Multi-Agent Orchestration
Multiple specialized agents working in parallel, each with a defined role, memory, and communication protocol.
Index Mavens: 8-agent trading intelligence for Indian market workflows, running in production.
Agentic Architect / growthperclick
I build AI agent systems that actually run in production.
Not pilots. Not prototypes. Production-grade multi-agent systems: self-hosted, model-agnostic, and wired into your business.
8-agent trading systems. Compliance automation. LinkedIn OS. Built and running. See the work →
Most AI projects never leave the pilot stage.
You have done a proof of concept. Maybe two. The demo worked. The team was impressed. Then six months passed and it is still not in production.
It is not the model. It is not the budget. It is the architecture: how agents communicate, how memory is handled, and how the system keeps running when something breaks at 2am.
Most developers know how to build an AI demo. Very few know how to architect a system that holds up. That is the gap I fill.
Common reasons AI projects stall
| 01 | Agent built on a single LLM with no fallback | HIGH RISK |
| 02 | No persistent memory, agent forgets context | HIGH RISK |
| 03 | Demo runs locally, no VPS or infra plan | HIGH RISK |
| 04 | Multi-agent communication not designed upfront | MED RISK |
| 05 | No cost controls, API spend unpredictable | MED RISK |
| 06 | Monitoring gaps, agent fails silently | MED RISK |
| 07 | Tool and skill bloat, agents doing too much | LOW RISK |
Production AI agent systems. Built and running.
Specific infrastructure, named systems, and clear handoff. Not a generic chatbot layer.
Self-Hosted Infrastructure
Agents on your infrastructure with control, predictable cost, and private-by-default networking.
Production systems run on Hetzner with Tailscale for secure agent networking.
Agent-to-Product Integration
Agents wired into the tools your team already uses, triggered through familiar interfaces.
SharkOS: LinkedIn OS replacing four separate GTM tools behind one agent interface.
AI Ops and Automation
End-to-end automation of expensive manual workflows, designed without locking into one model provider.
ComplianceHQ: AI-powered compliance automation for startup security readiness.
Real systems. Real infrastructure. Real outcomes.
Proof is not a testimonial carousel. It is shipped work with names, stacks, and architecture notes.
BuildLIVE
Index Mavens
An 8-agent trading intelligence system for Indian market workflows. It coordinates specialized agents for signal analysis, research, and delivery through a Telegram interface.
8 agents running in parallel with model-agnostic orchestration and custom agent configuration.
Read the build log →BuildLIVE
ComplianceHQ
AI-powered compliance automation for startup security readiness. It turns slow manual evidence gathering into structured agent workflows.
Security-readiness workflows compressed from weeks of coordination into guided automation.
Read the build log →BuildLIVE
SharkOS
A LinkedIn operating system for founder-led GTM. It replaces separate tooling for research, drafting, scheduling, and iteration.
Replaced four GTM SaaS tools with one agent-operated system.
Read the build log →BuildLIVE
JARVIS OS
A local-first morning briefing assistant that keeps daily intelligence private and cost-controlled.
Runs without external API dependency for privacy-sensitive briefing workflows.
Read the build log →Quantified outcomes from production systems.
Real metrics from real deployments, not theoretical projections.
Three steps. No decks, no fluff.
Discovery call
We talk through what you are building, what is stuck, and whether agent architecture is the right answer.
Scoped proposal
If there is a fit, you get a clear scope: what gets built, how it runs, what it costs, and what handoff includes.
Build and hand off
I build, test, deploy, and document the system so your team has access, context, and no black boxes.
What you can hire me to build.
Fixed-scope engagements for founders and operators who need systems that run.
MVP Agent Sprint
Best for: Founders who need a working agent system fast.
A scoped single-agent or simple multi-agent build, deployed, documented, and yours to run.
Book a discovery call →Production Agent System
Best for: Businesses that need multi-agent architecture integrated into existing operations.
Full system design, build, deployment, and handoff using model-agnostic infrastructure.
Book a discovery call →Build + GTM
Best for: Founders who need the product built and the first distribution loop wired from day one.
Agent system plus launch strategy and the first operational growth loop.
Book a discovery call →All engagements are fixed-scope, not open retainers. You know what you are getting before we start.
Best for founders and operators who are past the demo stage.
You need someone who knows the difference between a demo that impresses and an agent that actually runs unsupervised.
I work best with
- Founders building AI-native products who need the agent layer done right.
- Operators with a clear workflow problem and the budget to automate it.
- Technical teams who know what they want but need an architect to scope and build it.
I am probably not the right fit if
- You want a white-label chatbot.
- You need a strategy doc without shipping anything.
- You do not have a specific problem, just "AI somewhere".
What operators say about the systems.
Direct feedback from founders and operators running production agent systems.
“Amit delivered a production-grade multi-agent system that handles 500+ daily trades across 8 specialized agents. The system runs autonomously on our infrastructure with model-agnostic fallback.”
“What used to take our compliance team 3 days of manual evidence gathering now runs in under 2 hours through automated agent workflows. The ROI was immediate.”
“We replaced four separate GTM SaaS tools with one agent-operated system. The cost savings alone justified the build, but the operational efficiency is what truly matters.”
Questions that usually come up before a build.
Do you work with clients outside India?
Yes. All work is remote. Infrastructure runs wherever you host it, and the collaboration model works across time zones.
What if I already have a developer on my team?
Good. I scope the agent architecture, build the core system, and document everything so your team can extend and maintain it.
Which AI models do you use?
The systems are model-agnostic. In practice, Claude and GPT-4 handle reasoning-heavy agents, while Ollama supports local, cost-sensitive, or privacy-sensitive workloads.
What does a typical engagement cost?
It depends on scope. MVP sprints and production systems are scoped after discovery, priced against the work and value rather than open-ended hourly billing.
/08 - Next step
Still reading? Let's talk.
A 30-minute discovery call. Free. I come prepared, use the booking context as a fit check, and tell you directly if an agent system is not the right answer.