AI Engineer Project Bible — From Beginner to Top 1%

01 —

Foundation Projects

Beginner · 2–4 weeks each

Beginner

Intermediate

Advanced

Research

Monetizable

Beginner$$ Monetizable

P-01

Document Q&A Chatbot (RAG over your own PDFs)

Upload any PDF, ask questions, get cited answers. Think "ChatPDF" but you built it and control it. Works for legal docs, contracts, manuals.

Every SMB has documents they can't search. This is a real SaaS product. Law firms, HR teams, and consultants pay for this today.

PythonClaude APILangChainChromaDBStreamlit

$29–99/mo SaaS · Freelance $500–2k 2–3 weeks

Beginner$$ Monetizable

P-02

AI Customer Support Bot with Escalation

A support bot trained on your FAQ, product docs, and past tickets. Knows when to escalate to a human. Tracks what it can't answer.

Every e-commerce store and SaaS product needs this. The escalation logic and "I don't know" tracking is what separates a real product from a toy.

PythonFastAPIClaude APIPineconeSlack/Webhook

$199–499/mo per client · Agency play 2–3 weeks

Beginner

P-03

Meeting Transcription + Action Items Extractor

Paste or upload a meeting transcript. Get structured summary, decisions made, action items with owners, and open questions. Exports to Notion/Slack.

Shows you can handle structured output engineering — one of the most in-demand practical LLM skills. Also genuinely useful to demonstrate to interviewers.

PythonWhisperClaude APIPydanticNotion API

Internal tool / B2B pilot 1–2 weeks

Beginner$$ Monetizable

P-04

Resume + Job Description Match Scorer

Upload a resume and paste a JD. Get a fit score, gap analysis, suggested rewrites, and keywords to add. Batch mode for 50+ JDs at once.

Recruiting and career coaching market. Batch mode + API makes it B2B. Outplacement firms, recruiters, and bootcamps pay for this.

PythonClaude APIPyPDF2FastAPIReact

$9–29/mo consumer · $500/mo B2B bulk 2 weeks

Beginner

P-05

LLM Cost & Latency Benchmarking Dashboard

Run identical prompts across Claude, GPT-4, Gemini, Llama. Measure tokens/sec, cost per 1k tokens, quality scores. Live dashboard with filters.

Signals eval engineering skills. Interviewers love seeing this — it shows you think about production costs, not just "does it work." Publish the benchmarks publicly for SEO.

PythonMultiple APIsPlotly DashSQLiteGitHub Actions

Open source → consulting leads 2 weeks

Beginner$$ Monetizable

P-06

AI Email Responder (Gmail + Tone Control)

Connect to Gmail. Classify incoming emails by urgency and type. Draft responses in your tone. One-click send. Works with your contact history as context.

Real workflow automation for executives and solopreneurs. Tone matching requires context engineering — a core skill to demonstrate.

PythonGmail APIClaude APIOAuth2Flask

$19/mo consumer · White-label B2B 2–3 weeks

Beginner

P-07

Prompt Evaluation Harness (Your Own PromptFoo)

Build a CLI tool that runs a test suite of prompts against an LLM, scores outputs on custom criteria (factual, format, tone), and diffs results across model versions.

This IS the job. Any team running LLMs in prod needs this. Building it from scratch shows you understand why evals matter, not just how to use a library.

PythonTyper CLIClaude APIYAML configsRich

Open source credibility project 2–3 weeks

Beginner$$ Monetizable

P-08

AI SEO Content Brief Generator

Input a keyword. Scrape top 10 SERPs. Analyze structure, headings, word count. Generate a content brief with outline, entities to cover, questions to answer.

Content agencies pay $50–200 per manual brief. Automate that. Real business with clear ROI. Teaches web scraping + LLM synthesis pipeline.

PythonBeautifulSoupSerpAPIClaude APIFastAPI

$49–199/mo · Agency white-label 2 weeks

02 —

Production-Grade Projects

Intermediate · 4–8 weeks each

Intermediate$$ Monetizable

P-09

Multi-Source Research Agent

Agent that takes a research question, searches web + arxiv + PubMed, deduplicates sources, synthesizes findings, and produces a cited report with confidence scores.

Demonstrates the full agent loop — planning, tool use, source evaluation, synthesis. Sellable to consulting firms, law firms, pharma. A working demo gets attention.

PythonClaude APITavily SearchArXiv APILangGraphFastAPI

$99–499/mo per team · Consulting 4–5 weeks

Intermediate$$ Monetizable

P-10

AI Code Review Bot (GitHub Actions Integration)

GitHub Action that triggers on every PR. Reviews code for bugs, security issues, style violations, and complexity. Comments inline. Learns from approvals over time.

Every engineering team wants this. GitHub Actions integration is the key — it fits existing workflow. Sell as a GitHub App. Teaching moment: real prompt injection surface area.

PythonGitHub APIClaude APIGitHub ActionsDocker

$29–199/mo per repo · $0 OSS tier 4–6 weeks

Intermediate$$ Monetizable

P-11

Legal Contract Analyzer & Risk Flagging

Upload any contract. Extract key clauses, flag unusual or risky terms, compare against standard templates, summarize obligations by party. Export redline suggestions.

NDA review alone is a $500–2k/hr lawyer task. Startups and SMBs desperately need affordable contract review. Hybrid search over legal clause embeddings is the technical core.

PythonClaude APILangChainpgvectorFastAPIReact

$199–999/mo · Law firm white-label 5–7 weeks

Intermediate

P-12

RAG Evaluation Pipeline with RAGAS + Custom Metrics

Build a full eval pipeline: generate test Q&A from your corpus, run retrieval, score with RAGAS (faithfulness, context recall, answer relevancy), track regressions in CI/CD.

This is the project that gets you hired as a senior AI engineer. Most teams have RAG. Almost none have evals. Showing this in an interview ends the technical bar.

PythonRAGASLangChainPineconeGitHub ActionsMLflow

Career signal — gets you hired 4–5 weeks

Intermediate$$ Monetizable

P-13

E-commerce Product Description Generator at Scale

Input: product specs CSV. Output: SEO-optimized descriptions, bullet points, A/B variants, translated to 5 languages. Batch 10k products with quality scoring per output.

Shopify merchants, Amazon sellers, manufacturing companies all have this problem. The batch + quality scoring at scale is the differentiator over a simple prompt call.

PythonClaude API (Batch)CeleryRedisFastAPIPostgres

$0.10–0.50 per product · Agency model 4–5 weeks

Intermediate$$ Monetizable

P-14

HR Policy Q&A Bot with Source Citations + Audit Log

Ingests employee handbook, HR policies, benefits docs. Employees ask questions. Bot answers with policy citations. Logs every Q&A for HR audit. Escalates when unsure.

HRIS vendors charge $50k+/year for this. Mid-market companies (200–2000 employees) are underserved. The audit log is what makes this enterprise-ready.

PythonClaude APIWeaviateFastAPIPostgresSlack SDK

$500–5k/mo per company 5–6 weeks

Intermediate

P-15

Structured Data Extraction Pipeline (Invoices, Receipts, Forms)

Upload any document — invoice, receipt, form, medical record. Extract structured JSON with 98%+ accuracy. Handle tables, handwriting, multiple layouts.

Accounts payable automation alone is a billion-dollar market. Vision + structured output is the technical skill. Teaches you multimodal document AI — critical for enterprise AI.

PythonClaude VisionPydanticFastAPIS3Postgres

$0.05–0.20/doc · ERP integration play 4–5 weeks

Intermediate$$ Monetizable

P-16

AI Sales Call Analyzer (Transcription + Coaching)

Upload sales call recordings. Transcribe with speaker diarization. Score against sales framework (MEDDIC, SPIN). Extract objections, next steps, coaching feedback per rep.

Sales coaching software is $50k+/yr enterprise. This competes on price and AI quality. Every B2B sales team needs it. Revenue intelligence is a hot space in 2026.

PythonWhisperClaude APIpyannoteFastAPIReact

$99–499/mo per team 5–7 weeks

Intermediate$$ Monetizable

P-17

Knowledge Base Auto-Updater from Slack/Confluence

Monitor Slack channels and Confluence pages. When something new is discussed or decided, auto-draft knowledge base updates, flag gaps, remove outdated entries.

Documentation rot is universal. This solves it automatically. Teaches real-time pipeline design, webhook processing, and knowledge graph maintenance.

PythonSlack Events APIConfluence APIClaude APICelery

$299–999/mo per workspace 5–6 weeks

Intermediate

P-18

Local LLM Deployment with OpenAI-Compatible API

Deploy Llama 3 or Mistral locally using vLLM. Expose an OpenAI-compatible REST API. Add prompt caching, rate limiting, token counting, and a usage dashboard.

Data-sensitive companies (healthcare, legal, finance) cannot use cloud APIs. Building this proves you can handle on-prem AI deployments — a premium consulting use case.

PythonvLLMFastAPIDockerPrometheusGrafana

Consulting: $5–15k deployment 4–6 weeks

03 —

Advanced System Projects

Advanced · 6–16 weeks each

Advanced$$ Monetizable

P-19

Multi-Agent Workflow Engine (LangGraph / Custom)

Build a general-purpose multi-agent engine. Agents for research, writing, coding, fact-checking, critique. Orchestrator assigns tasks, checks results, retries on failure. Human checkpoints configurable.

This is the core of every AI product company right now. Understanding orchestration patterns, failure modes, and inter-agent communication is what senior AI engineers are paid for.

PythonLangGraphClaude APIRedisPostgresFastAPITemporal

B2B platform $1k–10k/mo · VC-fundable 8–12 weeks

Advanced$$ Monetizable

P-20

Domain-Specific Fine-Tuned Model (Medical / Legal / Finance)

Fine-tune Llama 3 or Mistral 7B on domain-specific data using QLoRA. Build eval harness comparing fine-tuned vs base vs GPT-4. Deploy adapter with A/B testing. Write up the results.

Proves you can go below the API layer. Medical, legal, and finance verticals have specialized vocabulary and reasoning patterns that base models fail at. Domain specialists + AI = rare and expensive.

PythonHugging FaceQLoRAunslothRAGASvLLMW&B

Sell the adapter · Vertical SaaS play 8–10 weeks

Advanced$$ Monetizable

P-21

Real-Time Voice AI Agent (Sub-800ms Latency)

End-to-end voice agent: Whisper STT → LLM reasoning → ElevenLabs TTS. Sub-800ms perceived latency via streaming. Add persona, memory of past calls, tool access.

Voice AI is the next UI wave. Phone agents for appointment booking, customer service, and surveys are replacing traditional IVR. Latency is the hard technical problem.

PythonWhisperClaude APIElevenLabsPipecatWebSocketsFastAPI

$0.10/min · $500–2k/mo per client 8–12 weeks

Advanced

P-22

Hallucination Detection & Guardrails System

Build a system that detects hallucinations in LLM output by cross-referencing source documents, runs factual consistency checks, and applies configurable guardrails before output is shown to users.

AI safety/reliability is the #1 blocker for enterprise adoption. Building a working hallucination detector is a research-adjacent skill that signals serious engineering depth.

PythonNLI modelsClaude APIBLEURTPrometheusFastAPI

Enterprise middleware · Safety layer SaaS 8–10 weeks

Advanced$$ Monetizable

P-23

Autonomous Coding Agent (GitHub Issue → PR)

Agent reads GitHub issues, writes code, runs tests, fixes failures, opens a PR with explanation. Handles simple bugs and feature additions. Human reviews the PR.

This is literally what GitHub Copilot Workspace, Devin, and Claude Code are. Building your own version teaches you agent architecture at its hardest — real code execution, error recovery.

PythonClaude APIGitHub APIDocker (sandboxed exec)LangGraphpytest

OSS → consulting · $1–5k/mo per team 10–14 weeks

Advanced$$ Monetizable

P-24

AI-Powered BI Dashboard (Natural Language → SQL → Chart)

Connect to any Postgres/MySQL DB. Ask "Show me revenue by region last quarter." Get SQL, chart, and plain-English explanation. Learns your schema over time. Flags suspicious queries.

Text-to-SQL is one of the hottest LLM application areas. Every BI tool is adding this. Building your own proves you understand the schema injection, disambiguation, and SQL safety problems.

PythonClaude APISQLAlchemyPlotlyFastAPIReactpgvector

$299–2k/mo per company 8–12 weeks

Advanced$$ Monetizable

P-25

Personalized AI Tutor with Adaptive Learning

Tutoring system that builds a knowledge model of the student, identifies gaps, generates targeted questions, adjusts difficulty, tracks mastery over time. For any subject domain.

EdTech is a massive market. Adaptive learning systems (like Khanmigo) are extremely hard to build well. The memory architecture — tracking per-concept mastery — is the technical challenge here.

PythonClaude APIPostgresKnowledge graphFastAPIReact

$29/mo consumer · $5k/mo school license 10–14 weeks

Advanced

P-26

Production LLMOps Platform (Monitoring + Tracing)

Self-hosted LLMOps dashboard: log every LLM call with latency, cost, prompt, output. Trace multi-step chains. Alert on regressions. Compare prompts A/B. Replay failed calls.

Langfuse and Helicone exist but building this yourself shows you understand the whole observability stack. Companies with internal models need self-hosted solutions for data privacy.

PythonOpenTelemetryClickHouseFastAPIReactDocker

Open source → enterprise support deals 10–14 weeks

Advanced$$ Monetizable

P-27

Synthetic Training Data Generator with Quality Filtering

Given a task and a few examples, generate thousands of high-quality training examples. Auto-filter with LLM-as-judge. Output in any fine-tuning format. Dedup and diversity checks.

Data is the bottleneck for every fine-tuning project. A pipeline that generates + validates synthetic data is directly monetizable to any team doing model training.

PythonClaude APISentence-transformersPandasFastAPIMinIO

$0.001/example at scale · MLOps tool 6–8 weeks

Advanced$$ Monetizable

P-28

AI Due Diligence Tool for Investors (Company Research Agent)

Input a company name. Agent pulls Crunchbase, LinkedIn, news, filings, reviews, patents, GitHub activity. Produces structured DD report: team, market, competition, risks, red flags.

VC analysts spend 40+ hours on early DD. This compresses it to 2 hours of validation. VCs, PE firms, and M&A teams pay $5k–50k for comprehensive DD reports.

PythonLangGraphClaude APICrunchbase APISerpAPIPostgres

$500–5k per report · $2k/mo subscription 10–14 weeks

Advanced$$ Monetizable

P-29

Multimodal Product Catalog AI (Vision + Search + Rec)

Upload product images. Auto-generate titles, descriptions, tags using vision. Build semantic search over visual + text embeddings. Add "similar products" and "complete the look" recommendations.

E-commerce companies with 10k+ SKUs have a catalog management nightmare. Vision + multimodal search is a concrete, deployable solution with clear ROI.

PythonClaude VisionWeaviateCLIP embeddingsFastAPIReact

$0.01/product + $999/mo SaaS 8–10 weeks

Advanced

P-30

GraphRAG: Knowledge Graph-Augmented Retrieval System

Build a retrieval system where entities and relationships are extracted into a knowledge graph (Neo4j). Queries traverse the graph + vector search. Compare quality vs naive RAG with eval suite.

GraphRAG is Microsoft's published approach and represents the frontier of production RAG. Building and benchmarking it shows you're tracking the state-of-the-art, not just using last year's patterns.

PythonNeo4jLangChainClaude APIPineconeRAGAS

Research portfolio + consulting 8–10 weeks

04 —

Research Reimplementations

Research · Proof of Deep Engineering

Each of these involves reading the original paper, implementing the core contribution, building something usable on top of it, and writing a blog post explaining what you learned. This combination — paper + implementation + product + writeup — is the research portfolio signal that opens doors to AI labs and frontier teams.

RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

Sarthi et al., Stanford · 2024 · arxiv:2401.18059

Standard RAG chunks documents linearly. RAPTOR clusters + recursively summarizes chunks into a tree — enabling queries at multiple abstraction levels. Dramatically outperforms naive RAG on multi-hop questions.

Build on top: Implement RAPTOR indexing over a large document corpus (legal cases, academic papers, product manuals). Compare retrieval quality with standard RAG using RAGAS. Add a UI showing the tree structure. Write a blog post with benchmarks.

PythonUMAPGMM clusteringClaude APIChromaDBRAGAS

Signal: RAG depth + research implementation

Self-RAG: Learning to Retrieve, Generate, and Critique

Asai et al., UW · 2023 · arxiv:2310.11511

Instead of always retrieving, Self-RAG trains the model to decide when to retrieve and to critique its own outputs using special reflection tokens. Outperforms standard RAG by large margins on factual tasks.

Build on top: Replicate the inference loop (retrieval decision + critique tokens) using prompt engineering with a strong base model. Apply to a medical QA task. Measure hallucination rate vs standard RAG. Ship as a demo.

PythonClaude APIPineconeBioASQ datasetRAGAS

Signal: Agentic RAG + self-critique patterns

HyDE: Hypothetical Document Embeddings for Dense Retrieval

Gao et al., CMU · 2022 · arxiv:2212.10496

Instead of embedding the query directly, generate a hypothetical answer first, embed that, and use it for retrieval. Bridges the gap between query and document embedding spaces — especially for sparse queries.

Build on top: Implement HyDE retrieval and compare vs standard embedding retrieval on 3 datasets. Add it as a toggle in a RAG API so users can A/B test. Benchmark latency tradeoff vs quality gain.

PythonClaude APISentenceTransformersFAISSBEIR benchmark

Signal: Retrieval engineering depth

ReAct: Synergizing Reasoning and Acting in Language Models

Yao et al., Princeton · 2022 · arxiv:2210.03629

ReAct alternates between reasoning traces ("Thought:") and external actions ("Action:") in a single prompt loop. It's the foundational paper behind most modern agent frameworks including LangChain's agent executor.

Build on top: Implement ReAct from scratch (no LangChain). Apply to a real tool-using task (web search + calculator + code execution). Compare against Chain-of-Thought without actions. Deploy as a demo showing the trace.

PythonClaude APITavilyRaw promptingFastAPI

Signal: Agent architecture from first principles

Constitutional AI: Harmlessness from AI Feedback

Anthropic · 2022 · arxiv:2212.08073

CAI trains models to be helpful and harmless using a "constitution" of principles. The model critiques and revises its own outputs against these principles — removing the need for human labelers to flag harmful content.

Build on top: Implement a mini-CAI pipeline: write a constitution, have a model generate responses, critique against the constitution, revise. Apply to a content moderation task for a real domain. Ship an API.

PythonClaude APICustom constitution YAMLFastAPIEval suite

Signal: AI safety + RLHF alignment concepts

LoRA: Low-Rank Adaptation of Large Language Models

Hu et al., Microsoft · 2021 · arxiv:2106.09685

LoRA freezes pretrained model weights and injects trainable rank decomposition matrices — reducing fine-tuning parameters by 10,000x with no inference latency penalty. This paper is how almost all fine-tuning is done today.

Build on top: Fine-tune a 7B model on a domain-specific dataset using LoRA (Hugging Face PEFT). Write a blog post explaining the math (rank decomposition, why it works). Benchmark: base vs LoRA vs full fine-tune on your eval set.

PythonPEFTTransformersW&BA100 / ColabRAGAS

Signal: Model training depth — rare and valued

Attention Is All You Need (Transformer from scratch)

Vaswani et al., Google · 2017 · arxiv:1706.03762

The paper that created modern AI. Multi-head self-attention, positional encoding, encoder-decoder architecture. Every LLM you use is built on this. Reading and implementing this is a rite of passage.

Build on top: Implement a small transformer in PyTorch from scratch (following Karpathy's nanoGPT approach). Train on a small text dataset. Write an annotated explanation of every component. Publish to GitHub with a demo.

PythonPyTorchNumPyMatplotlibGPU (Colab)

Signal: Architecture fundamentals — research credibility

RAGAS: Automated Evaluation of Retrieval Augmented Generation

Es et al. · 2023 · arxiv:2309.15217

RAGAS defines four key metrics for RAG evaluation: faithfulness, answer relevancy, context precision, context recall. It uses LLMs to evaluate LLMs — removing the need for human annotations in eval pipelines.

Build on top: Implement the four RAGAS metrics from scratch (without the library) to prove you understand them. Apply to your own RAG project. Then extend with a 5th custom metric for your domain. Write up the methodology.

PythonClaude APICustom metricsPandasPlotly

Signal: Evals expertise — the most valued AI skill

Chain-of-Thought Prompting Elicits Reasoning in LLMs

Wei et al., Google · 2022 · arxiv:2201.11903

Shows that providing step-by-step reasoning examples in prompts dramatically improves LLM performance on complex reasoning tasks. Foundation for everything from zero-shot CoT to tree-of-thought approaches.

Build on top: Implement and benchmark zero-shot CoT, few-shot CoT, and Tree-of-Thoughts on a hard reasoning task (GSM8K math, LSAT). Build a prompt engineering playground where users can see reasoning quality change with each technique.

PythonClaude APIGSM8K datasetStreamlitPandas

Signal: Prompt engineering at research depth

Toolformer: Language Models Can Teach Themselves to Use Tools

Schick et al., Meta · 2023 · arxiv:2302.04761

Toolformer teaches LLMs to decide when and how to call APIs (calculator, search, calendar) by self-supervising on when tool calls improve prediction. It's the conceptual ancestor of all function-calling APIs.

Build on top: Implement the Toolformer data generation pipeline (self-supervised tool annotation). Apply to 3 tools. Compare vs standard function-calling in accuracy and tool selection precision. Publish the annotated dataset.

PythonHugging FaceCustom toolsPandasClaude API

Signal: Tool use + agent foundations

Mixture of Experts (MoE): From Dense to Sparse Models

Shazeer et al., Google · 2017 + Mixtral 2024 · arxiv:2401.04088

MoE routes each token to only a subset of "expert" feed-forward networks, enabling massive parameter counts with constant compute per token. Mixtral, DeepSeek, and GPT-4 all use this architecture.

Build on top: Implement a small MoE transformer layer in PyTorch. Train a tiny MoE language model and compare perplexity and compute vs dense equivalent. Visualize which experts activate for different token types.

PythonPyTorchMatplotlibWeights & BiasesGPU

Signal: Architecture research — frontier-level understanding

Sparse Priming Representations (SPR) for LLM Memory Compression

David Shapiro · 2023 · GitHub: daveshap/SparsePrimingRepresentations

SPR compresses large amounts of information into dense, semantically rich bullet points that efficiently prime LLMs. Effectively a lossy compression format designed specifically for LLM context windows.

Build on top: Build a long-term memory system for AI agents using SPR compression. Compress past conversations into SPR format, store in a vector DB, retrieve and reconstruct for new sessions. Measure token savings vs retrieval quality.

PythonClaude APIPineconeCustom compressionFastAPI

Signal: Memory architecture + context engineering depth

05 —

Pick Your Industry Vertical

Given you have prior industry experience — this is your biggest unfair advantage. An AI engineer who actually understands the domain problem is worth 3x a generalist. Pick one vertical and go deep.

Vertical$$ High Value

V-01

Automotive: AI Parts Catalog + Fitment Intelligence

Natural language search over parts catalogs. "What fits a 2019 Toyota Camry 2.5L?" Returns compatible parts with confidence, supplier options, and install instructions. Cross-reference multiple catalogs.

Given your OSS Motors / OSS Car Care background — you have the domain knowledge. Auto parts e-commerce is $70B. Fitment is the hardest search problem in the space.

PythonClaude APIpgvectorACES/PIES dataFastAPIReact

$500–5k/mo per dealer · Your domain edge 6–8 weeks

Vertical$$ High Value

V-02

E-Commerce: AI Amazon Listing Optimizer + PPC Analyzer

Input ASIN or product. Scrape current listing, competitor listings, search terms. Generate optimized title, bullets, A+ content. Analyze PPC bids vs search volume. Track listing score over time.

OSS Car Care gives you seller experience. Amazon seller tools market is $1B+. Sellers pay $200–2k/mo for optimization tools. You know what actually matters.

PythonClaude APIJungle Scout APISP-APIFastAPIReact

$99–499/mo per seller account 6–8 weeks

Vertical$$ High Value

V-03

Enigmax HiggsField MVP: AI Research OS

Vertical AI OS for researchers and knowledge workers. Ingest papers, notes, books. Ask cross-document questions. Auto-build knowledge graph of concepts and relationships. Track open questions. Export to reports.

This IS your Enigmax thesis. Build it as a real product, not a demo. The first version with 10 paying researchers will teach you more than 6 months of planning. Start here.

PythonClaude APINeo4jWeaviateFastAPIReactRAGAS

$49–199/mo · Your actual startup 10–16 weeks

Vertical$$ High Value

V-04

Manufacturing: QA Defect Detection + Root Cause AI

Vision model flags defects in product images. LLM analyzes defect patterns against production parameters to suggest root causes. Tracks defect rate trends. Generates shift reports automatically.

Manufacturing quality AI is massively underserved outside Tier 1 auto. Vision + structured reporting is practical with current models. A single 10% defect reduction for a mid-size plant is worth millions.

PythonClaude VisionYOLOv8FastAPIPostgresGrafana

$2k–10k/mo per production line 8–10 weeks

Build thingscompaniesalready need.

Foundation Projects

Production-Grade Projects

Advanced System Projects

Research Reimplementations

Pick Your Industry Vertical

Build things
companies
already need.