Prathamesh | AI Builder | AI Deploy Network

About Prathamesh

AI engineer who builds agentic systems and retrieval infrastructure for teams that can't send sensitive data to the cloud. AegisRAG gives organisations accurate answers from their own documents, entirely on-premise, with reranked hybrid search and per-role access so each team sees only what it's cleared for. AegisResearch replaces hours of manual research with a self-correcting agent that plans, searches, verifies, and delivers cited reports. Open to AI agent, RAG, and automation work.

Professional profile

Location: Pune, India
Time zone: Asia/Calcutta
Work mode: Remote, Hybrid
Availability: Available for Permanent Roles
AI experience: 1+ years
Industry experience: 1+ years
Languages: English, Hindi, Marathi
Typical response: Within 24 hours
Preferred engagement: Medium (multi-team programmes)
Travel: Occasional travel
Available in: India, United States, Russia, Japan, China, France, United Kingdom, Spain

Proven on the network

Implementation records and publicly available work, where published.

1 AI Deployment delivered, including 1 publicly identified implementation available to inspect.

Reported business outcomes

Hours Automated80 hours
period not specified
Automated 80 hours (period not specified)

Builder-reported outcomes, recorded on deployment and case study records that AI Deploy Network reviewed before publication. AI Deploy Network does not currently capture a measurement basis or calculation methodology for reported outcomes, so none is shown.

What Prathamesh builds

Implementation domains listed on this profile. Evidence-supported items are identified separately below.

Specialisations

Agentic Workflow Automation

AI Agents

AI Analytics

Decision Intelligence

RAG & Knowledge Retrieval

Builder-listed deployment types

Enterprise Search
Knowledge Assistant

AI Deployments

Real AI implementations demonstrated through evidence.

01 / AI DeploymentPublished record

Multi-Tenate Rag System

Multi-tenant Enterprise RAG assistant with role-based access control: employees ask questions in natural language and get cited answers only from documents their role and tenant are authorized to see.

80 hours automated / per week

FinTech · Enterprise Search · Knowledge Assistant

View Deployment

Implementation capabilities

Evidence-supported means linked to a published Deployment or Case Study; other capabilities are Builder declared.

Evidence-supported

Enterprise Search1 record
Knowledge Assistant1 record

Builder declared

Agentic Workflow Automation
AI Agents
AI Analytics
Decision Intelligence
RAG & Knowledge Retrieval

Technology

Technologies listed by the Builder, distinguished by published evidence.

Evidence-supported

Agent Skills1 record

Builder declared

Django
Redis Agent Memory

Industry experience

Published Deployments support the listed counts. Other industries are Builder declared.

FinTech

1 published Deployment

Banking

Builder declared

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

Community

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

Ready to discuss your AI implementation?

Tell Prathamesh what you are trying to automate and the outcome you need. Your message goes directly to the builder, with a copy kept in your Organisation Workspace.