We’re hiring an extraordinary developer to own and grow our Base44 apps and sales products. around the future of AI discovery 1. Future of AI Discovery Core Demo – https://pull-discovery-core.base44.app/ You’ll evolve https://pull-discovery-core.base44.app/ into a beautiful, fluid, high‑performance, full-functional future of AI discovery demo following our advanced and sophisticated technical blueprint Integrate and orchestrate AI models incorporating LLM's, Search and World Models into a seamless experience with no visible seams between UX and intelligence. Own front‑end performance, responsiveness, and micro‑interactions—animations, transitions, and state changes should feel intentional and “alive,” not bolted on. Implement robust logging and analytics to understand how users explore, where they get stuck, and how the discovery engine can adapt dynamically. 2. Book Sales Engine – Six‑Channel Publishing System The second current Base44 project is a system that operationalizes our comprehensive sales plan across six channels. SEE THE COMPREHENSIVE BOOKSALES PLAN ATTACHMENT UNDERNEATH THIS POSTING You will: Translate a detailed multi‑channel publishing strategy (KDP optimization, physical bookstores via IngramSpark, other digital platforms, libraries, bulk institutional sales, and authority‑engine content marketing) into concrete workflows, tools, and dashboards. Build internal interfaces and automations to: Track metadata, pricing, and promotions across Amazon KDP and other platforms. Monitor campaigns across TikTok, Meta, LinkedIn, YouTube, newsletters, and partnerships. Surface KPIs like BSR, review velocity, ad spend, email growth, library adoptions, and bulk orders in a single, coherent view. Design light internal UIs that make it easy for non‑technical team members to update copy, add titles, trigger campaigns, and view performance without breaking anything. Implement robust, testable integrations between Base44, external APIs, and data sources to keep everything in sync as we scale from 8 to 22+ titles and beyond. Who You Are We’re not looking for a generic “full‑stack dev.” We’re looking for an unusual combination of visionary and doer: Creative technologist mindset – You think in systems and interfaces at the same time. You care deeply about how a product feels as well as how it works. Obsessed with execution – You’re disciplined, structured, and relentless about shipping. You break ambiguity into sprints, reduce complexity into tickets, and never let projects stall. Proactive owner – You don’t wait for instructions. You propose better ways to do things, flag risks early, and bring options—not problems—to every conversation. Strong product sense – You can balance ideal UX with realistic constraints and understand when to ship v1 vs. when to invest in polish. Comfortable with complexity – Multi‑channel distribution, layered data flows, and evolving requirements don’t scare you; they energize you. Ideal Skills & Experience You don’t need all of these, but you should recognize yourself in most: 5+ years building production web applications, ideally with a strong front‑end/UI focus. Deep experience with modern web stacks (React/Vue/Svelte or similar) and TypeScript, plus comfort with Node or comparable back‑end runtimes. Strong visual/UI instincts: experience collaborating with designers or owning design yourself for data‑rich interfaces and dashboards. Experience integrating AI/LLM APIs and retrieval systems into real products (RAG flows, multi‑step tool use, chat‑like interfaces, recommendation engines). Experience with analytics and experimentation: event tracking, funnel analysis, A/B testing. Familiarity with publishing, ecommerce, or multi‑channel marketing systems is a plus (KDP, IngramSpark, email platforms, ad platforms, analytics). Prior work in environments like Base44 or other low‑code/agentic platforms is a strong plus, but not required if you learn fast.

I am looking for an experienced AI engineer to fix one critical issue in an existing AI agent. The AI agent is already built and working correctly in most workflow sections. It is a document processing and structured data extraction agent. The agent receives uploaded documents, analyzes the content, extracts required fields, validates the extracted information, and generates a structured final output for the application. However, there is one critical issue in the validation and final-output generation section of the workflow. In some cases, the agent marks the process as completed and returns a final result even when required extracted fields are missing or the validation step has failed. This causes the application to treat incomplete or invalid document data as successfully processed. This is not a full rebuild. I only need an experienced engineer to investigate the existing workflow, identify the root cause, and fix this specific issue. Detailed technical documentation is attached. Please review it before applying. Timeline: Urgent / start as soon as possible Please apply only if you have experience AI agents, LLM workflows, tool/function calling, validation logic, and state management.

Project Title: Build Fast Web-Based AI Anime Companion MVP (RAG + Merch Gen) – 15–30 Days, $25K Budget: $18,000–$25,000 fixed Timeline: 15–30 days (3 weeks preferred) Must-Have Skills: FlowiseAI (or LangChain/LlamaIndex), RAG pipelines, OpenAI/Claude/Grok, anime image generation (Leonardo.ai/Ideogram/PixAI), Vercel or Railway deployment Project Goal Build a mobile-friendly web AI Companion for Big A Anime that converts passive FAST viewers into active fans. The key objective is to connect our Pluto TV channel experience directly to the AI companion, allowing viewers to scan QR codes during live programming and instantly access interactive content, recommendations, and merch tied to what’s currently airing—no downloads required. Core MVP Features Web Chat Interface Clean anime-branded chat UI with voice input Mobile-first, responsive Custom domain (e.g., companion.biganime.tv) RAG Knowledge Base Ingest episodes, schedules, transcripts, and metadata Provide accurate recaps, lore, and “what’s on now/next” tied to Pluto TV programming Session memory + light user profiles AI Merch Generator Anime-style image generation (“me as [character]”) Leonardo.ai or similar integration Export + links to Printful/Printify FAST / TV Integration Tools Dynamic QR codes for on-screen use Deep linking between Pluto TV programming and companion experience Voice-friendly prompts (“Ask Big A Companion…”) Admin & Analytics Simple CMS for content uploads Dashboard: usage, queries, merch clicks Technical Requirements Global hosting (CDN) FlowiseAI preferred Full source + documentation 30 days post-launch support Out of Scope Native apps, payments, deep integrations, multi-language Deliverables Live URL, admin access, training, source code, 30-day support Application Fixed bid + 3-week plan 2–3 relevant project links Willingness for small paid test ($500–$800)

Project Overview We are building an AI-powered voice receptionist agent for a dental practice client. The agent will handle inbound calls 24/7 — booking, rescheduling, and canceling appointments, answering FAQs, and escalating complex situations to a human. The voice layer is built in Retell.ai and the orchestration/automation flows run in n8n, with integration into a dental practice management systems. This is a hands-on build role, not consulting. You will design, configure, and iterate the agent working directly with our founding team. What You'll Build: Conversational voice flows for new and returning patients (booking, rescheduling, cancellations, FAQs, insurance questions, urgent/emergency triage) Clean escalation paths to human staff when the agent can't handle the call n8n workflows connecting the voice agent to the practice management API for real-time schedule reads/writes HIPAA-conscious configurations and tool choices throughout the stack Performance tracking and iterative prompt/flow improvements based on real call feedback Seeking an Expert Who: You've built multiple production voice agents, ideally in healthcare or dental, and can own the full stack from architecture to delivery. You think critically about latency, barge-in behavior, edge cases, and HIPAA compliance — not just happy-path flows. Requirements: Deep expertise in Retell.ai — prompt tuning, latency optimization, interruption/barge-in handling, LLM selection, and cost modeling Advanced n8n automation — complex branching logic, dynamic data handling, external API integrations, and error recovery Experience with dental or healthcare organizations — you understand how practices operate, what front desks actually deal with, and how to translate that into agent logic HIPAA awareness — you know which tools and configurations are appropriate for PHI workflows Track record of full-cycle delivery: scoping → build → pilot → iteration Nice to have: Dentrix Ascend integration, NexHealth, prior AI receptionist or appointment-booking agent for a clinic/practice. 📋 To Apply, Please Answer Have you built a production voice agent with Retell.ai? If yes, briefly describe it. What's your approach to managing Retell.ai per-minute costs on an inbound call agent? Have you worked with dental or healthcare clients before? What was the workflow? Paste a relevant n8n workflow screenshot or describe your most complex flow.

Overview We’re looking for an experienced AI engineer or AI systems builder to help us design and build an internal intelligence layer that turns fragmented customer data into actionable growth opportunities. Right now, customer insights live across multiple disconnected systems — CRM notes, product usage data, emails, support tickets, and spreadsheets. While the data exists, it is not structured in a way that helps us proactively identify expansion opportunities, churn risks, or account-level next steps. We want to build an AI-driven system that continuously synthesizes this information and helps our team understand: * What is happening inside each account * Where expansion or upsell opportunities exist * Which accounts are at risk and why * What the next best action should be for each customer ⸻ What You’ll Build You will design and implement an AI system that can: * Ingest structured and unstructured data (CRM, emails, notes, product signals) * Build dynamic “account intelligence profiles” for each customer * Identify patterns across accounts (usage drops, feature gaps, expansion signals) * Generate clear, human-readable account summaries * Recommend next-best-actions for sales, customer success, or leadership * Surface expansion opportunities based on behavioral and contextual signals * Flag risk signals early with supporting reasoning ⸻ Ideal Output For each account, the system should be able to generate: * A concise account narrative (“what’s going on here”) * Key signals and anomalies * Expansion opportunities (with rationale) * Risk factors (churn or stagnation indicators) * Suggested actions for the team this week * Confidence level and supporting evidence ⸻ Why This Matters We are sitting on a large amount of customer data, but most of it is passive. The goal is to turn it into an active intelligence system that helps our team: * Prioritize the right accounts * Increase expansion revenue * Reduce churn risk * Spend time on the highest-impact opportunities This becomes a core internal system that directly impacts revenue efficiency and customer outcomes. ⸻ Ideal Candidate We’re looking for someone with experience in: * LLM-based systems and agentic workflows * Data pipelines and multi-source data ingestion * Prompt engineering + structured reasoning systems * CRM systems (Salesforce, HubSpot, etc.) * Customer analytics / product analytics * Building internal AI tools or copilots * Backend + API integration work Bonus if you’ve worked on: * RevOps tooling * Customer success platforms * Data enrichment or account intelligence systems * SaaS growth analytics ⸻ Deliverables * System architecture for AI customer intelligence layer * Data ingestion and normalization approach * Prompting / reasoning framework for account analysis * Prototype system (or working MVP) * Output format for account intelligence reports * Documentation for internal expansion and scaling * Recommendations for tooling (build vs buy decisions) ⸻ Engagement This starts as a project-based build, but could expand into a long-term role as we scale the system across our entire customer base and additional workflows. ⸻ To Apply Please include: * Examples of AI systems or agentic workflows you’ve built * Experience integrating LLMs with real business data * Your recommended architecture for a system like this * Any clarifying questions you’d want answered before starting

We’re a product-focused team developing an AI-driven SaaS platform aimed at improving how businesses handle information, workflows, and internal processes. We’re looking for a developer who can build a smart feature that allows users to interact with their data and documents in a more automated and intuitive way. The goal is to reduce manual effort and enable faster, more informed actions through AI. This role involves designing and implementing the core logic behind the feature, connecting AI capabilities into the product, and ensuring everything works smoothly within the existing system. You should be comfortable building end-to-end functionality, working with modern web applications, and integrating AI services into real user-facing features. Experience with AI-powered products, automation tools, or data-driven applications is highly relevant.

We are looking for a hands-on Forward Deployed AI Engineer to help build practical AI systems This is not a pure backend role and not a strategy-only consulting role. You will work close to end users, understand how their workflows actually operate, and then build AI-enabled tools that solve specific business problems. The ideal person is a strong software engineer who is comfortable with ambiguity, can communicate clearly with non-technical stakeholders, and can take an AI prototype from idea to something reliable and usable. What you will do - Learn the business workflows, systems, data, and constraints. - Build AI applications using Claude or similar large language models. - Use the right mix of prompting, retrieval, tool use, agents, and workflow automation. - Own delivery from scoping through prototype, testing, hardening, and handoff. - Create evaluations to determine whether the system is accurate, reliable, and safe enough to use. - Translate between domain experts and technical implementation. - Work carefully with sensitive or regulated data. - Document what you build so it can be maintained and reused. What we are looking for - Strong Python engineering skills. - Hands-on experience building with LLMs, preferably Claude or the Anthropic API. - Experience with RAG, structured prompting, tool use, evaluation, or agentic workflows. - Ability to operate independently in a messy, ambiguous environment. - Strong communication skills with both technical and non-technical stakeholders. - Track record of shipping working software, not just demos. - Comfort working with real-world data, integrations, and imperfect requirements. Helpful but not required - Prior forward deployed engineering, solutions engineering, or technical consulting experience. - Experience building AI tools for enterprise customers. - Experience in regulated or sensitive-data environments. - Familiarity with validation, auditability, traceability, or compliance-oriented workflows.

Looking for an experienced AI developer to help build an AI agent using Claude. Requirements: Experience building AI agents and autonomous workflows Strong experience with Claude and Anthropic models Ability to integrate external data sources and APIs Experience deploying production-ready AI solutions Please include: Examples of similar AI agent projects you've built Your experience with Claude Your recommended tech stack Estimated timeline and cost Looking to start immediately.

I need a advanced agentic system built with persistent memory and up to 6 agents that work together. I am building a franchised coffee shop business. there is so much data that can be pulled together and harvested from customer spending habits and also what is the highest grossing items that sell , vs the most profitable hours of the day. All that data needs to be meshed with the actual Quickbooks data and financials. All that then needs to be balanced with real world site selection for new coffee shop locations. Here is what I need: Agent 1. Pulls information Directly from clover POS automatically. Agent 2. takes Agent's 1 information and cross references with Margin Data from Quickbooks. Recommends New drinks that are both on trend AND High Margin. Agent 3 is the financial Agent. It works directly with Quickbooks. It monitors cash flow and alerts when labor percentage exceeds parameters. It also stress tests expansion and " what if" scenarios. Agent 4. the site selection agent. agent 4 monitors LoopNet, Costar, and parcel data for commercial land available. It cross references traffic count and demographics, it checks competitor coffee presence etc. Agent 5 is the capital strategist. when agent 4 finds a location, it consults with agent 3 which is connected to Quickbooks , it models out loan scenarios, cash flow impact. and helps run " what if " scenarios that it gets asked. Agent 6 is the main Orchestrator that runs everything that I would communicate soley with through Whatsapp. It connects all the agents and pulls data collectively and makes them all work together and stress tests ideas that one agent might find.

We are an investment firm with a portfolio of healthcare companies. We are seeking to begin building our data capture systems across our business and layer AI to surface summarize and store insights. This is a process that is in parallel to our operations team SOP'ing our process in anticipation of expansion. It is our opinion that we have a relatively simple business process from end to end and lots of potential to capture useful data signals across each department/function. We have drafted a rough business process / data ontology diagram showing our preferred approach. We are seeking an expert to: 1 ) Create lightweight data systems to capture data signals from end to end across our business (Recruiting to Onboarding to Scheduling to Payroll to Finance to Legal to). This also includes organizing and categorizing our past / existing data in addition to capturing signals for future data. 2 ) Layer AI / agentic AI automations that can surface insights, categorize and aggregate info, populate knowledge databases, etc. Example Data Signals / Use Cases: Fireflies recorded meetings Tagging emails in inbox as Legal/Finance/Scheduling/Onboarding etc Job Board Postings Airtable (For building a lightweight scheduling/employee management system) (For storing a knowledge database and rolodex) To Apply: Please briefly present an instance of implementing a similar lightweight solution to capture data signals and convert the data into meaningful and actionable insight via AI