Service 01 // AI Engineering
We design and build AI that does real work inside your business: agents that take action, assistants that hold a conversation, and systems that know your data.
Start an AI project01
- 01
Your team spends hours on repetitive work that follows the same steps every time.
- 02
Customers wait for answers that already sit somewhere in your documents.
- 03
You tried a chatbot, but it guesses instead of knowing your business.
- 04
You have an AI product idea, but no team to build it properly.
02
- Agentic AI and autonomous agents that plan and complete multi-step tasks.
- Multi-agent systems where specialised agents share one workflow.
- RAG systems that answer from your own documents and data.
- Chatbots and voice assistants, including real-time voice in Hindi.
- Generative AI applications built around your product and users.
- LLM integration and fine-tuning that fit a model to your domain.
- Workflow automation that connects AI to the tools you already use.
03
A system built for real use, not a demo.
Full ownership of the code once the project is paid for.
Straight answers about what the AI can and can't do, before you rely on it.
FetchLead AI
AI pipelines that find freelance jobs, score each one and write the proposal.
Agentrix AI product · Live
Real-Time Hindi Voice Assistant
Streaming speech recognition, a language model and Hindi speech, in real time.
Client confidential
Voice Call & Document Summarisation
Calls and scanned documents turned into structured summaries.
Client confidential
Medical Knowledge Chatbot
Answers grounded in a medical knowledge base with RAG.
Client confidential
- LangGraph
- CrewAI
- AutoGen
- OpenAI Agents SDK
- Model Context Protocol (MCP)
- Tool use / function calling
- Multi-agent systems
- n8n
- OpenAI API
- Anthropic Claude API
- Gemini API
- Llama 3.3
- Mistral
- Hugging Face
- Prompt engineering
- Structured outputs
- AI image generation
- LangChain
- LlamaIndex
- RAG / vector search
- Vector databases
- Pinecone
- ChromaDB
- Qdrant
- FAISS
- Sentence Transformers
- Embeddings & reranking
- Groq
- OpenRouter
- Ollama
- vLLM
- Pipecat
- Deepgram
- Whisper
- ElevenLabs
- Sarvam AI
- AssemblyAI
- Tesseract OCR
- LLM fine-tuning
- LoRA / QLoRA
- PyTorch
- Hugging Face Transformers
- NLP
- LangSmith
- pandas
- NumPy
- Python
- FastAPI
- Flask
- Django
- Celery
- Redis
05
What's the difference between a chatbot and an AI agent?
A chatbot answers questions. An agent can also take actions, such as updating a record or sending an email, within limits you set.
Can the AI use our own data?
Yes. With retrieval-augmented generation (RAG), it answers from your documents and systems instead of guessing.
Which AI models do you use?
Whichever fits the job, budget and privacy needs. Recent projects used Llama 3.3 through Groq and OpenRouter.
Do you build in languages other than English?
Yes. We've built a real-time voice assistant that listens and speaks in Hindi.
How long does an AI project take?
It depends on scope. You'll get a clear timeline after the first call.
