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About Sonal
I’m a software engineer in Bengaluru with 3+ years of production experience, focused on building AI systems people can trust. I work with RAG pipelines, tool-calling agents, and the evaluation and guardrails that make them dependable, and I follow a simple rule: human oversight comes before autonomy.
I independently designed and deployed Idea Vault, a production RAG and agentic platform with tiered LLM routing, cross-provider failover, and approval gates so the model can never change data on its own. I enjoy the unglamorous parts of AI work, like testing, tracing, and edge cases, because that’s what makes a demo into something people can rely on.
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I’m easy to work with: I explain technical trade-offs plainly, ask questions early, and follow through on what I commit to. If you’re building something real, I’d love to hear about it.
Professional profile
- Location
- Bangalore, India
- Time zone
- Asia/Calcutta
- Work mode
- Remote
- Availability
- Available This Week
- Languages
- English, Hindi
- Travel
- Occasional travel
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Proven on the network
Implementation records and publicly available work, where published.
0 AI Deployments delivered. No publicly identified implementations are available to inspect.
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What Sonal builds
Implementation domains listed on this profile. Evidence-supported items are identified separately below.
Specialisations
Agentic Workflow Automation
AI Analytics
Chatbots
Computer Vision
RAG & Knowledge Retrieval
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AI Deployments
Real AI implementations demonstrated through evidence.
No publicly identified Deployments are available to inspect.
05
Implementation capabilities
Evidence-supported means linked to a published Deployment or Case Study; other capabilities are Builder declared.
Evidence-supported
No published evidence linked yet.
Builder declared
- Agentic Workflow Automation
- AI Analytics
- Chatbots
- Computer Vision
- RAG & Knowledge Retrieval
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Technology
Technologies listed by the Builder, distinguished by published evidence.
Evidence-supported
No published evidence linked yet.
Builder declared
- Agent Skills
- Anthropic Console
- Azure AI Foundry
- Banana
- Chroma
- CopilotKit
- Datadog LLM Observability
- Elasticsearch Vector
- Google AI Studio
- Groq
- Hugging Face Inference
- Humanloop
- LangChain
- LangGraph
- Langfuse
- Lovable
- MongoDB Atlas Vector Search
- OpenRouter
- Pydantic AI
- Python
- Redis Agent Memory
- n8n
07
Industry experience
Published Deployments support the listed counts. Other industries are Builder declared.
IT Services & Systems Integration
Builder declared
Software & SaaS
Builder declared
08
Trust & verification
A closer look at how this profile and its evidence are reviewed.
Community Member
Published evidence is reviewed against the AI Deploy Network Evidence Standard.
How verification works
Verified Builder
Independently reviewed by AI Deploy Labs against the platform Evidence Standard.
How this is verifiedWhat the platform has established, and what remains builder-declared.
- Identity and organisation
- Identity checks confirm that the person presenting this profile controls the account and the professional identifiers attached to it, such as a LinkedIn profile. Organisation membership is confirmed by the organisation, not self-declared by the builder.
- AI Deployments
- An approved AI Deployment is an implementation record the builder submitted and the platform reviewed against the Evidence Standard before publication. Approval confirms the record is complete, coherent and consistent with the supporting material provided — it is not an audit of the client's systems.
- Business outcomes
- Outcome figures are recorded by the builder on the deployment or case study that produced them, and are reviewed at publication. Aggregates shown on this profile are calculated from those approved records only.
- Evidence
- Supporting evidence comes from the records the builder published on this platform: approved AI Deployments, published AI Case Studies, verification outcomes and organisation participation.
- Currency
- Older evidence continues to count and is never removed. Recency is presented separately through Professional Currency so that current expertise can be assessed alongside historical work.
- Traceability
- Every figure on this profile can be followed to the record that produced it. Open the linked AI Deployment or AI Case Study to read the outcome exactly as it was reported.
The platform does not independently audit client systems or financial records. How this is verified
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