You will get AI features integrated into your existing SaaS or app
Project details
I will add AI features directly into your existing SaaS or app — an assistant, smart search, agentic features , automation — without disrupting what's already live.
The problem this solves: your product works, but it has no AI, and rebuilding it from scratch isn't realistic. You need AI features added cleanly into your current stack, tested, and shipped.
What you get: an AI feature integrated into your app — this could be an in-app AI assistant/copilot, Agentic features , RAG-based search over your data, an automation layer for a workflow, or an LLM-powered feature you specify — wired into your existing frontend and backend, with proper auth, rate limiting, and error handling.
Technologies: Python or Node.js (matched to your stack), OpenAI/Claude/Gemini APIs, LangChain, LangGraph, vector databases (Pinecone/Chroma) where relevant, and your existing frontend (React, Next.js, etc.). I collaborate with the wider Amilek team for larger integrations.
Documentation: how the integration works, where it lives in your codebase, and how to extend or swap models later.
Communication: regular updates and a walkthrough of the feature before handover.
The problem this solves: your product works, but it has no AI, and rebuilding it from scratch isn't realistic. You need AI features added cleanly into your current stack, tested, and shipped.
What you get: an AI feature integrated into your app — this could be an in-app AI assistant/copilot, Agentic features , RAG-based search over your data, an automation layer for a workflow, or an LLM-powered feature you specify — wired into your existing frontend and backend, with proper auth, rate limiting, and error handling.
Technologies: Python or Node.js (matched to your stack), OpenAI/Claude/Gemini APIs, LangChain, LangGraph, vector databases (Pinecone/Chroma) where relevant, and your existing frontend (React, Next.js, etc.). I collaborate with the wider Amilek team for larger integrations.
Documentation: how the integration works, where it lives in your codebase, and how to extend or swap models later.
Communication: regular updates and a walkthrough of the feature before handover.
AI Algorithms
Autoencoder, Convolutional Neural Network, Generative Adversarial Network, Large Language Model, YOLOAI Applications
AI Chatbot, AI Text-to-Image, AI Text-to-Speech, AI-Generated Art, AIOps, Anomaly Detection, Facial Recognition, Image Analysis, Image Recognition, Natural Language Generation, Time Series Analysis, Time Series ForecastingAI Development Language
PythonAI Tools
Hugging Face, TensorFlowAI Models
BERT, GPT-3, GPT-4, LLaMA, Midjourney AI, OpenAI CodexWhat's included
| Service Tiers |
Starter
$500
|
Standard
$1,200
|
Advanced
$3,000
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 18 days |
Number of Revisions | 2 | 3 | 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.
Agentic feature integration
(+ 4 Days)
+$200
RAG/vector search setup
(+ 5 Days)
+$300
Model swap/fallback setup (multi-provider)
(+ 5 Days)
+$200About Jenil
ML Engineer | LLM Fine-Tuning | MLOps | SaaS AI Integration
Ahmedabad, India - 12:48 am local time
I am an AI/ML engineer with hands-on project experience across the full modeling lifecycle: data preparation, feature engineering, model training, evaluation, and experiment tracking. My work spans classical machine learning, deep learning, modern LLM fine-tuning, and integrating AI into live SaaS products.
𝗪𝗵𝗮𝘁 𝗜 𝗰𝗮𝗻 𝗵𝗲𝗹𝗽 𝘆𝗼𝘂 𝘄𝗶𝘁𝗵:
• Prediction Model : Predictive analytics and forecasting, using models such as XGBoost, ensembles, and LSTM for tasks like demand, churn, price, or trend prediction.
• FineTuning : Model fine-tuning and domain adaptation for language models using LoRA and QLoRA, so a model speaks your domain without huge compute.
• MLOps: reproducible pipelines with MLflow and DVC, containerized training, and model serving so your model does not stay stuck in a notebook.
• AI integration into your existing SaaS or app: adding an AI assistant, smart search, or automation feature directly into your current product, wired into your existing auth and data.
𝗧𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝘀𝘁𝗮𝗰𝗸 𝗜 𝘂𝘀𝗲:
Python, Scikit-learn, XGBoost, TensorFlow, HuggingFace and PEFT for fine-tuning, MLflow and DVC for tracking and versioning, Docker, FastAPI for serving models as APIs, and Reactjs ,,OpenAI/Claude/LangChain/LangGraph for AI feature integration.
𝗛𝗼𝘄 𝗜 𝘄𝗼𝗿𝗸:
I start from your data (or your product, for integration work) and the decision or feature you want to improve, then choose the right approach rather than the most complex one. I engineer features, train and compare models, and evaluate them honestly with the right metrics. I track every experiment for reproducibility, then package the model—or feature—so it can be served, integrated, and monitored.
𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘆:
I validate models against held-out data, watch for overfitting, and report real metrics, not inflated claims. I add tests around the pipeline, handle errors, and document how to retrain, run, or extend the system.
𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁:
I can serve models as clean APIs, containerize them, set up basic monitoring, and integrate AI features directly into your existing product without disrupting what's already live. For production scaling I collaborate with the wider Amilek team.
I communicate in plain language, explain what a model can and cannot do, and share progress and results clearly so you can make decisions with confidence.
If you want a custom model, a forecast, a fine-tuned LLM, or AI features added to your product, tell me about your data or your app and the outcome you are after, and I will suggest a realistic approach and what results to expect.
Steps for completing your project
After purchasing the project, send requirements so Jenil can start the project.
Delivery time starts when Jenil receives requirements from you.
Jenil works on your project following the steps below.
Revisions may occur after the delivery date.
Delivery steps
* Review your app, stack, and integration goal * Design the AI feature and integration point * Build and connect it to your existing app * Test end-to-end, including edge cases * Deploy and hand over with documentation