You will get a production-grade review of your AI agent system
Project details
AI agents that demo well and agents that survive production are different species. I review your agent or LLM system the way I build my own: security first, reliability second, cleverness last.
Credentials: I run a production agent platform inside my own enterprise SaaS — a governed gateway where agents act as the signed-in user and never hold credentials, tool catalogs generated from the API contract, propose→confirm on any write, per-turn cost metering. Plus LLM pipelines that compile legal documents into a deterministic pricing engine.
What I review: prompt-injection and credential exposure, permission and tenant boundaries, tool design, retrieval grounding, cost and latency, eval/testing gaps, and the failure modes that only appear on retry #2. You get a severity-ranked findings report with concrete fixes — each finding verified before it reaches you, not vibes.
Works for LangGraph, LlamaIndex, OpenAI/Anthropic SDKs, or custom stacks — Python, TypeScript, or Go.
Credentials: I run a production agent platform inside my own enterprise SaaS — a governed gateway where agents act as the signed-in user and never hold credentials, tool catalogs generated from the API contract, propose→confirm on any write, per-turn cost metering. Plus LLM pipelines that compile legal documents into a deterministic pricing engine.
What I review: prompt-injection and credential exposure, permission and tenant boundaries, tool design, retrieval grounding, cost and latency, eval/testing gaps, and the failure modes that only appear on retry #2. You get a severity-ranked findings report with concrete fixes — each finding verified before it reaches you, not vibes.
Works for LangGraph, LlamaIndex, OpenAI/Anthropic SDKs, or custom stacks — Python, TypeScript, or Go.
AI Algorithms
Large Language ModelAI Applications
AI Mobile App Development, AI-Enhanced Classification, AIOpsAI Development Language
PythonAI Models
ChatGPTWhat's included
| Service Tiers |
Starter
$600
|
Standard
$1,500
|
Advanced
$3,500
|
|---|---|---|---|
| Delivery Time | 7 days | 10 days | 14 days |
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 | - | - | - |
Frequently asked questions
About Rohan
Fractional CTO
Pune, India - 10:45 pm local time
What I build for clients:
⚙️ AI agent systems that survive production — not chatbot demos. My platform runs a governed agent gateway: tool catalogs generated from the API contract, agents acting as the signed-in user through full permission checks, propose→confirm on any action that writes, per-turn cost metering. LLM pipelines in production include document-to-rules extraction (replacing hours of manual extractions with a 6-minute, $5 run) and voice-to-structured-data in app-store apps.
🏗️ Full product builds, zero to production — contract-first API design, PostgreSQL, Go/TypeScript, React/Next.js, React Native, real-time sync, durable workflow orchestration (Temporal), multi-tenant zero-trust security. One accountable engineer, senior-team output.
📊 Data & integration platforms — ERP integrations (modern REST down to 1990s ODBC and flat-file imports), reconciliation-grade reporting that matches the ledger to the cent, idempotent sync pipelines, executive dashboards.
🧭 Fractional CTO / architecture partner — architecture reviews, AI-native engineering practices (the guardrail systems that let AI write 90% of code safely: encoded constraints, adversarial AI review, per-PR production-parity preview environments), hiring specs, and honest written judgment. My method produces documentation as a by-product, not an afterthought.
Why one person can credibly offer this breadth: I've built every layer of a real enterprise product — database, API, web, mobile, AI, infrastructure, CI/CD — and operate it in production for paying business customers (Australian construction industry: multi-branch companies, field crews, payroll-critical pricing, ERP reconciliation). Breadth isn't a claim on this profile; it's what shipping an entire platform requires.
How I work: 90% async, written-first (plans, architecture memos, review reports, runbooks), fluent across Claude Code / OpenAI / Anthropic APIs as daily production tooling. Australian, English-native, comfortable with US/EU overlap hours.
If you're weighing me against specialists: I'm the person you call when the work crosses layers — when the AI agent needs real security, the dashboard needs real data engineering, and the automation needs to still work in six months.
Steps for completing your project
After purchasing the project, send requirements so Rohan can start the project.
Delivery time starts when Rohan receives requirements from you.
Rohan works on your project following the steps below.
Revisions may occur after the delivery date.
Review your AI agent architecture
I assess your AI agent or LLM system for security, reliability, permissions, tool design, retrieval quality, cost, latency, and testing gaps. I work with LangGraph, LlamaIndex, OpenAI/Anthropic SDKs, and custom Python, TypeScript, or Go stacks.
Validate real production risks
I identify issues that matter in production, including prompt injection, credential exposure, tenant isolation, retry failures, hallucination risks, and governance weaknesses. Every finding is verified before it's reported.