AI Architect for Regulatory Document Processor

@augurstech

A Warm Hello! We are readily available to start working on this project ! We are an AI engineering agency focused on production-grade, high-risk AI systems—not demos or chat toys. Our work sits at the intersection of LLMs, regulatory rigor, and software engineering, with a strong bias toward traceability, explainability, and operational safety. We will provide you Regulatory Watchtower, a private AI system to: • Continuously ingest and monitor evolving regulatory documents (pharma, compliance, legal) • Analyze and interpret changes with verifiable sources • Produce outputs that are: • Explainable • Auditable • Resistant to hallucinations • Suitable for real regulatory environment We would welcome the opportunity to discuss: • Your regulatory domains and jurisdictions • Risk tolerance and audit expectations • Deployment constraints (cloud vs private) • What “success” looks like beyond the POC Regards Ana

@Guptapuru304

We have successfully delivered similar projects, focusing on AI systems that are explainable, auditable, and resistant to hallucinations. Understanding the need for a robust AI architecture for your Regulatory Watchtower, we're prepared to design and implement a production-grade RAG pipeline ensuring traceability and reliability. With over eight years of experience, our team excels in AI-first product development, especially with LLMs like OpenAI and local models such as LLaMA. Our expertise in FastAPI, vector databases, and document processing aligns perfectly with your project requirements. We are equipped to transition your POC into a production-ready system, leveraging our skills in Python, AI development, and regulatory documents. Our portfolio showcases projects involving scalable applications and intelligent systems, demonstrating our commitment to uncompromised quality and transparent collaboration. Let's discuss how we can bring value to your project. Q: Are there specific regulatory documents or jurisdictions you want us to focus on? Q: What are the key performance indicators for the system's success? We look forward to the opportunity to contribute to your project. Thank you for considering our expertise. Best regards, Puru Gupta

@ZohaibRoy

⭐⭐⭐⭐⭐ Design a Reliable AI System for Monitoring Regulatory Documents ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and noticed you're looking for an AI/LLM Architect. Look no further; Zohaib is here to assist you! My team has successfully completed 50+ similar projects for AI architecture and implementation. I will create a robust AI system that is explainable, auditable, and reliable, ensuring it meets all regulatory standards. ➡️ Why Me? I can easily do your AI architecture project as I have 5 years of experience in Python development, working with LLMs, and implementing RAG pipelines. My expertise includes document processing, prompt engineering, and using vector databases. Additionally, I have a strong grip on technologies like FastAPI, Git, and Docker, which are vital for this project. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. I look forward to discussing this with you in our chat. ➡️ Skills & Experience: ✅ Python (Production-level) ✅ LLMs (OpenAI, Azure OpenAI, LLaMA) ✅ RAG Architectures ✅ Vector Databases (FAISS, Qdrant) ✅ Document Processing (PDFs, OCR) ✅ Prompt Engineering ✅ FastAPI ✅ Git ✅ Docker ✅ System Design ✅ Regulatory Compliance ✅ Monitoring & Evaluation Waiting for your response! Best Regards, Zohaib

@diektech

Hi, this is Elias from Miami. I read your description for Regulatory Watchtower and I’m aligned with the mindset: production-grade RAG, explainable + auditable outputs, source integrity, and real guardrails (not “chatbot vibes”). What I’d focus on in the first 4–6 weeks: ingestion + normalization (PDFs, OCR when needed, metadata + versioning) retrieval design (chunking strategy, hybrid search if needed, rerankers, grounding) answer layer (strict citations, refusal rules, confidence signals, trace logs) eval harness (test set, regression checks, hallucination probes, audit trails) FastAPI + Docker setup with clean docs so the team can extend it safely Q1: What’s the “unit of truth” you need to cite — page-level, paragraph-level, or clause/section-level (e.g., CFR/EMA style numbering)? Q2: Do you need full change detection (diffs across versions + impact summaries), or is it enough to answer questions against the latest corpus first? Q3: What’s your deployment constraint — Azure/OpenAI allowed, or do you need private/on-prem models only (and any data residency requirements)? If you share a small sample corpus + 5–10 example queries you care about, I can propose the POC architecture + milestones immediately. Regards, Elias

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in Python, UML Design, Machine Learning (ML), PostgreSQL, Database Development, OpenAI, Azure OpenAI, AI Development and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.    Best regards, Sales department Tangram Canada Inc.     

@WaleedKhader

I'm in the feild sibce it wasn't a hype yet. I built a two systems exactly like this before. Let's chat and design my third one.

@chraheelbhatti

As an experienced PhD researcher and Senior Machine Learning Engineer, I offer a unique mix of academic brilliance and practical expertise in the realm of AI architecture. I have a comprehensive understanding of Machine Learning models, especially in Computer Vision and NLP - including the exact LLMs your project requires (OpenAI / Azure OpenAI and/or local models like LLaMA). My proficiency with Python, FastAPI, Git, Docker is battle-tested in deploying robust ML systems. Lastly, my prior work's focus on applicability in regulated environments empowers me to offer the desired insights for your complex regulatory documents. With this background, I am equipped to support the transition from POC to production as well as efficiently mitigate hall

@sasaprogramer21

I will build production grade RAG systems for high risk domains where traceability, auditability, and hallucination resistance are mandatory, not optional. In similar regulatory and compliance document workflows, I delivered a pipeline that ingests PDFs with OCR when needed, normalizes text, applies tuned chunking and embeddings, and retrieves evidence with strict source integrity so every claim is tied to a cited passage. I also implemented answer reliability guardrails like retrieval confidence thresholds, quote first drafting, and structured evaluations to detect unsupported statements. For Regulatory Watchtower, I would design an explainable architecture in Python with FastAPI, Docker, and Git, using OpenAI or Azure OpenAI with a vector store such as Qdrant or Weaviate and PostgreSQL for metadata and audit trails. You will get UML level architecture reasoning, clear documentation, and a POC that is ready to harden into production with monitoring and continuous evaluation. I am eager to work with you as a trusted, professional partner and deliver this with strong guardrails and confidence. Time duration: 4 to 6 weeks

@ail5a9f298b597ed

With a deep understanding of AI and its integration into complex systems, I am confident I am the right candidate for the AI Architect position you are seeking. My extensive knowledge in Python and experience with production-level work gives me an advantage in designing a robust RAG pipeline that is explainable, auditable, and resistant to hallucinations. I've also worked with prominent LLMs like OpenAI and Azure OpenAI. One of my notable strengths lies in document processing, ranging from PDFs to OCR to regulatory texts. I possess a strong grip over prompt engineering and response evaluation skills which ensures impeccable traceability, source integrity, and consistent answer reliability - an essential aspect of your project. Additionally, my proficiency in FastAPI (or similar) means seamless integration with your existing system. Moreover, my experience across industries such as regulatory, legal, pharma, compliance, and finance will undoubtedly provide valuable insights during the development process. I'm well-versed in AI Agents / workflow orchestration and have set up on-prem AI solutions previously. Besides, I am well aware of the limitations and risks that come with utilizing LLMs thus can ensure their efficient monitoring & evaluation leaving no room for Hallucinations.

@va01v

As a seasoned AI and ML expert, my name is Alessio and I am thrilled about the opportunity to become your project's lead AI Architect. I have walked the walk and talked the talk of designing and implementing real-world AI systems for over 7 years, including working extensively with OpenAI models - which you specifically mentioned. Over the course of my career, I have gained incomparable expertise in dealing with text data, natural language processing, and data science problems. Moreover, my mastery extends to numerous components of your project requirements such as designing an auditable and explanaible architecture with a deep understanding of LLM limitations and robust RAG pipelines - ensuring answer reliability throughout. My technical acumen includes FastAPI (or similar), Git, Docker that would enable an stimulated command at functions of Pipelines Designing/Runtime running/ implementation making transition from POC to production smoother. Lastly, but not limited to the above skills are my efficiency in writing clean documentation paired with a comprehensive knowledge of designing architectural algorithms would result in your work being traceable and source integrity safely guarded protecting your work from any unwanted hallucination risk. With me onboard not only- will deliver produce top-notch execution but one backed by professionals who value system thinking over all else ensuring your Regulatory Watchtower stands tall even against intense scrutiny!

@saadrizwan97

Hi, For the POC, what are the acceptance criteria for an “Impact Alert”: required output fields (impacted SKU/BOM item + market/country + effective date + severity/traffic-light) and required audit artifacts (evidence snippet with page/section citation + source version hash + trace log of retrieved chunks/model/prompt version)? Hi NextSquareAG, Systemapic team here. Thanks for sharing the Regulatory Watchtower brief + slides. This is the kind of work we specialize in: building production-grade AI systems for regulated environments (not chatbot demos). We can design the full LLM/RAG architecture and implement an auditable pipeline: ingestion + versioning of regulatory docs, PDF/OCR processing, entity extraction, portfolio linking (SKU/BOM/market), and evidence-based outputs. Key point: every answer is traceable (source citations, document version/date), with confidence scoring + “abstain” logic to reduce hallucinations. Outputs can include alerts + dashboard-ready structured results (which SKU impacted, where, when effective, why). We suggest a 4–6 week POC to validate accuracy, traceability, and retrieval quality, then scale into production. Let me know if you’re available for a quick chat.