AI systems that actually ship.
AmrutamAI builds AI systems that make it to production. The flagship product is LedgerAI, a financial document intelligence platform powered by RAG pipelines. I also work with founders and teams to turn AI ideas into working software. No hype, no oversized teams, just focused engineering.
LedgerAI
Financial document intelligence platform
What I build
Services
From chatbots to custom SaaS - every solution is designed to integrate with your existing tools and run reliably in production.
AI Chatbots & Assistants
Customer support bots, internal knowledge assistants and AI copilots.
RAG Applications
Document search and knowledge systems powered by retrieval-augmented generation.
AI Agents, Automation & No-Code Workflows
AI agents and business process automation using tools like LangChain, CrewAI and n8n.
Custom AI Software
End-to-end AI SaaS products, dashboards and internal tools.
Selected Work
Real-world AI systems built for production.
LedgerAI
Financial document intelligence powered by RAG pipelines.
CropToolz
AI-powered agricultural expert reports with multi-agent intelligence.
ReWear
AI-powered recommendation engine for sustainable fashion.
Process
From discovery to deployment, with you at every step.
Discovery
Understand your goals, data, and constraints.
Planning
Architecture, timeline, and measurable outcomes.
Design
System design, UX flow, and API contracts.
AI Development
Building, training, and iterating on the AI layer.
Testing
Rigorous validation, edge cases, and performance tuning.
Deployment
Production release with monitoring and rollback plans.
Ongoing Support
Maintenance, updates, and continuous improvement.
Why founder-led
Direct collaboration.
No layers.
You work directly with the engineer building your system. Clear communication, practical architecture decisions, and transparent scope from day one.
- Direct access to the builder — no account managers
- Clean, maintainable architecture you can build on
- Practical solutions that ship, not slide decks
- Transparent scope and flat-rate pricing
Focused execution
Clear scope. Fast feedback cycles.
Security-conscious
Privacy and data integrity built in.
LLM expertise
RAG, agents, and prompt engineering.
Full-stack delivery
Frontend → backend → AI → deploy.
Tech Stack
Modern AI infrastructure.
FAQ
Common questions.
How long does an AI project take?
It depends on scope, but most projects land somewhere between 4 and 12 weeks. Smaller automation builds move faster, while deeper integrations need more planning. I'll give you a realistic timeline after the discovery call — and I'll stick to it.
Can you integrate AI into existing software?
That's actually most of what I do. Existing apps don't need a rewrite. I wrap AI capabilities in APIs or modular services that plug into your current stack. I've integrated into Django backends, FastAPI microservices, WordPress sites, and even Google Sheets workflows.