFs -> validated JSON## Constraints- 99% field accuracy, response < 2s- Schema-validated, audit-logged## Acceptance- [x] Parse messy scanned invoices- [x] FastAPI + tests scaffolded- [ ] Deploy to staging→ agent: implement task 3 against spec
Example spec file used to drive agentic implementation.

Why a spec

Speed that never costs quality.

  • One source of truth: the spec, not tribal knowledge
  • Agents ship faster without drifting from intent
  • Predictable, reviewable, production-grade increments
  • Same rigor for web apps, SaaS, RAG, mobile & automations
Build With SDD

Prompt engineering + AI coding tools

Rapid agentic coding.

Superfast product design and development

Kingshuk combines prompt engineering with agentic coding tools to compress product development cycles. This helps move faster from business idea to architecture, UI, backend, AI workflow, testing, and deployable MVP.

  • AI-assisted architecture planning
  • Rapid UI and component generation
  • Backend/API scaffolding
  • Debugging and refactoring acceleration
  • Faster MVP prototyping
  • Automation-first development workflow

The agentic coding stack · right tool per task, orchestrated against one spec

  • Claude Code

    Primary agentic pair-programmer

  • Codex

    OpenAI code generation & review

  • GitHub Copilot

    Inline completion & chat

  • Cursor

    Spec-aware AI IDE

  • Gemini CLI

    Long-context planning agent

  • Windsurf

    Agentic flow IDE

  • Cline

    Autonomous coding agent

  • Replit Agent

    Cloud build & deploy agent

  • Aider

    Git-native AI pair-programmer

  • v0

    AI UI & component generation

  • Plan

    Architecture Copilot

    AI-assisted system design, data modelling, and API contracts before a line of code is written.

  • Build

    Rapid Scaffolding

    Agents generate UI, backend, and AI-workflow boilerplate from the approved spec in hours, not weeks.

  • Ship

    Automated Refactoring

    Continuous refactoring, test generation, and debugging accelerated by agentic coding loops.

  • Scale

    MVP Acceleration

    Structured spec-to-MVP delivery that keeps velocity high without sacrificing production quality.

Build Faster With AI

Engagement models

Premium AI builds without agency bloat.

  • 4–8 weeks

    AI SaaS MVP

    For founders who want to launch a custom AI SaaS product fast.

    Discuss MVP

  • 3–4 weeks

    Enterprise RAG System

    For teams with internal documents, policies, contracts, support data, or compliance workflows.

    Build RAG System

  • 1–3 weeks

    AI Automation Sprint

    For businesses that want to automate sales, reporting, follow-up, support, or operations.

    Automate My Business

  • 5 days

    AI Readiness Audit

    Identify 3–5 automatable workflows, ROI opportunities, and an MVP-first roadmap.

    Start Audit
The technology behind it

One message is enough

Bring the idea. Leave with a spec.

No forms, no booking calendars. Send the idea the way you would to a friend; Kingshuk replies personally.

Start with a spec Email the idea