New · My Approach to AI-Assisted Web Development: An SDLC

~/agents — dependency-audit

$ agent run --task "audit and patch dependencies"

✓ loaded 42 tools · policy: least-privilege
✓ scanned package-lock.json · 440 packages
! found 13 advisories (1 critical, 5 high)
✓ 3 stale overrides pinned inside advisory ranges
✓ patched · typecheck, tests, build all green

 13 advisories → 0 no breaking changes

Raoof Altaher

I build production AI systems and advise leaders on AI strategy.

AI Engineer & Strategic Advisory ConsultantPhD Candidate, Computer Engineering

I design, build and ship agents, automations, RAG pipelines, AI services and APIs for enterprise SaaS, and I advise leadership on roadmaps, build‑versus‑buy decisions and governance.

68 peer reviews for 6 journals, including Neural NetworksPublished with Taylor & Francis and MDPI

What I do

Hands-on engineering, combined with technical leadership: three areas I build, three I advise on.

Secure & Responsible AI

Guardrails, evaluations, observability and human oversight, with prompt-injection prevention, PII protection and rate limiting.

Custom AI Tooling

MCP servers, skills, plugins and long-term memory, connected to self-hosted models, cloud AI APIs and orchestrators such as LangGraph and CrewAI.

AI Systems & Agents

Agents, RAG pipelines, model workflows, APIs and automations, built, deployed and monitored end to end in .NET and Python.

AI Strategy & Roadmaps

AI opportunities, feasibility, risks and build-versus-buy decisions, turned into a roadmap with clear execution priorities.

Goals to Technical Plans

Business goals turned into technical plans, together with engineering, product, security, compliance, sales and executive teams.

Team Enablement

AI coding agents with pre-configured plugins and workflows, plus architecture guidance, code review, mentoring and workshops.

From the blog

All posts

Pinned

Introducing UltrapowersA development method your coding agent follows. It asks before it codes, plans before it builds, tests first, reviews every task, and stops at your gates. One command takes a ticket to a pull request; you approve, you merge.read AI-Native SDLC with UltrapowersA practical walkthrough of the AI-native SDLC using Ultrapowers: the six-stage mapping, one ticket from brief to reviewed pull request, skill pipelines, human gates and current limitations.read The AI-Native SDLC: A Developer's PlaybookA practical guide to the AI-native software development lifecycle, grounded in Anthropic's six-stage model and focused on artifacts, verification, human decisions and operational feedback.read

Latest

My Approach to AI-Assisted Web Development: An SDLCA practical, experience-led workflow for building websites with AI, from visual research and architecture to implementation, security, accessibility, and QA.read Decision Models: Fine-Tuning LLMs for Structured DecisionsHow decision models score predefined options instead of generating text, and how Unsloth trains, evaluates and calibrates them with a dedicated decision head.read AI Agents Need More Than Guardrails: Engineering Secure Agentic SystemsA practical guide to securing AI agents through independent policy enforcement, least privilege, runtime isolation, network controls, credential protection, and adversarial testing.read