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Mahbub Rahman
Mahbub Rahman
Available for new projects

AI Computer Vision & Image Processing Developer

Give your application eyes.

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EXECUTIVE SUMMARY

Mahbub Rahman integrates advanced computer vision APIs (OpenAI Vision, Claude 3.5 Sonnet) and image processing pipelines into Next.js SaaS applications for automated visual analysis.

The Technical Reality

Sending raw, high-resolution user uploads to an AI Vision model is a massive waste of API credits and latency. I architect vision pipelines that pre-process, compress, and compress images on the client or edge before they ever hit the LLM, ensuring you get accurate visual analysis at a fraction of the cost.

THE VISION BOTTLENECK

Images are heavy.Analysis is slow.
THE COST

Vision APIs are expensive.

Sending a 10MB 4K photo from an iPhone to GPT-4o Vision eats up thousands of tokens. You need automated image optimization and resizing before the API call.

Optimized Token Usage
THE RELIABILITY

The AI misses details in complex images.

You can't just pass an image and ask 'what is this?'. You need precise system prompts, bounding box coordination, and structured output schemas to extract exact data from forms or photos.

Structured Extraction
THE STORAGE

Your database is bloated.

Storing raw user uploads in base64 strings will destroy your database performance. You need proper cloud bucket architecture (S3/R2) with CDN delivery.

CDN-backed Storage

WHAT I BUILD WITH

Processing pixels.No hand-offs required.

From database to deployment. I own the whole thing.

VISION APIs
GPT-4o Vision
Claude 3.5 Sonnet
Google Cloud Vision
PROCESSING
Sharp (Node.js)
Browser-image-compression
STORAGE
AWS S3
Cloudflare R2
Presigned URLs
BACKEND
Next.js
Zod Validation
PostgreSQL

THE VISION PROCESS

From pixel to payload.

We build a pipeline that extracts structured data from visual noise.

01

Client-Side Optimization

Save bandwidth

We implement in-browser image compression before upload, converting heavy HEIC/JPEG files to optimized WebP formats to dramatically reduce upload times and API costs.

02

Prompt Engineering for Vision

Guiding the eye

Vision models require highly specific prompts. We design instructions that tell the AI exactly where to look and what format to return the extracted data in.

03

Structured Extraction

Typed JSON

We enforce strict JSON schemas (using Zod) so that the vision model doesn't just return a descriptive paragraph, but actual key-value pairs your database can store.

PROVEN RESULTS

TryOn Live
ShopifyTypeScript

TryOn Live

AI-powered virtual try-on Shopify app for clothing and accessories using GCP Vision API for real-time fitting.

Read Case Study→

COMMON QUESTIONS

Questions aboutalways ask me.

Making sense of multimodal features.

It depends on the task. Claude 3.5 Sonnet is currently exceptional at reading complex charts, graphs, and dense UI screenshots. GPT-4o is excellent for general object recognition and real-world photos. We can benchmark both for your specific use case.

Yes, modern multimodal models are incredibly good at OCR (Optical Character Recognition), even with messy handwriting. However, if OCR is the ONLY goal, traditional tools like AWS Textract or Google Document AI are often faster and cheaper than an LLM.

By default, data sent via the OpenAI API (unlike the consumer ChatGPT app) is NOT used to train their models. We also implement zero-data retention policies on our end, ensuring images are processed and immediately deleted if they contain PII or PHI.

NEXT STEP

Resume, email,or a 30-min call.

Recruiters: download the PDF or email me. Founders: book 30 minutes. No pitch deck. Calendar is Pacific Time.

Download Resume
Contract / fractionalW-9 · full IPPT / CT / ET overlap