I build AI systems that run real operations, not demos.

Portrait of Ravi P.

I'm Ravi, an independent AI engineer. I build AI agents, RAG systems that answer from your own data, voice agents and workflow automation, and I keep tuning them as real usage shows what to improve.

Most AI projects stall between the demo and production. The cause is rarely the model. It's the connection to existing systems, messy or stale data, answers nobody can verify, and no monitoring once it's live. That's where I put the work.

“If AI isn't the right fix for your workflow, I'll tell you before you spend anything.”
Ravi P.
  • Top Ratedon Upwork
  • 100%Job Success
  • 20+clients
  • Bengaluru, India

How I work

Outcome first

We agree on the number that should move, like hours saved or calls booked, before we pick any tool.

Grounded in your data

Answers come from your documents and systems, with sources you can check, not from the model's memory.

Built to last

Logging, retries and monitoring ship with every build, and I iterate on real usage after launch.

Honest scoping

If a simple automation, or no AI at all, solves it, I'll say so before you spend anything.

The stack, wired together.

Pick a job and watch which tool handles each step.

  1. HearTwiliopicks up the call
  2. HearDeepgramturns speech into text
  3. HearVapiruns the conversation
  4. ThinkClaudedecides what to say next
  5. RememberPostgresfinds the lead in the CRM
  6. Actn8nbooks the slot and updates the CRM
  7. HearElevenLabsconfirms it out loud
Hear
Think
Remember
Act
Runs on
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Jobs the stack handles

A call books a meeting

  1. Twilio picks up the call
  2. Deepgram turns speech into text
  3. Vapi runs the conversation
  4. Claude decides what to say next
  5. Postgres finds the lead in the CRM
  6. n8n books the slot and updates the CRM
  7. ElevenLabs confirms it out loud

A resume gets scored

  1. n8n picks up the new application
  2. FastAPI parses the resume
  3. Hugging Face embeds the skills
  4. Supabase compares them with the job
  5. OpenAI scores and explains the match
  6. n8n sends the shortlist

A question gets a sourced answer

  1. Next.js takes the question
  2. FastAPI prepares the search
  3. Pinecone finds matching passages
  4. Claude writes the answer
  5. LangGraph checks every claim against its source
  6. Next.js shows the answer with citations

An inbox gets cleared

  1. Next.js you ask in chat
  2. LangGraph plans the steps
  3. OpenAI drafts the replies
  4. Redis keeps the context
  5. Make sends and schedules after you approve

Have a workflow in mind?

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Describe your workflow