You will get AI/ML: End-to-end design, build, & deployment of production-grade systems
Project details
Production-grade AI systems designed, built, and deployed by a 15-person team of senior AI/ML engineers. We don't do slide decks — we ship working systems that reduce costs, automate workflows, and unlock new revenue.
Sprint-based delivery in 10-day cycles with clear milestones, mid-sprint reviews, and weekly updates. Every engagement starts with a scoping document so you know exactly what you're getting before code is written. Full stack: LLM integration, RAG, AI agents, workflow automation, ML models, data pipelines, NLP, prompt engineering, fine-tuning, and production deployment with MLOps.
Led by Clarence Stephen (Yale Physics, former Tiger Global/Morgan Stanley) with 10+ years of production AI experience across fintech, healthcare, legal tech, e-commerce, supply chain, SaaS, edTech, and retailing. Clients include Machinify, Triple Whale, Surgere, TomoCredit, and Supio.
Three tiers: 1 sprint ($20K) for a single milestone build. 2 sprints ($36K) for multi-milestone projects with testing and documentation. 3 sprints ($50K) for full-scale builds with deployment, MLOps, and monitoring.
Retainer available post-project at 50% of our daily rate.
Sprint-based delivery in 10-day cycles with clear milestones, mid-sprint reviews, and weekly updates. Every engagement starts with a scoping document so you know exactly what you're getting before code is written. Full stack: LLM integration, RAG, AI agents, workflow automation, ML models, data pipelines, NLP, prompt engineering, fine-tuning, and production deployment with MLOps.
Led by Clarence Stephen (Yale Physics, former Tiger Global/Morgan Stanley) with 10+ years of production AI experience across fintech, healthcare, legal tech, e-commerce, supply chain, SaaS, edTech, and retailing. Clients include Machinify, Triple Whale, Surgere, TomoCredit, and Supio.
Three tiers: 1 sprint ($20K) for a single milestone build. 2 sprints ($36K) for multi-milestone projects with testing and documentation. 3 sprints ($50K) for full-scale builds with deployment, MLOps, and monitoring.
Retainer available post-project at 50% of our daily rate.
AI Algorithms
Autoencoder, Convolutional Neural Network, Feedforward Neural Network, Generative Adversarial Network, Large Language Model, Long Short-Term Memory Network, Multimodal Large Language Model, Recurrent Neural Network, Regression Analysis, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI-Enhanced Classification, AI-Generated Code, AIOps, Anomaly Detection, Conversational AI, Natural Language Generation, Natural Language Understanding, Sentiment Analysis, Sequence Modeling, Time Series ForecastingAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Gradio, Hugging Face, Microsoft 365 Copilot, NVIDIA AI Platform, PyTorch, Streamlit, TensorFlow, Word2vecAI Models
AlphaCode, BERT, ChatGPT, DALL-E, GPT-3, GPT-4, LLaMA, Midjourney AI, Naive Bayes Classifier, OpenAI Codex, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$20,000
|
Standard
$36,000
|
Advanced
$50,000
|
|---|---|---|---|
| Delivery Time | 14 days | 28 days | 42 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
+$3,000 - $5,000
Additional Revision
+$1,500
Pricing Negotiations
+$1Frequently asked questions
About Clarence
AI/ML Engineering | Career Coach/Educator | Data Science | Tutor
Queens County, United States - 6:38 pm local time
Today, I’m the CEO of AcceLLM, where I lead a team of 15 senior AI engineers building production-grade AI systems for companies that want measurable business outcomes — not another strategy PDF. Our work spans LLM-powered automation, multi-agent RAG systems, fine-tuning pipelines, and AI infrastructure designed to scale. I focus on bridging the gap between cutting-edge AI research and practical business execution, ensuring teams can move from experimentation to measurable ROI.
I also run Clarence Stephen Solutions, coaching professionals through career transitions into AI, data science, and quantitative fields. Mentorship is not a side note in my career — it’s part of the operating system that drives how I lead teams, how I structure learning, and how I deliver value to clients.
The path here has been diverse but connected. I started on the equity derivatives desk at Morgan Stanley, then traded FX and equity derivatives strategies at Tiger Global, before returning to Morgan Stanley to lead client analytics for fixed income quant strategy. From there, I transitioned into senior data science and AI roles across e-commerce, retail, and pet tech, including building Readify, a generative AI ed-tech startup inspired by my daughters’ love of reading. Across all these roles, the constant has been the same instinct: find the signal in the noise, build something that works, and make it understandable and actionable for both technical and non-technical audiences.
In AI, this translates to designing autonomous agent workflows, RAG pipelines, and ML-powered decision systems that handle complex business problems while remaining reliable, interpretable, and scalable. I’ve worked with LangChain, LlamaIndex, AutoGen, and LoRA fine-tuning, integrating LLMs with internal and external systems, implementing secure access, and designing systems that allow humans and AI to collaborate seamlessly.
Teaching Is How I Lead
Mentoring and teaching for over 20 years isn’t a footnote — it’s central to how I operate. I tutor professionals and students 1-on-1 in Python, SQL, machine learning, statistics, deep learning, data engineering, and AI fundamentals. I also cover the quantitative and finance foundations underpinning data science — probability, linear algebra, calculus, derivatives pricing, risk management, and trading strategies. Whether preparing for licensing exams like the Series 7 or standardized tests like the SAT, my approach is the same: teach deeply, contextualize practically, and make the material click.
Every session is customized. No canned curriculum. I meet learners where they are: career switchers building their first ML model, graduate students diving into NLP theory, or finance professionals mastering Python and AI for competitive advantage. My dual experience at the intersection of AI and finance ensures examples are real, context is practical, and the learning sticks.
Beyond work, my curiosity is full stack: Yale Physics, Python, SQL, GenAI, NLP, ML, 40+ countries, 5 languages, Olympic lifting, hockey, snowboarding, rollerblading, reading, and guitar. Just like my professional work, my personal pursuits are wide-ranging, rigorous, and driven by curiosity.
If you’re serious about leveling up — in your career, technical skills, or both — book a free 15-minute scoping call, and let’s explore what you need to succeed.
Steps for completing your project
After purchasing the project, send requirements so Clarence can start the project.
Delivery time starts when Clarence receives requirements from you.
Clarence works on your project following the steps below.
Revisions may occur after the delivery date.
strategy call
call to assess project goals, technical requirements, and integration landscape.
Scoping document delivered
sprint plan, milestones, timeline, deliverables, and desired outcome explicitly defined