Blueprint & Specs
SPINLAP // AI Engineering Studio
BUILD SOFTWARE
THAT THINKS.
AI Agents. Automation. Intelligent Systems. Production Software.Built by engineers—not marketers._
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async function buildSolution(problem) {
const ai = new Agent();ai.reason(problem);ai.use(tools);ai.automate(workflow);return ai.deliver(results);}
AI
</>
API
import { solve } from 'ai';
solve(problem)
.with(memory).with(tools).execute();> Engineering Intelligence.
Production-grade AI systems and custom backend software.
{
"studio": "SPINLAP",
"focus": "Production-Grade AI Systems",
"capabilities": [
"Intelligent Agents",
"Workflow Automation",
"RAG & LLM Integration",
"MCP server connectors",
"Cloud-native scalable backends"
],
"uptime": "100% focused on latency and security boundaries",
"engineering_first": true
}
"studio": "SPINLAP",
"focus": "Production-Grade AI Systems",
"capabilities": [
"Intelligent Agents",
"Workflow Automation",
"RAG & LLM Integration",
"MCP server connectors",
"Cloud-native scalable backends"
],
"uptime": "100% focused on latency and security boundaries",
"engineering_first": true
}
Serious AI systems—not just templates.
Spinlap builds production-grade AI systems and software. From intelligent agents to cloud-native applications, we design technology that solves real business problems.
We operate under an engineering-first directive. We do not sell visual hype, nor do we slap "AI stickers" onto legacy APIs. We build core cognitive layers, implement model routers, set safety guardrails, and structure scalable databases that hold context securely.
No hype. No AI stickers. Just engineering.
Spinlap has shipped AI systems across industries including real estate intelligence (HomeShow.ai), sports data analytics (SICScore.com), and ride-sharing infrastructure (DRIFE). Our stack spans OpenAI GPT-4, Anthropic Claude, Google Gemini, LangChain, pgvector, and Kubernetes — deployed on AWS and GCP.
🤖
“He shipped the core LLM querying agents for DataCube AI in 3 weeks. What would have taken traditional agencies months was completed in days using agentic code synthesis.”
☁️
“Our auto-scaling cloud deployment and secure payment systems were configured in under 48 hours. His DevOps telemetry saved us thousands in AWS resources.”
⚡
“70% cheaper than our previous quote and infinitely faster. He successfully integrated real-time AI transparency queries into our database indices.”
💳
“The deal appraisal and SaaS project management dashboard was built with incredible speed. We had auth, billing, and the app running in 10 days.”
🐳
“Optimized our high-availability PostgreSQL clusters and container orchestration. He uses SRE agents to monitor and auto-tune Kubernetes pods.”
Active Deployments
No standard case studies. Real technical receipts. AI platforms, SaaS products, and cloud infrastructure — shipped and running live.
AI
DataCube AI
An enterprise-grade AI data analytics and business intelligence platform that automates database querying and visualization using LLM agents.
architecture: LLM Agent Pool ➔ LangChain Router ➔ pgvector Embedding Cache ➔ RDS Output Visualizer
ai_workflow: User Query ➔ Intent Classifier agent ➔ Schema Explorer agent ➔ SQL Generator agent ➔ Execution & Render
impact: Client operational efficiency increased by 92%. Zero manual SQL queries needed for standard reporting.
AI
Home Show
A smarter, simpler marketplace platform to buy, sell, book, rent, or host homes, services, and local events with AI-powered transparency and speed.
architecture: Next.js Server Actions ➔ Cognitive LLM Parser ➔ MongoDB Atlas Vector Search ➔ WebSockets Router
ai_workflow: Natural Language Spec ➔ Context Embedding generation ➔ Vector Similarity Search ➔ Dynamic Filters compilation
impact: User booking conversions increased by 38% within 30 days of agent integration.
Web & Mobile
My PDA
My Property Development Assistant: a quick deal appraisal and project management SaaS application designed for property developers.
architecture: Laravel Gateway ➔ React App Engine ➔ Stripe Subscriptions manager ➔ Deal Appraiser logic
ai_workflow: Appraisal request ➔ Automatic financial parameters evaluation ➔ Cashflow forecast engine ➔ PDF compilation
impact: Appraisal creation time reduced from 4 hours to 90 seconds. Over 1,200 deals appraised live.
Web & Mobile
Sports Injury Central
A comprehensive sports injury guidance, prevention, and rehab resource platform built for elite head team physicians (NFL, NBA, MLB) and athletes.
architecture: Laravel API ➔ Firebase Real-time DB ➔ React Native mobile clients ➔ Stripe subscriptions gateway
ai_workflow: Injury logging ➔ Anatomical mapping engine ➔ Rehabilitation protocol lookup ➔ Physician alert trigger
impact: Active support for 300+ professional athletes. Diagnostic lookup reduced to single-tap action.
Web & Mobile
TouchNote
A global card-sending e-commerce web and mobile app allowing users to design and order customized physical greeting cards.
architecture: Microservices Gateway ➔ React Design canvas ➔ MongoDB Asset Store ➔ Printing API integration
ai_workflow: Canvas upload ➔ Vector image rasterizer ➔ Print resolution compiler ➔ Shipping router queue
impact: Millions of physical cards printed and shipped worldwide. High reliability on seasonal peak traffic.
Cloud/Infra
BookMyCarNow Infrastructure
Designed and implemented the auto-scaling cloud deployment, Nginx load balancing, and secure payment gateway integrations for the BookMyCarNow booking engine.
architecture: AWS Route 53 ➔ Nginx Load Balancers ➔ Auto-scaling EC2 instances ➔ RDS Multi-AZ PostgreSQL
ai_workflow: Booking Request payload ➔ Rate-limit checker ➔ Transaction execution ➔ Database replica write-commit
impact: Survived peak booking traffic load spike of 12,000 requests/min without a single packet dropped.
Cloud/Infra
DRIFE Infrastructure
Architected and managed the scalable cloud infrastructure, Docker containers, and high-availability PostgreSQL server clusters for DRIFE's ride-hailing platform.
architecture: Docker Swarm cluster ➔ PostgreSQL Master-Replica sync ➔ Redis caching cluster ➔ GitHub Actions CI/CD
ai_workflow: Code push ➔ Automated unit validation ➔ Container rebuild ➔ AWS cluster rolling updates trigger
impact: Maintains high-availability rides dispatch database for thousands of active drivers.
Compiled Toolkit
Every studio says they use AI. We mean it differently. We don't use AI to write emails. We orchestrate LLM agents to handle boilerplate, run test coverage, and automate deployments—allowing our architects to focus entirely on robust system design, API safety bounds, and high-performance queries.
Claude CodeAutonomous developer agent
Cursor IDEAI-native code environment
GPT-4 / ChatGPTArchitecture & schema design
Gemini ProMulti-modal model integration
v0 by VercelUI component generation
Bolt.newFull-stack app scaffolding
GitHub CopilotInline code completions
Docker + K8sContainer orchestration
TerraformInfrastructure as code
Vercel / AWSEdge & cloud deployment
The Compile Protocol
Your last agency gave you a roadmap. We give you a pipeline. Here is how we compile specifications into running systems.
Architecture Layout
Structuring database schemas, API endpoints, security bounds, and state machines.
Code Synthesis
Orchestrating agent networks to write standard-compliant, performant code blocks.
Swarm Verification
Agents writing test cases for agents. Unit, integration, and load test runs.
Orchestration & Deploy
Terraform runs, docker builds, and zero-downtime rolling deploys to Vercel/AWS.
Self-Healing Telemetry
Configuring server watchdogs that monitor memory and database queries automatically.
What We Build
We build production-grade AI systems, server integrations, and scalable software architectures designed to solve complex business problems.
AI Agents
Engineers that never sleep. Autonomous systems that reason, use tools, and complete work.
Claude Code · GPT-4 · Swarm NetworksAutomation
Turn repetitive workflows into one-click operations.
Node.js · Event Loops · REST/WebhooksAI Solutions
RAG. LLMs. MCP. Voice AI. Custom AI products built for real use cases.
pgvector · LangChain · OpenAI · GeminiSoftware Engineering
Modern web applications designed to scale from day one.
React · Next.js · Postgres · RedisCloud Infrastructure
Deploy. Monitor. Scale. Secure. Cloud-native from the ground up.
Terraform · Docker · Kubernetes · AWS❯ spinlap deploy --agent-swarm --mcp-server-connect
🤖 [Orchestrator] Spinning up 4 autonomous developer agents...
✓ Active context loaded from vector database indexes (RAG mode)
✓ Scaffolding MCP (Model Context Protocol) server pipelines
✓ Connecting OAuth security guardrails and tokens tracking
✓ Merged 18 unit tests into production database migration
✓ Active server clusters online: status OK.
Frequently Asked Questions
Common questions about our AI engineering services.
What are autonomous AI agents and how do they work?
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Autonomous AI agents are software systems that use large language models (LLMs) to reason, plan, and execute multi-step tasks with minimal human input. They call external tools, manage context across steps, and make decisions — replacing manual workflows with intelligent automation.
How does Spinlap automate business workflows?
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We analyze your existing processes and replace repetitive manual steps with event-driven automation pipelines using Node.js, webhooks, and REST integrations. The result: faster execution, fewer errors, and your team freed from manual tasks.
What custom AI solutions does Spinlap build?
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Spinlap builds production-ready AI products including RAG (Retrieval-Augmented Generation) systems, LLM-powered chatbots, voice AI interfaces, and Model Context Protocol (MCP) server integrations that connect LLMs to enterprise data sources.
What software engineering services does Spinlap offer?
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We build full-stack web applications using React, Next.js, PostgreSQL, and Redis — designed for scale from day one. Our engineering-first approach means clean architecture, proper testing, and systems that can grow with your business.
How does Spinlap handle cloud infrastructure and DevOps?
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We design and deploy cloud-native infrastructure using AWS, Terraform for infrastructure-as-code, Docker containers, and Kubernetes orchestration. Every deployment includes monitoring, auto-scaling, and security hardening.
What makes Spinlap different from other AI development agencies?
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Spinlap operates under an engineering-first directive — no discovery call loops, no sales theater, and no "AI stickers" on legacy systems. We build core cognitive layers, implement model routers, set safety guardrails, and structure scalable databases. Every system we ship is production-grade: tested under real load, with real failure handling.
How do I start a project with Spinlap?
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Use the System Specification Calculator on this page to describe your project. We review every submission and respond within 24 hours with a technical assessment — no NDAs required to start the conversation. Email us directly at akash@spinlap.com.
Meet the Engineer Behind Spinlap
Building autonomous agents and robust cognitive architectures.
Akash is an AI engineer who builds production-ready AI systems combining software engineering, automation, cloud infrastructure, and large language models. Based in Chandigarh, India — working remotely with clients worldwide.
My focus isn't adding AI to products. It's engineering systems that solve real business problems.
With years of experience engineering systems for high-performance companies, Spinlap was built to skip standard agency bureaucracy. We focus on zero discovery calls, zero sales-fluff, and pure execution.
- Specializes in multi-agent AI orchestration and RAG pipeline architecture
- Builds MCP (Model Context Protocol) servers for enterprise integrations
- Deploys AI systems on AWS, GCP with Terraform + Kubernetes
- Author of technical guides on AI agents, RAG, and production LLM systems
Currently Building
[System Telemetry: Shipped and Active Dev Tasks]
✓Multi-Agent Systems
✓Model Context Protocol (MCP)
✓RAG Applications
✓AI Coding Agents
✓Enterprise Automation
✓Intelligent Workflows
Have an impossible idea?
Perfect. Those are our favorite. Run the spec calculator below to compile scope metrics and initiate a build request.
> select --target
> select --complexity
{
"target_architecture": "SaaS Platform",
"scope_complexity": "Medium Scale",
"metrics": {
"autonomous_build_time": "21 Days",
"traditional_agency_time": "~18 Weeks",
"estimated_development_cost": "$12,000",
"traditional_agency_quote": "$52,500"
},
"efficiency_ratio": "6x faster delivery",
"net_financial_saving": "$40,500 (77%)",
"compilation_status": "SUCCESS"
}
"target_architecture": "SaaS Platform",
"scope_complexity": "Medium Scale",
"metrics": {
"autonomous_build_time": "21 Days",
"traditional_agency_time": "~18 Weeks",
"estimated_development_cost": "$12,000",
"traditional_agency_quote": "$52,500"
},
"efficiency_ratio": "6x faster delivery",
"net_financial_saving": "$40,500 (77%)",
"compilation_status": "SUCCESS"
}
✓ COMPILATION COMPLETE
Ready to execute. Submitting builds estimate config files...