You will get audit, fix, and optimization of your OpenClaw multi-agent setup
Rising Talent
Rising Talent
Project details
I've been working with OpenClaw since its Clawdbot days and have deployed it across personal automation and client-facing multi-agent setups. My typical production stack runs a Gateway process with a persistent orchestrator on Claude Sonnet routing to specialized Haiku-powered sub-agents for triage, digests, research, and scheduling, achieving around 40% cost reduction through tiered model routing without quality loss.
What sets this project apart is that I don't just configure OpenClaw. I diagnose and fix the specific failure modes that show up at scale: SOUL.md prompt drift, AgentSkill scoping conflicts, broken inter-agent communication, shared memory contention, and runaway cron costs. I've written custom AgentSkills for Google Workspace, internal APIs, and vector search, and I've handled multi-machine context sync for always-on setups.
Beyond OpenClaw, my day job is building production LLM orchestration systems at S&P Global, so I bring enterprise-grade thinking to reliability, observability, and cost control. Clients get a system that runs itself, not one they have to babysit.
What sets this project apart is that I don't just configure OpenClaw. I diagnose and fix the specific failure modes that show up at scale: SOUL.md prompt drift, AgentSkill scoping conflicts, broken inter-agent communication, shared memory contention, and runaway cron costs. I've written custom AgentSkills for Google Workspace, internal APIs, and vector search, and I've handled multi-machine context sync for always-on setups.
Beyond OpenClaw, my day job is building production LLM orchestration systems at S&P Global, so I bring enterprise-grade thinking to reliability, observability, and cost control. Clients get a system that runs itself, not one they have to babysit.
AI Algorithms
Generative Adversarial Network, Large Language Model, Multimodal Large Language Model, Recurrent Neural Network, Transformer ModelAI Applications
AI Chatbot, AI-Generated Code, AIOps, Anomaly Detection, Conversational AI, Natural Language Generation, Natural Language Understanding, Sentiment AnalysisAI Development Language
PythonAI Tools
Azure OpenAI, Gradio, Hugging Face, StreamlitAI Models
BERT, ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$150
|
Standard
$450
|
Advanced
$950
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | - | |
Detailed Code Comments | - | ||
Image Upscaling | - | - | - |
MLOps | - | ||
Model Deployment | - | - | |
Model Documentation | |||
Model Monitoring | - | ||
Model Testing & Optimization | - | ||
Model Tuning | - | ||
Natural Language Processing | |||
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | |||
Source Code | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$50 - $150
Additional Revision
+$50
Multi-Machine Context Sync
+$150
Custom AgentSkill Development
+$120Frequently asked questions
About Nihanth
Sde II
Hyderabad, India - 10:57 am local time
I specialize in designing and deploying production-ready RAG systems, graph-based AI applications, and agentic LLM workflows. I’ve worked with global organizations like S&P Global and Perficient, delivering scalable AI solutions using OpenAI, Azure OpenAI, Gemini, LangChain, and Hugging Face
What I can help you with:
• RAG-based chatbot development
• LLM application architecture
• Vector databases
• Graph-based AI systems
• End-to-end ML pipelines
• Cloud deployment on Azure, AWS, and GCP
• Responsible and compliance-aware AI systems
• End to end agentic workflows
• MCP Servers
• n8n Automation
I focus on building solutions that are not just innovative, but scalable, secure, and production-ready. Whether you need a custom AI chatbot, an automated workflow powered by LLMs, or a full-stack AI deployment, I can help you bring it to life.
Steps for completing your project
After purchasing the project, send requirements so Nihanth can start the project.
Delivery time starts when Nihanth receives requirements from you.
Nihanth works on your project following the steps below.
Revisions may occur after the delivery date.
Config review
Review all SOUL.md files, AgentSkills, cron configs, and Gateway setup shared by the client.
Failure reproduction
Reproduce each reported failure using provided logs and trajectories and document root causes.