You will get a working MVP within three to four weeks
Project details
AI & LLM Product Build Packages
Deliver end-to-end AI products from prototype to MVP to scalable, investor-ready solutions.
Starter Build – “Idea Spark”: Lightweight AI prototype with core LLM features, basic RAG pipeline, technical feasibility assessment, and roadmap for development.
Standard Build – “MVP Builder”: Full MVP with defined user flows, advanced RAG/embedding integration, multi-modal AI (text, audio, CV/NLP), cloud/database setup, and growth-oriented roadmap.
Advanced Build – “Scale & Fund Accelerator”: Scalable AI product with full RAG/LLM pipelines, multi-modal AI, real-time SaaS architecture, investor-ready roadmap, metrics dashboard, and funding readiness guidance.
Tech stack exposure includes GPT, Claude, LLaMA, LangChain, Pinecone, React, Node.js, AWS/GCP/Azure, multi-modal AI tools, and DevOps automation. Each tier provides a functional AI product, modern tech integration, and growth roadmap.
Deliver end-to-end AI products from prototype to MVP to scalable, investor-ready solutions.
Starter Build – “Idea Spark”: Lightweight AI prototype with core LLM features, basic RAG pipeline, technical feasibility assessment, and roadmap for development.
Standard Build – “MVP Builder”: Full MVP with defined user flows, advanced RAG/embedding integration, multi-modal AI (text, audio, CV/NLP), cloud/database setup, and growth-oriented roadmap.
Advanced Build – “Scale & Fund Accelerator”: Scalable AI product with full RAG/LLM pipelines, multi-modal AI, real-time SaaS architecture, investor-ready roadmap, metrics dashboard, and funding readiness guidance.
Tech stack exposure includes GPT, Claude, LLaMA, LangChain, Pinecone, React, Node.js, AWS/GCP/Azure, multi-modal AI tools, and DevOps automation. Each tier provides a functional AI product, modern tech integration, and growth roadmap.
AI Development Type
Deep Learning, Model Tuning, Recommendation SystemAI Tools
MLflow, OpenCV, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$2,000
|
Standard
$3,500
|
Advanced
$5,000
|
|---|---|---|---|
| Delivery Time | 10 days | 19 days | 28 days |
Number of Revisions | 2 | 3 | 4 |
AI Model Integration | |||
Detailed Code Comments | - | - | |
Knowledge Graph | - | ||
Model Documentation | - | ||
Ontology | - | - | |
Source Code | |||
Taxonomy |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$500 - $1,500
Starter package add-ons
+$1
Standard package also includes
+$1
Advanced Package also includes:
+$1
163 reviews
(139)
(15)
(6)
(1)
(2)
This project doesn't have any reviews.
OS
Olushola S.
Mar 27, 2026
Principal Agentic AI & Multimodal Intelligence Engineer
Excellent delivery! Otun is an exceptional engineer and a true technical leader. As Lead Full-Stack AI Engineer, he was instrumental in building our AI-native investment research platform, delivering high-impact systems across agentic AI workflows, multimodal pipelines, and scalable full-stack infrastructure.
He combined deep technical expertise with a product-focused mindset, turning complex AI concepts into actionable, reliable features. Otun’s ownership, speed, and innovation make him an invaluable asset to any team pushing the boundaries of AI-powered platforms.
He combined deep technical expertise with a product-focused mindset, turning complex AI concepts into actionable, reliable features. Otun’s ownership, speed, and innovation make him an invaluable asset to any team pushing the boundaries of AI-powered platforms.
AA
Alex A.
Mar 20, 2026
Senior Full-Stack AI Engineer (LLM, RAG, Marketplace)
Perfect delivery as usual! Otun is the best Fullstack AI engineer I’ve worked with. Our team needed someone who can build a Fullstack AI system. I highly recommend Otun for RAG development work and full stack AI embedded development systems. I’m especially impressed with his strong sense of ownership, fast execution, and a clear ability to turn complex AI into real product value. Highly recommended for any AI-native product team
MD
Michael D.
Sep 18, 2025
Email Welcome Double Optin Copy
KC
Kalvin D C.
Aug 17, 2025
End to End Configuration and Launch of GoHighLevel
Very responsive
OS
Olushola S.
Jun 9, 2025
Marketing Automation Architect | Advanced Systems, AI Workflows & Scalable Infrastructure
Otun’s ASCEND™ Framework completely transformed how we scale. We had worked with other automation freelancers before, but no one came close to Otun. His ASCEND™ Framework is pure genius—it’s not just a system, it’s a strategy. He mapped out our entire marketing and sales process, identified every leak in our funnel, and rebuilt everything with precision.
From the way he structured our lead capture and qualification, to the follow-up automations and Slack handoffs—every step was intentional and scalable. We now have SOPs, a clean CRM, automated nurture, and a backend that supports real growth.
We went from daily chaos to a high-functioning, systematized operation. If you're a coach, consultant, or agency owner serious about growth, you need Otun and the ASCEND™ Framework on your side. Game-changer doesn’t even begin to describe
From the way he structured our lead capture and qualification, to the follow-up automations and Slack handoffs—every step was intentional and scalable. We now have SOPs, a clean CRM, automated nurture, and a backend that supports real growth.
We went from daily chaos to a high-functioning, systematized operation. If you're a coach, consultant, or agency owner serious about growth, you need Otun and the ASCEND™ Framework on your side. Game-changer doesn’t even begin to describe
About Otun
AI Automation Engineer | n8n, AI Voice Agents, RAG, Production LLMs
100%
Job Success
Glasgow, United Kingdom - 7:39 pm local time
Most automation work falls into two camps. There are no-code builders who can wire an n8n workflow but can't tell you what happens when the LLM returns malformed JSON at 2am. And there are cloud engineers who can deploy anything but have never sat with a business owner who just wants missed calls to stop costing them money. I work across both.
WHAT I ACTUALLY BUILD
▸ n8n automation & AI agents — self-hosted (Docker, VPS, nginx, Let's Encrypt) or cloud. Multi-step workflows with proper error branches, retries and dead-letter handling. Telegram, Slack, WhatsApp, Gmail, Google Sheets, CRM and webhook integrations.
▸ AI voice agents — inbound and outbound. Retell AI + Twilio (UK numbers) + GoHighLevel. Missed-call-to-text recovery, appointment booking, qualification and routing. Full stack: number provisioning, prompt design, call transfer logic, CRM writeback.
▸ RAG systems that don't hallucinate — pgvector on Postgres, hybrid dense + BM25 retrieval, LangChain orchestration. My preferred pattern is two-stage: deterministic SQL or structured retrieval first, then grounded synthesis, so every answer traces back to a source row rather than to the model's imagination. I've published a peer-reviewed paper on exactly this architecture.
▸ Production LLM applications — FastAPI + Next.js, streamed responses, Clerk auth, Stripe subscription gating. Deployed to Vercel for speed or AWS for scale: Lambda, App Runner, ECR, S3, API Gateway, CloudFront, Route 53. Amazon Bedrock (Nova, Claude) where the client needs data to stay inside their AWS account.
▸ Infrastructure that isn't clicked together by hand — Terraform with isolated dev/test/prod workspaces, GitHub Actions CI/CD using OIDC (no long-lived AWS keys anywhere), CloudWatch dashboards for token spend and latency, budget alarms so nobody gets a surprise bill.
▸ MCP servers and multi-agent orchestration — tool-calling agents with human-in-the-loop checkpoints at the steps where a wrong answer is expensive.
WHAT MAKES THE DIFFERENCE
Anyone can get an LLM to produce something plausible. The engineering is in making it produce the *same* thing tomorrow, proving where the answer came from, and knowing what it costs per call.
So I build with that from day one: structured outputs with schema validation, retrieval that carries citations, eval sets before prompt tuning, retry and fallback paths on every external call, and cost observability wired in rather than bolted on afterwards. I've written and published research on production AI architecture and audit-trail design, and I've shipped multi-tenant SaaS on this stack — Next.js BFF, FastAPI compute API, queued CPU/GPU workers on AWS Batch, usage-based billing.
That's also why I'm comfortable on projects where the output gets checked by someone other than the person who ordered it: compliance reviews, scientific workflows, financial reporting, anything with an audit trail.
HOW I WORK
I scope before I build. You get an architecture note and a fixed number before I touch a node. I write down what the system will refuse to do, not just what it will do — that's usually the part that saves the project. Handover includes documentation and a walkthrough recording, because an automation only I can debug is a liability, not an asset.
Based in the UK. Comfortable working async across US and EU hours.
If you're not sure whether your problem is an n8n workflow, a proper agent, or just a better-written Zap — message me and I'll tell you straight, even if the answer is "you don't need to hire anyone for this."
Steps for completing your project
After purchasing the project, send requirements so Otun can start the project.
Delivery time starts when Otun receives requirements from you.
Otun works on your project following the steps below.
Revisions may occur after the delivery date.
An initial 30min - 1 hour consultation before project starts is recommended