Ten end-to-end products, one per domain. Each brief gives you the problem, a plan, a tech stack, and the key features to build. These are specs to build from, not finished solutions.
Problem. Patients and front-line clinicians can't get fast, guideline-grounded answers, triage is inconsistent between staff, and documentation eats clinician time. A generic chatbot is unsafe here.
Plan. Scope with a clinician and a compliance owner first. Ingest approved guidelines and a formulary into a permissioned RAG index. Build a triage agent with hard safety rails, then a clinician review queue and audit trail. Run a clinician-graded eval set before any pilot, then deploy inside a private cloud tenant.
Tech stack. Python + FastAPI, LangGraph, Azure OpenAI or on-prem vLLM, Azure AI Search / pgvector, Pydantic guardrails, Next.js, Postgres, Langfuse or Phoenix, Docker.
Problem. Bank support lines are slow and costly, agents repeat the same policy lookups, and risky requests like disputes or suspected fraud need careful, compliant handling.
Plan. Build RAG over policies and a SQL agent over a transactions table. Layer a no-advice guardrail and PII masking on every turn. Add fraud-pattern flagging and a clean human handoff, then a quality dashboard and weekly eval reports.
Tech stack. Python + FastAPI, LangGraph, Qdrant or pgvector, Postgres, an LLM via API, Presidio for PII, Next.js, LangSmith.
Problem. Lectures are one-size-fits-all, teachers spend hours building materials, and students get stuck with no on-demand help that doesn't just hand them the answer.
Plan. Build a Socratic tutoring agent that gives graduated hints with no answer leakage. Add a course-generation pipeline from a syllabus, rubric-based grading, and a teacher dashboard. Include an offline overlap check on submissions.
Tech stack. Python + FastAPI, LangGraph, RAG over course content, Next.js, Postgres, an open or hosted LLM, DeepEval, an offline self-overlap checker.
Problem. Recruiters drown in resumes, screening is inconsistent and bias-prone, and employees can't get quick answers to policy questions.
Plan. Build bias-aware resume-to-JD matching, structured interview-kit generation, and an employee policy bot over the handbook. Add fairness checks, PII redaction, and a hiring-funnel dashboard.
Tech stack. Python + FastAPI, sentence-transformers and a vector DB, LangGraph, Next.js, Postgres, Presidio, an LLM via API, an eval suite for fairness and consistency.
Problem. Listing search is keyword-only and dumb, buyers can't quickly judge whether a deal is good, and listing documents arrive as messy PDFs.
Plan. Build hybrid geo plus semantic listing search, vision tagging on photos, document extraction from listing and contract PDFs, and a deal-analysis agent. Surface it in a map-based UI with a lead chatbot.
Tech stack. Python + FastAPI, a vector DB with geo filters, a vision-language model, OCR (Docling or Qwen-VL), Next.js with a maps SDK, Postgres / PostGIS, an LLM via API.
Problem. Company knowledge is scattered across Drive, Confluence, Slack, and tickets, internal search is poor, and answers must respect who is allowed to see what.
Plan. Build connectors that sync sources, hybrid retrieval with reranking, permission-aware (ACL) filtering, and a GraphRAG layer for entity questions. Add a feedback loop and continuous evals.
Tech stack. Python, a hybrid backend (OpenSearch or Elasticsearch plus vectors) or Azure AI Search, a reranker, GraphRAG, Next.js, an LLM via API, Ragas or Phoenix.
Problem. Non-developers in ops, support, and sales need to build and govern their own agents without writing code, and IT needs governance over what gets shipped.
Plan. Build on Microsoft Copilot Studio and Azure AI Foundry Agent Service, adding a thin layer for a connector registry, governance, and evals. Let users upload knowledge, wire tools visually, test in a sandbox, and publish to Teams or web.
Tech stack. Microsoft Copilot Studio, Azure AI Foundry (Agent Service and model catalog), Azure AI Search for knowledge, Azure Functions for custom tools, Entra ID for RBAC, Foundry evaluations and Application Insights.
Problem. A regulated org wants AI built only on managed Azure services, not self-hosted models, turning forms and recorded calls into structured, searchable, compliant data.
Plan. Use Azure AI Document Intelligence for forms, Azure AI Speech for call transcription and sentiment, Azure OpenAI for reasoning and summaries, Azure AI Search as the index, and Content Safety as the guardrail. Deploy on Container Apps with cost and latency monitoring.
Tech stack. Azure AI Document Intelligence, Azure AI Speech, Azure OpenAI, Azure AI Search, Azure AI Content Safety, Azure Container Apps, Application Insights, Foundry evaluations.
Problem. Policyholders can't understand their coverage, claims intake is manual and slow, and answers must cite the exact policy clause to be trustworthy.
Plan. Build RAG over policy documents with clause-level citations, an OCR and extraction pipeline for claims, a coverage-checker agent against a rules engine, and anomaly flagging. Add a compliance guardrail and an adjuster dashboard.
Tech stack. Python + FastAPI, OCR (Docling), a vector DB, a rules engine, LangGraph, Pydantic validation, Next.js, Postgres, an eval and tracing stack.
Problem. Analysts spend hours reading filings and reconciling portfolio data, and any tool here must be strictly educational with no advice.
Plan. Build RAG over filings and research plus NL-to-SQL over holdings, a risk and exposure analyzer, and a scenario-simulation agent. Enforce a hard no-advice disclaimer and add alerting and observability. Frame it clearly as research, not advice.
Tech stack. Python + FastAPI, RAG over filings, a SQL agent over a holdings DB, a market-data API, LangGraph, Next.js, Postgres, LangSmith or Phoenix.