01
About Siam
I build automation and AI agent workflows, mostly in n8n, and the software around them when a workflow is not enough.
Recent work: production lead generation and outreach pipelines during an AI operations internship, including lead scoring against a valuation API and intent classification on inbound replies. A full stack research agent on FastAPI and Supabase that takes a topic, runs a live web search, and returns a sourced report. And wallop, a load testing CLI I published on PyPI, with about forty tests running green across a three OS matrix.
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The part I care about most is whether an automation did the right thing, not just whether it ran. A field can hold a value of the right shape and the wrong meaning, and every row count check will still pass. That is usually where the money leaks.
Based in Dhaka, working remotely, comfortable on European hours and US Eastern mornings.
Professional profile
- Location
- Dhaka, Bangladesh
- Time zone
- Asia/Dhaka
- Work mode
- Remote
- Availability
- Available Now
- AI experience
- 2+ years
- Industry experience
- 2+ years
- Languages
- English, Bengali
- Working hours
- 13:00-23:00 (UTC+6), Mon-Fri. Overlaps European hours and US Eastern mornings.
- Typical response
- Within 24 hours
- Preferred engagement
- Small (individual or single team)
- Travel
- No travel
- Available in
- Bangladesh
02
Proven on the network
Implementation records and publicly available work, where published.
0 AI Deployments delivered. No publicly identified implementations are available to inspect.
03
What Siam builds
Implementation domains listed on this profile. Evidence-supported items are identified separately below.
Specialisations
Agentic Workflows
AI Agents
Customer Support AI
RAG Systems
Workflow Automation
04
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 Workflows
- AI Agents
- Customer Support AI
- RAG Systems
- Workflow Automation
06
Technology
Technologies listed by the Builder, distinguished by published evidence.
Evidence-supported
No published evidence linked yet.
Builder declared
- Anthropic Claude
- Docker
- FastAPI
- GitHub Actions
- JavaScript
- MySQL
- Next.js
- Node.js
- OpenRouter
- PHP
- Pinecone
- PostgreSQL
- PyTorch
- Python
- React
- Slack
- Supabase
- TensorFlow
- TypeScript
- n8n
07
Industry experience
Published Deployments support the listed counts. Other industries are Builder declared.
Data & Analytics
Builder declared
E-Commerce
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
Ready to discuss your AI implementation?
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