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Generative AI and LLM app development

Language models put to work on your own data, with guardrails. Assistants that answer from your documents and show their sources, and agents that handle multi-step work with a person approving what matters.

Start a projectMore AI and machine learning

What’s included

Assistants and chatbots

Grounded in your documents, policies and product data, showing where each answer came from.

Document search, summaries and Q&A

Find, summarise and compare across thousands of files.

AI agents

Multi-step work across your tools, with a person approving anything that sends, pays or changes records.

Guardrails and testing

Checks on what goes in and out, and a set of real questions it has to answer correctly.

Cost control

The smallest model that does the job well, with caching and usage limits.

When it makes sense

  • Your team answers the same questions from the same documents every day.
  • A workflow means reading, deciding and typing into several tools.
  • You’ve tried a chatbot and it made things up.

What you get

  • A prototype on a sample of your own documents
  • A test set of real questions and expected answers
  • The assistant or agent in your systems, with logs
  • Usage and cost reporting

Tools we work with

We pick what fits your project and your team, and we’ll say why.

  • OpenAI
  • Anthropic
  • Gemini
  • Open-source models
  • LangGraph
  • Vector search
  • FastAPI

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.

Read the code (AI Travel Agent)
  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.

Read the code (AgentRAG)
  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.

Read the code (Aura)

Questions about generative AI and LLM apps

Is our data used to train AI models?

Not by us, and we use providers whose business terms say your data isn’t used for training. If your data can’t leave your own systems, we can run open-source models in your own cloud.

How do you stop it from making things up?

We ground answers in your documents and show the sources, test against real questions before launch, and make the system say it doesn’t know instead of guessing.

Which models do you use?

Whichever fits the job and the budget: models from OpenAI, Anthropic or Google, or open-source models you host yourself. You’re not tied to one provider.

Related services

NLP and computer visionReading text, documents and images at scale.Automation and RPARepetitive manual work, taken off your team’s plate.MLOps and responsible AIModels that keep working long after launch.All of AI and machine learningFrom forecasting models to assistants that work on your own documents.

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.

Start a projector email hello@orandic.com