You will get Custom ML Models: Sales, Demand & Risk Forecasting
Project details
You will get a custom ML model that accurately forecasts sales, demand, risk, or any time‑based metric — delivered as an API, dashboard, or scheduled report.
I'm an AI Engineer who built financial analytics pipelines for a production FinTech ERP. I architect the full workflow — from raw data to deployed prediction, not just a script.
What sets this apart:
• Hybrid modeling — statistical methods (ARIMA, ETS) combined with deep learning (LSTM, GRU) and tree‑based models (LightGBM, XGBoost) for the best of both worlds.
• Rigorous evaluation — back‑testing on your data, multiple accuracy metrics (MAPE, RMSE, MAE), residual diagnostics, and prediction intervals.
• Business‑ready output — not just a notebook. An API endpoint, interactive dashboard, or scheduled CSV that integrates cleanly into your workflow.
• Security‑first — your data stays encrypted and isolated; nothing is shared or used for model training elsewhere.
• Production DNA — I've built systems handling real financial data at scale; reliability, not just a prototype.
Every model is 100% original, battle‑tested against edge cases, and ready to drive impact.
I'm an AI Engineer who built financial analytics pipelines for a production FinTech ERP. I architect the full workflow — from raw data to deployed prediction, not just a script.
What sets this apart:
• Hybrid modeling — statistical methods (ARIMA, ETS) combined with deep learning (LSTM, GRU) and tree‑based models (LightGBM, XGBoost) for the best of both worlds.
• Rigorous evaluation — back‑testing on your data, multiple accuracy metrics (MAPE, RMSE, MAE), residual diagnostics, and prediction intervals.
• Business‑ready output — not just a notebook. An API endpoint, interactive dashboard, or scheduled CSV that integrates cleanly into your workflow.
• Security‑first — your data stays encrypted and isolated; nothing is shared or used for model training elsewhere.
• Production DNA — I've built systems handling real financial data at scale; reliability, not just a prototype.
Every model is 100% original, battle‑tested against edge cases, and ready to drive impact.
AI Algorithms
Autoencoder, Gated Recurrent Unit, Large Language Model, Long Short-Term Memory Network, Multilayer Perceptron, Regression AnalysisAI Applications
AI-Enhanced Classification, Anomaly Detection, Natural Language Understanding, Time Series Analysis, Time Series ForecastingAI Development Language
PythonAI Tools
Hugging Face, PyTorch, TensorFlowAI Models
BERT, ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$49
|
Standard
$499
|
Advanced
$999
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 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 |
Frequently asked questions
About Soumasnigdha
AI Engineer
Bengaluru, India - 5:46 pm local time
Architecting Intelligence | Engineering Reliability
I am an AI Engineer specializing in building autonomous, AI‑native FinTech systems—transforming complex financial logic into reliable, production‑grade platforms where artificial intelligence serves as a core utility. My expertise lies in bridging agentic AI, product engineering, and high‑fidelity user experiences to deliver enterprise solutions from zero to one.
Core Competencies:
- Intelligent Backend Systems: I architect high‑concurrency, asynchronous APIs using FastAPI and Pydantic. I standardize service layers and refactor complex financial workflows—such as automated reconciliation and multi‑tenant procurement—into modular, observable architectures with structured logging and error interception.
- Agentic AI & Predictive Analytics: I design autonomous agentic workflows that orchestrate LLMs (LangChain), RAG with vector search (pgvector), and computer vision (OCR) for intelligent document parsing, real‑time analytics, and conversational BI. I apply advanced prompt engineering and generative AI to deliver a forensic‑level financial intelligence engine, enabling 10x Autonomous Finance.
- High‑Fidelity Frontends: I craft premium, responsive user experiences using React, TypeScript, and Vite. I leverage Framer Motion, Radix UI, and Tailwind CSS with glassmorphic aesthetics to simplify complex financial interactions and make enterprise data intuitive.
- Cloud & Infrastructure: I manage scalable, secure data layers with PostgreSQL and Supabase, enforcing Row‑Level Security and RBAC for bank‑grade isolation. I deploy multi‑service, zero‑trust environments on GCP/AWS via Docker, CI/CD (GitHub Actions), and automated secret management.
Technical Arsenal:
- Languages & Logic: Python (FastAPI, Pydantic, Asyncio), TypeScript (React, Vite)
- AI & Science: LLM Orchestration (LangChain), RAG (pgvector), OCR, Agentic AI, Prompt Engineering, Time‑Series, Pandas, NumPy, Statistics
- Styling & UI: Tailwind CSS, Framer Motion (Advanced Animations), Glassmorphism, Radix UI, CSS‑in‑JS
- Infra & Reliability: GCP/AWS, PostgreSQL (Supabase RLS/RBAC), Docker, CI/CD (GitHub Actions), Zero‑Trust Secret Management, Structured Logging, System Metrics, Code Refactoring
Why I Build:
I believe AI should not be a siloed experiment but a seamless, reliable layer within the user experience. Whether architecting agentic workflows for financial decision‑making or polishing a frontend design system, I build for consistency, security, and scalable intelligence—turning bold product visions into enterprise‑grade reality.
Steps for completing your project
After purchasing the project, send requirements so Soumasnigdha can start the project.
Delivery time starts when Soumasnigdha receives requirements from you.
Soumasnigdha works on your project following the steps below.
Revisions may occur after the delivery date.
Data Audit & Preprocessing
I’ll analyze your dataset for quality issues, handle missing values, detect outliers, and engineer time‑based features (lags, rolling windows) to prepare it for modeling.
Exploratory Analysis & Baseline
I’ll uncover trends, seasonality, and correlations, then build a simple baseline model so we can later measure the lift from more advanced approaches.