Agentic AI Engineer to help optimize our media monitoring stack

@Herculesz

Hi I reviewed your brief and can help optimize the existing agentic media monitoring stack rather than rebuilding it from scratch. The main technical challenge is improving relevance across 600–800 keywords and 600 sources while reducing false positives, token waste, duplicate stories, and weak social-platform coverage. I have experience with agentic AI workflows, retrieval architecture, hybrid keyword and semantic search, RAG, PostgreSQL pipelines, OpenAI/Claude APIs, structured outputs, prompt optimization, and evaluation loops. For your stack, I would first audit keyword logic, Exa query formulation, source quality, routing between agents, and where Telegram/OpenClaw handoffs are causing unnecessary context or missed signal. I can help design Boolean/proximity patterns, semantic reranking, dedup scoring, source reliability tracking, and relevance benchmarks so improvements are measurable instead of subjective. For X, YouTube, Meta, and LinkedIn, I would work around real API limits with compliant API-based ingestion, approved third-party providers, RSS/transcript sources, and source-specific monitoring strategies. Thanks, Hercules

@OutsourceMan

⭐⭐⭐⭐⭐ • CnELIndia team proposes optimizing your existing media monitoring stack as experienced Agentic AI Engineers. • Examples: Optimized OpenClaw orchestration for a policy think tank using Claude Sonnet, Exa semantic search, GPT models, and PostgreSQL; reduced false positives 35% via hybrid retrieval and tighter agent handoffs. • Stack read: Prioritize keyword Boolean/proximity precision and OpenClaw routing to cut token waste; recommend replacing Tavily with Brave Search for better policy coverage. • Relevance approach: Auto-generate scalable Boolean patterns with Claude, fuse with tuned Exa queries and reranking, then build PostgreSQL-backed evaluation loops for scoring, dedup, and source reliability at 600x600 scale. • Social platforms: X via official v2 API (paid tier) for full posts/transcripts; YouTube Data API for metadata/episodes; LinkedIn via official Graph API limits plus ethical third-party ingestion acknowledging constraints. • CnELIndia help steps: 1. Audit sources/keywords for gaps. 2. Tune hybrid retrieval and prompts. 3. Refine agent orchestration. 4. Deploy live eval metrics for measurable gains. • Rate: $120/hour; 4 weeks for initial precision/coverage improvements. • Question: What are your top 2 current pain points and success KPIs? {Return}

@corpmember29

AGENTIC AI ENGINEER WITH STRONG RAG, MULTI-AGENT ORCHESTRATION & MEDIA MONITORING OPTIMIZATION EXPERIENCE. Your stack is already thoughtfully designed. The biggest opportunities I see immediately are: • Hybrid retrieval optimization (Boolean + semantic reranking) • Query normalization across the 600-keyword matrix • Source reliability scoring & dedup intelligence • Better token economics inside OpenClaw orchestration • Smarter platform-specific ingestion strategies Platform handling: • X: API v2 + fallback ingestion services where needed • YouTube: Data API + transcript extraction pipelines • LinkedIn: realistic partial coverage via allowed public surfaces & enrichment workflows • Meta: Graph API compliant ingestion only Strong experience with: Claude, OpenAI, Exa, Tavily, PostgreSQL, RAG pipelines, vector search, AI agents, prompt engineering, API orchestration, and monitoring systems. I’m comfortable working inside existing architecture rather than replacing it, and I can contribute at both strategic and implementation levels quickly. Available for ongoing optimization work with rapid iteration cycles and measurable KPI improvements.

@AwaisChaudhry

With over a decade of experience in IT and engineering and a zeal for tackling complex challenges head-on, ZAWN Tech led by myself is the right choice for your media monitoring optimization project. We excel in building, improving, and operating agentic AI systems, making us proficient with orchestration, tool routing, and multi-agent handoffs which your project relies heavily upon. Our deep understanding of retrieval architectures like hybrid search and semantic + keyword fusion will significantly benefit your goal to enhance query precision at scale across 600 policy keywords and improve retrieval using Exa or its alternative. Familiarity with retrieval architecture is not complete without knowing prominent APIs; hence our expertise with Anthropic's Claude, OpenAI's GPT-5.4, among others. Being comfortable with PostgreSQL and structured data processing, we can capably optimize agent orchestration for more efficient token use and construct robust evaluation loops. In addition to our technical skills, our proficiency in understanding token economics through writing tight modular prompts can contribute immensely to your mission of reducing false positives while capturing genuinely meaningful stories from the policy conversation. In conclusion, choosing us would mean signing up for an outcome-oriented collaboration that respects token optimization as much as data quality.

@mobasherreza10

As an AI engineer with over a decade of experience, I have consistently focused my work on optimization rather than building from scratch, which makes me tailor-made for your project. My team and I understand the importance of pushing the boundaries of existing systems to deliver better quality and coverage. We can build upon your current stack using our deep working knowledge of OpenClaw, Hermes, and other frameworks you mentioned. In addition to strong expertise in AI models like Claude and OpenAI's GPT-5.4, we have hands-on experience with retrieval architectures like hybrid search, RAG, and semantic + keyword fusion, just to name a few. This is complemented by our proficiency in tools such as Exa, Tavily, Brave Search, as well as social platform APIs like X-API v2 and YouTube Data API that are crucial to your unique monitoring needs. However, what sets us further apart is our ability to not just optimize systems but also strategize evaluations to make a realistic measurement of progress. Prompt engineering for retrieval and structured outputs is one of our core competencies that will enable us to build effective evaluation loops for your project's success. Finally, having worked across diverse sectors including policy intelligence and public affairs gives us an added advantage in understanding\\ the nuances specific to media monitoring in your domain.

@Mahihassi

As an experienced AI and Cloud Developer with a strong background in building and optimizing agentic AI systems, I understand the nuances involved in improving an existing stack rather than starting from scratch. From working with open-source tools like OpenClaw to orchestrating models like Claude Sonnet and GPT-5.4 for retrieval tasks, I have the technical know-how to make your media monitoring system more precise, efficient, and effective. In dealing with your specific challenges of handling a high volume of keywords and diverse data sources, my expertise lies in developing intelligent search techniques that involve hybrid search, semantic retrieval, and keyword fusion for improved performance. Evaluating your current stack, including Exa and Tavily, I can also suggest complementary tools or replacement options where necessary. Moreover, my familiarity with media platforms such as X, YouTube, and LinkedIn API constraints will prove invaluable when capturing relevant policy conversations from these challenging sources. Besides optimizing these retrieval operations, I'll emphasize setting up proper evaluation loops so we can measure the real impact of the changes made – ensuring that the optimization efforts are well-documented in terms of relevance scoring, dedup quality, and source reliability tracking.

@amadorjay

Hello! I am interested in your project and confident I can deliver excellent results. Let's discuss your requirements so I can start immediately.

@Hubready

Hello!, I am a Florida-based senior software engineer with extensive experience in AI, data scraping, and automation. I read your project description carefully and understand the need for an optimized media monitoring stack, especially in the context of policy and public affairs. With around 15 years of experience in developing production-grade software, I have a deep understanding of AI model development and natural language processing. I’ve successfully implemented AI-powered solutions that streamline workflows and enhance data analysis. I have a few questions to help me better understand the project: 1. What specific metrics or outcomes are you hoping to achieve with this optimization? 2. Are there particular data sources or platforms you want to prioritize in your media monitoring? To tackle your project, I suggest a phased approach: first, assessing the current stack and identifying areas for improvement; next, implementing AI models for enhanced data processing; and finally, ensuring seamless integration and user-friendly dashboards. I’m dedicated to delivering high-quality, practical solutions that drive ROI. If you’re looking for an engineer who truly understands the requirements and is committed to your project's success, let’s chat. Looking forward to your response. -James

@hr157

Hello, With 8+ years of experience in AI development, agentic AI systems, and retrieval optimization, we can help enhance your existing media monitoring stack with a focus on precision, scalability, and low-noise intelligence capture. • Skills: Agentic AI, OpenClaw, Claude/OpenAI APIs, Exa, PostgreSQL, RAG, prompt engineering, semantic search, social monitoring APIs • Deliverables: Retrieval optimization, keyword precision tuning, source quality enhancement, orchestration improvements, evaluation loops, and social platform monitoring workflows We understand platform limitations and can provide practical, scalable solutions aligned with your current architecture. Let’s connect.

@noursherif030

With over five+ years of experience in software engineering, I excel at optimizing existing systems to deliver remarkable improvements. I've developed **full-stack web applications**, designed **IoT systems**, and built **desktop applications** from scratch. My broad skill set will come in particularly handy when improving your media monitoring stack for policy and public affairs. On the AI front, I am well-versed with orchestration layers like **OpenClaw** that you use and have working familiarity with **Hermes**, another agentic framework. I'm adept at working with retrieval architectures such as hybrid search and semantic + keyword fusion, which would be invaluable when strengthening semantic retrieval as you desire. My experience in both software engineering and medical coding demonstrates my ability to streamline processes to achieve desired outcomes. This will be crucial as we seek to improve your agent orchestration, web search, and social media capture, especially on platforms like X, YouTube, and LinkedIn. Overall, my expertise lies in optimization and problem-solving - and these are precisely what your project needs!

@Feriver

Hello I understand you’re looking for an experienced agentic AI engineer to optimize an existing media monitoring stack (not rebuild it), improving precision, retrieval quality, and orchestration across ~600–800 policy keywords and ~600 heterogeneous sources spanning news, podcasts, and constrained social platforms like X, YouTube, Instagram, Facebook, and LinkedIn. I will focus on tightening your current OpenClaw-based architecture by refining retrieval pipelines across Exa + keyword systems, improving Boolean/proximity query generation at scale, and introducing structured evaluation loops to measure precision/recall drift over time. I will also optimize agent routing between your specialist components (RSS, X ingestion, PostgreSQL persistence) to reduce redundancy, token waste, and low-signal outputs while improving ranking consistency for policy-relevant content. On the retrieval side, I will implement a hybrid strategy combining deterministic keyword expansion (synonyms, legislative variants, entity normalization) with semantic enrichment via Exa and reranking heuristics to suppress noise-heavy sources. For social platforms, I will design realistic ingestion strategies respecting API constraints—using official APIs where possible (X API v2, YouTube Data API), structured scraping fallbacks where compliant, and third-party enrichment services only where necessary, ensuring coverage without violating platform limitations. Thanks, Asif

@krisk63

Hi, this is Kris from McKinney, Texas, I've reviewed your project requirements and understand that you are looking for an experienced agentic AI engineer to optimize your existing media monitoring stack in the public policy space. The key challenges include improving keyword search precision, strengthening semantic retrieval, and enhancing source quality and coverage. My approach would involve refining Boolean/proximity/stemming patterns for 600 keywords, fine-tuning Exa for semantic retrieval, and evaluating hybrid keyword + semantic approaches to enhance overall performance. A few additional questions: Q1: How do you currently measure the effectiveness of your media monitoring system? Q2: Are there any specific KPIs or metrics you are aiming to improve through this optimization process? Q3: What are your expectations regarding communication and progress updates during the project? Best regards, Kris Kramer

@dorofiip

Hey, I am ready when you are.✅ I’ve worked on something very similar. What really matters here is optimizing an existing media monitoring stack without starting from scratch. The tricky part is improving keyword search precision, semantic retrieval, and source quality across 600+ sources and keywords. In a recent project, I fine-tuned retrieval architectures and improved query formulation for a similar setup. While I haven't specifically worked with OpenClaw, I have experience with similar agentic frameworks and AI systems. Let's chat! -Dorofii

@MIndlabsAi6

Hello, I am Vishal Maharaj, an AI expert with 20 years of experience in AI Agents, AI Development, AI Model Development, AI Research, AI Text-to-text, and Agentic AI. I have carefully reviewed your project requirements for optimizing your media monitoring stack in the public policy space. To enhance the quality, precision, and coverage of your existing system, I propose to: - Design advanced Boolean/proximity/stemming patterns for 600 keywords - Fine-tune Exa for semantic retrieval and suggest complementary tools - Audit and improve the reliability of 600 media sources - Optimize agent orchestration with OpenClaw and model handoffs - Enhance social media capture strategies for X, YouTube, and LinkedIn - Implement evaluation loops for relevance scoring and deduplication I have extensive experience with agentic AI systems, retrieval architectures, and tools like Claude (Anthropic) and OpenAI APIs. I look forward to discussing further details and initiating the project. Cheers, Vishal Maharaj

@YamestimationLLC

Hello there, I understand that you are seeking an experienced agentic AI engineer to optimize your existing media monitoring stack in the public policy space, with a focus on improving keyword search precision, semantic retrieval, source quality and coverage, agent orchestration, social media capture, and evaluation loops. My proposed solution involves leveraging my hands-on experience with agentic AI systems, retrieval architectures, Claude (Anthropic), OpenAI APIs, Exa, and social platform APIs to enhance the efficiency and effectiveness of your current setup. I will work closely with your team to fine-tune the existing system and implement improvements in line with your specific requirements. Key Deliverables: - Improved keyword search precision - Strengthened semantic retrieval - Enhanced source quality and coverage - Optimized agent orchestration I'll share my portfolio with you in the DM, showcasing my expertise in AI development and media monitoring. I bring a unique skill set that aligns with your project needs. I’d love to connect for a quick chat to discuss your project in more detail. Best regards, Minhal

@GenoraLabs

Hello, How do you envision optimizing your existing media monitoring stack to achieve higher quality, precision, and coverage? I suggest focusing on improving keyword search precision, strengthening semantic retrieval, and enhancing source quality and coverage. I plan to efficiently optimize your media monitoring stack by improving keyword search precision, enhancing semantic retrieval, increasing source quality and coverage, optimizing agent orchestration, improving social media capture, and building evaluation loops. Core Deliverables: - Improved keyword search precision - Strengthened semantic retrieval - Increased source quality and coverage - Optimized agent orchestration - Enhanced social media capture - Built evaluation loops Expertise & Portfolio: I'll share the portfolio with you in the DM. Kindly ping me there. My experience with agentic AI systems and retrieval architectures ensures quality, consistency, and smooth delivery. I’d be happy to discuss your project further and answer any questions. Best regards, Malaika

@josea317

Hi — I actually like how opinionated this stack already is. The “retrieval-by-reference + durable notes + small prompts” direction is the right instinct for this class of system. A few things immediately jump out to me as optimization targets: Your biggest leverage is probably not model quality, but retrieval precision and orchestration discipline. At 600×600 scale, small query inefficiencies explode into noise and token waste very quickly. I’d strongly pressure-test where semantic retrieval is helping vs hurting. In policy monitoring, pure vector similarity often over-expands topical adjacency and creates subtle false positives that look plausible but degrade analyst trust over time. I’ve worked on agentic/RAG-style systems using OpenAI + Claude stacks, multi-step orchestration, vector retrieval (Qdrant/Pinecone), structured extraction pipelines. I’m also very comfortable with PostgreSQL-backed ingestion pipelines, evaluation loops, and token-economics optimization. For your retrieval layer, I’d likely move toward: -hybrid retrieval with weighted keyword + semantic fusion -aggressive entity normalization/disambiguation -query classes instead of one-query-per-keyword patterns -reranking stage optimized for policy relevance, not generic similarity -source reliability scoring feeding retrieval priority I’m also interested in how OpenClaw currently routes model selection and memory/reference handling. My guess is there’s meaningful optimization available there alone.

@jay8mac

Lets chat, a free consultation and no obligation. I understand you need a clean, professional, and user-friendly solution for your "Agentic AI Engineer to help optimize our media monitoring stack" project. My skills in PHP, Java, JavaScript are a perfect fit for this project. While I am new to freelancer.com, my extensive experience delivers integrated, automated solutions. Regards, Jason McLachlan

@alizain721

Nice brief — I read the stack and your constraints carefully. You already have the right pieces; the quick win is tightening retrieval, not swapping everything. I’ve worked directly with OpenClaw-style orchestration and Exa in production for a policy monitoring pipeline, where tightening boolean templates plus a small neural reranker cut obvious false positives and raised useful signal without expanding cost. My take on your stack: start with retrieval and prompt handoffs. I’d audit your 600 keywords for pattern families, generate programmatic boolean/proximity variants, add hybrid ranking (keyword boost + Exa semantic rerank), and reduce token waste by moving authoritative context into short references. Tavily can be replaced with Brave Search plus targeted crawlers for policy outlets; keep general web search for discovery. Platform handling: X — budget for paid API + third-party fallback; YouTube — Data API plus transcript parsing; LinkedIn — monitor public pages/profiles and use partner ingestors, not scraping; Meta — use Graph API where possible and respect scraping limits. Rate: $20/hr. Expect visible precision gains in ~2 weeks (40–80 hours). Can you grant read-only access to the repo and a sample week of hits so I can draft a prioritized plan?