AI and machine learning

Assistants that answer from your own documents, agents that take repetitive work off your team, and models that forecast and flag. Built with guardrails, tested against real questions, and with a person approving anything that matters.

Services in AI and machine learning

4 services. Open any of them for what’s included, when it makes sense and what you get.

Machine learning

Models that predict, classify and flag what people would miss.

  • Demand and sales forecasting
  • Churn prediction and segments
  • Recommendations and pricing
  • Fraud and anomaly detection

Generative AI and LLM apps

Language models put to work on your own data, with guardrails.

  • Assistants and chatbots
  • Document search, summaries and Q&A
  • AI agents
  • Guardrails and testing

NLP and computer vision

Reading text, documents and images at scale.

  • Document extraction and OCR
  • Text classification and sentiment
  • Image recognition and visual checks
  • Record matching

MLOps and responsible AI

Models that keep working long after launch.

  • Deployment
  • Monitoring
  • Retraining pipelines
  • Model registry

Projects we take on

The kinds of work clients bring to this practice.

An assistant on your own documents

Answers from your policies, manuals or contracts, with the source passage shown for every answer.

An AI agent for a repetitive workflow

Reading emails or documents, filling in systems and drafting replies, with a person approving each action.

Document extraction

Invoices, forms and PDFs turned into clean data, with uncertain fields flagged for a person to check.

Forecasting and prediction

Demand, sales, churn or fraud models trained on your history and monitored after launch.

Voice and Indian languages

Speech and text in Hindi and other Indian languages, as in our team’s own projects.

How we approach it

Grounded in your data

Answers come from your documents and systems, and show where they came from.

Tested before launch

We build a set of real questions and expected answers with you, and the system has to pass it before it goes live.

A person approves what matters

Anything that sends, pays, deletes or changes records waits for a person to approve it.

Costs you can predict

We use the smallest model that does the job well and set usage limits, so a busy month doesn’t surprise you.

Who works on it

Jatin Thakur

AI agents developer

LangGraph, FastAPI and MCP: multi-agent workflows with a human approval step built in.

Nilesh Patil

Full-stack and generative AI developer

Web platforms, Android apps and AI agents. Has code merged into Apple, Microsoft, OpenAI and Hugging Face projects.

Harsh Sahu

Machine learning, IIT Kharagpur

Speech models, retrieval systems and reproducible data science, with tests and checks built in.

Karandeep Singh Talwar

Forward deployed AI engineer and analytics architect

SQL Server back ends, ASP.NET APIs, Power BI reporting and assistants built on Amazon Bedrock.

If your project needs a specialist we don’t have in-house, you hear it on the first call, and they’re named in your proposal before any work starts.

Related work

Built by our engineers before Orandic. None of these clients hired Orandic.

  1. Guardrail
  2. Supervisor
  3. Five agentsFlights Hotels Weather Budget Itinerary
  4. Human approval

AI agents

AI Travel Agent

A supervisor routes each request to five specialist agents. A guardrail screens what comes in, and a person approves the plan before it’s final.

Jatin Thakur’s open-source project.

  1. Question
  2. One agent per passage
  3. Cross-check
  4. Answer

AI agents

AgentRAG

Question-answering over your own documents, where each retrieved passage gets its own agent. The agents work in parallel, cross-check each other’s findings, and a final agent writes the answer, streamed live.

Nilesh Patil’s open-source project, MIT licence.

Machine learning

Native language from spoken English

Hears five seconds of English and predicts the speaker’s first language: Hindi, Kannada, Malayalam, Tamil or Telugu.

Harsh Sahu’s machine-learning project. The code and the experiment log are public.

Ramesh ko 500 rupaye udhaar

Here’s what I understood: Ramesh, udhaar ₹500. Confirm and save?

Voice app

DukaanMitra

A shop ledger for kirana stores. Say it in Hindi, Hinglish or English, “Ramesh ko 500 rupaye udhaar”, and it shows exactly what it understood before anything is saved.

Harsh Sahu’s project.

  1. Voice
  2. Reads the screen
  3. Plans
  4. Clicks and types

AI agents

Aura

A voice-driven agent that operates a Windows PC. It reads the screen, plans the steps, then moves the mouse and types to finish the job, and it stops at the payment step to ask first.

Nilesh Patil’s project.

  1. Backstory
  2. Retriever
  3. Reasoner
  4. Verdict

AI agents

Backstory consistency checker

A multi-agent retrieval system that checks whether a character’s backstory contradicts the novel it comes from.

Harsh Sahu’s entry for the Kharagpur Data Science Hackathon 2026.

Questions about AI and machine learning

Where do we start with AI?

With one repetitive task that costs your team time every week. We look at your candidates, pick the one with the clearest payback, and prototype it on your own data before anything bigger.

Can AI work in Hindi and other Indian languages?

Yes. Our team has built speech and text projects in Hindi and other Indian languages. We test with the language your users actually write and speak before launch.

Will AI replace people on our team?

Most of what we build takes repetitive work off people, so they spend more time on decisions and customers. Anything important still goes past a person.

Other practices

Tell us what you need built.

A free 30-minute call with an engineer. You get the scope, timeline and price in writing before you commit, and a reply within one working day.