Nanonets is an AI-driven solution that automates document processing and data extraction workflows. Leveraging advanced Optical Character Recognition (OCR) and deep learning models, Nanonets helps companies automate document-heavy business processes like accounts payable, order processing and insurance underwriting. Nanonets processes unstructured documents such as invoices, receipts, purchase orders, contracts, claims, and forms and converts them into structured output.
Founders
Hiring Pitch
Nanonets is automating document information extraction using AI. We are headquartered in San Francisco. We are backed by prestigious investors from bay area like Y-Combinator, SV Angels, Sound Ventures by Ashton Kutcher. We are currently profitable and growing at a fast pace and looking to expand our team.
We are building a product that lets companies automate extracting key information from documents like invoices, receipts, or any other kind of document and integrate it into their workflows saving manual work. We need to keep building features that will let users automate millions of documents of different kinds every day, feed them to our AI for learning, plug our API to external systems like salesforce, quickbooks, RPA providers etc.
You should check it out at https://app.nanonets.com
Tech Stack
Some of the interesting things our backend team has shipped
- Compile python code into C which could be imported into golang and then shipped as binary for on premise systems
- Autoscale GPU dependent services with kubernetes with a custom metric
- Displaying machine learning metrics in simplified ways to end users so they can act based on those metrics
- Building large number and variety of integrations with relatively generic interface like salesforce, quickbooks, RPA's, external databases
- Process large number of files in highly distributed manner in golang
Some of the interesting things our frontend team has shipped
- Ability for users to annotate documents so AI can learn which fields to extract
- Displaying machine learning metrics in simplified ways to end users so they can act based on those metrics
- Letting users build complex visual workflows around our API in our product.
- Let users visualize complex ML metrics in a very simple and intuitive way
Our stack:
- Databases
- Cassandra DB
- Postgres/MySQL
- Backend
- Golang for API and other microservices
- Python for Machine learning (Tensorflow, Pytorch)
- Frontend
- React, Typescript
- Mobx
- Cloud Providers
- AWS
- GCP for ML heavy workload
- Monitoring/Alerting
- ELK for logging
- Prometheus for Monitoring
- Graphana for dashboards
- Orchestration
- Kubernetes
- DevOps
- Jenkins for CI/CD
Open Positions at NanoNets (8 Jobs)
8 open
