# Abu · One-Person Studio | Commercial AI Production & Financial RAG Systems > Helping business leaders replace repetitive manual analyst roles with private AI engines, saving $60K-$90K/yr. $2K Audit Package, 2-3 week Turnkey Delivery, and Monthly Retainers. No free POCs. ## 个人简介 - 姓名: Abu - 状态: 2026 Q4 · 2 Delivery & Audit Slots Open - 标语: Founder, One-Person Company · AI Systems Architect - 定位: Turning AI into Real Production Power — Cutting Heavy Labor for Founders - 介绍: I'm Abu (One-Person Engineering Studio) . I help teams crack high-precision financial RAG, complex multi-column document parsing, and production Agent reconciliation . Observable, private, and ROI-driven. - 技术关键词: Financial & Legal RAG, PDF/OCR Coordinate Inversion, Agent State Machine & Reconciliation, FTE Labor Replacement, 50-Doc Benchmark Verification - 数据: $75K+ Annual Labor Saved / Case · 80+ Internal Repos · 2-3 Wks Turnkey Docker Shipping · 50-Doc Benchmark Audit ## Production Cases Engineering breakthroughs that unlock clear commercial FTE replacement ledgers ### Massive Research Reports, yet Knowledge Base Hallucinates - 副标题: Financial Document RAG · Authority Rerank · $75K/Yr Labor Saved - 描述: Mid-sized research team: 4 junior analysts spent days manually segmenting reports with an 8% error rate. Our private Rerank pipeline reduced manual review to 1 person with 99% accuracy, saving $55K+ net annually. - 标签: RAG, Chunking, Rerank, Celery - 亮点: - Custom dual-column PDF segmentation eliminating layout breakage & table shifts - Time-weighted + institutional authority Rerank gateway pushing recall to 91.4% - Private Docker Compose turnkey image, reducing P95 latency to <320ms ### Scanned PDFs & Wild Layouts Dragging RAG into a Quagmire - 副标题: Coordinate Inversion · Smart De-Watermarking · Zero OCR Drift - 描述: The lethal flaw in enterprise AI: 90% of RAG failures stem from missing layout coordinates in pre-ingestion, not weak LLMs. Coordinate inversion fixes tables without expensive LLM token traps. - 标签: PDF, OCR, Layout, Structured - 亮点: - Raw coordinate extraction + multi-level fallback table reconstruction to Markdown - Rule-based image de-watermarking middleware elevating downstream vector clarity - 50-doc benchmark dataset sealed for objective 95% pass rate sign-off ### Scaling Community Transactions Beyond Manual Human Capacity - 副标题: Commercial Bot · Escrow Reconciliation · 24/7 Risk Control - 描述: High-volume commercial groups and cross-border businesses leak cash when audited manually. We ship state-machine accounting, escrow automation, and anti-fraud tripwires. - 标签: Bot, Risk, Escrow, Redis - 亮点: - FastAPI + Redis + Celery distributed async ticket & group reconciliation engine - Automated fund escrow, real-time balance anomaly triggers & audit trails - Zero human reconciliation errors across high-volume active communities ## Business flows Locate capability by where you are stuck - not by a wall of tech cards ### Knowledge base, 0 to production Full RAG chain - from dirty docs to a shippable Q&A gateway - 读者画像: If you are a tech/business lead: docs piled up, answers are wrong, will not ship - 一句话: Do not swap for a bigger model first - fix parsing, chunking, rerank, and the gateway #### A1 · Doc intake · Scans, tables, watermarks, missing TOC - dirty docs feed dirty knowledge The most common blocker is upstream: PDF structure lost, tables garbled, watermarks over text, TOC missing. No downstream tweak can save this. ### High-Precision PDF Structuring Engine - 怎么解: Reverse-engineers PDF coordinates via low-level libs to extract text/tables/hierarchy as the cleanest data source - 标签: PDF, FastAPI ### Smart PDF Watermark Removal - 怎么解: Configurable rule base + visual coordinate matching for batch watermark removal, raising the OCR baseline - 标签: Watermark, Batch ### Multimodal OCR Visual Service - 怎么解: OCR + layout understanding wrapped as a high-concurrency API - upload a scan and get structured output - 标签: OCR, VL ### Deep Report TOC Extraction - 怎么解: Bookmark extraction -> TOC page parsing -> layout understanding, a three-tier fallback - 标签: PDF, TOC ### Ultra-Fast GPU Layout Detection - 怎么解: GPU inference cluster acceleration with high-concurrency async ingest, dismantling charts/tables/paragraphs - 标签: Layout, GPU #### A2 · Semantic chunking · Naive chunking shreds tables and severs context - recall ceiling locked Chunking is the hidden ceiling of RAG precision. Hard character splits tear tables and cut context; even perfect recall cannot reassemble full meaning. ### Report Smart Semantic Chunking - 怎么解: Layout-aware chunking for financial reports, recognizing table shapes and emitting clean Markdown - 标签: Chunking, Tables ### Adaptive Multi-Format Chunking - 怎么解: One chunking pipeline for all source formats, streaming into a GPU production ingest path - 标签: Chunk, Vectorize #### A3 · Vector recall · Keyword-only hits miss semantics; chart data is invisible to search Default BM25 cannot match paraphrases, and chart content is a blind spot. Charts and text need dual-track vectorization to be retrievable. ### Qwen3 Vector Retrieval Platform - 怎么解: Qwen3 Embedding + PGVector recall microservice, tuned for vertical-industry semantic hit rates - 标签: Qwen3, PostgreSQL ### Multimodal Report Retrieval - 怎么解: Dual-track vector storage for inline charts and text in PDFs, answering chart-data and text-evidence queries - 标签: Vector, Research #### A4 · Rerank & citation · Dumping top-k into the LLM cites the wrong passage and answers miss the point Recalled top-k mixes stale and weakly-relevant snippets that mislead the LLM. You need freshness/authority rerank plus eval logs. ### Retrieval Reranker - 怎么解: Semantic rerank and dedup over recall candidates, fusing freshness and source authority for better citations - 标签: Rerank, RAG #### A5 · Async ingest infra · Manual ingest cannot scale and cannot roll back At volume, manual ingest is slow and lossy. You need an async ingest cluster with scalable deployment for throughput and handoff/rollback. ### Massive Long-Doc Async Ingestion - 怎么解: Celery + Docker async distributed document system for automated report ingest and vectorization orchestration - 标签: Celery, Docker #### A6 · Conversational Q&A gateway · No auth, audit, or logging - cannot go to production Internal demos can run raw, but production needs auth, call logs, and audit. The gateway must wrap retrieval and generation behind a standard OpenAPI. ### Enterprise Conversational Q&A Gateway - 怎么解: Standard conversational RAG OpenAPI wrapping retrieval and generation with auth and call-log auditing - 标签: OpenAPI, Chat ### Agent, demo to production Agent engineering - tool orchestration / code exec / multi-agent - 读者画像: If you are a CTO/tech lead: agent demos look great but break on new scenes, will not ship - 一句话: Embed tool orchestration, code execution, and multi-agent into an auditable engineering flow #### B1 · Dynamic tools · Hard-coded tools break on new scenes; static toolchains cannot generalize Tool calls baked into code mean every new tool needs a logic change. LLMs need to write code in a sandbox and mount tools on demand. ### Sandboxed Code Execution & Tool Proxy - 怎么解: Empowers LLMs to dynamically write and execute code in isolated sandboxes and mount tools on demand - 标签: MCP, Code Act #### B2 · Multi-tool orchestration · Multi-tool coordination via hand-written if-else is not reusable Chaining tools with spaghetti if-else means rewriting for every new intent. You need natural-language-driven call-chain planning. ### MCP Service Orchestration Engine - 怎么解: Plans multi-tool call chains from natural language, with context-aware intent decomposition and concurrent execution - 标签: JSON-RPC, Orchestration #### B3 · Capability aggregation · Every system calls the LLM directly - AI API assets are out of control Each business system connects to the LLM on its own; calls are scattered, costs run out of control, governance is impossible. A middle layer must aggregate AI API assets. ### Enterprise Model Capability Gateway - 怎么解: Unified AI tool scheduling middleware shielding business systems from direct LLM complexity, with centralized control and routing - 标签: MCP, API #### B4 · Multi-agent collaboration · A single agent cannot hold complex tasks - cannot split, routing is messy A single agent drifts as context grows. Complex tasks need multi-role collaboration: autonomous decomposition, multi-round routing, automated execution loops. ### Complex Multi-Agent Collaboration - 怎么解: Multi-role agent network for autonomous task decomposition, multi-round routing, and automated execution loops - 标签: Multi-Agent #### B5 · Edge execution · No controllable execution environment - mobile/edge scenes cannot plug in Much business happens on mobile/edge, but agents have no controllable environment to plug into. You need cloud-phone fleet control plus standard APIs. ### Phone AutoGLM Fleet Platform - 怎么解: AutoGLM-based cloud-phone fleet: web console + REST APIs for command dispatch, progress polling, and media import - 标签: AutoGLM, Fleet ### Finance research content From attachment collection to intraday anomalies - automate the research pipeline - 读者画像: If you are a finance/news team: too many reports, labor is expensive, workflows are broken - 一句话: From attachment collection to intraday anomalies - automate the research pipeline #### C1 · Multi-source collection · Report attachments scattered across inboxes - manual moving is slow and lossy Reports pile up in inboxes daily; manual download, rename, and ingest is slow and leaky. You need RPA auto-polling plus email intel routing. ### Report Attachment RPA Crawler - 怎么解: Auto-polls inboxes and extracts key data tables from attached reports into standard structured ledgers - 标签: RPA, PDF ### Email Intel Automated Routing - 怎么解: Real-time monitoring of core inboxes, extracting high-value email via custom rules and routing across platforms - 标签: IMAP, Bot #### C2 · Entity & semantic extraction · Entity/time extraction is inaccurate; business semantics are weak Regex extraction of company names, dates, and metrics from reports misses and errs; downstream aggregation cannot get accurate. You need custom NER plus auto-tagging. ### High-Precision Financial NER - 怎么解: Custom financial entity extraction with knowledge-graph recall and earnings-cycle logic parsing for stronger semantic understanding - 标签: NER, FastAPI ### Financial News Smart Tagging - 怎么解: NLP models auto-tag and classify massive announcements and news for downstream aggregation and push - 标签: NLP, Classification #### C3 · Intraday anomaly monitoring · Nobody watches intraday anomalies - risk lags, opportunities slip away Manual monitoring of intraday market and news anomalies is unrealistic; by the time you see it, it is too late. You need millisecond listening plus multi-dimensional quant scoring plus real-time alerts. ### Intraday ETF Quant Monitoring - 怎么解: Millisecond market and news anomaly engine, auto-evaluating risk across multi-dimensional quant indicators for real-time alerts - 标签: ETF, Quant ### High-Freq Asset Trading Monitor - 怎么解: Low-latency trade-data listeners plus Redis high-speed dedup, pushing core trading intel in milliseconds - 标签: Web3, IM #### C4 · Question parsing engine · Evaluation and generation are tangled - parsing is not rigorous In education scenarios, evaluating and generating the same question lets the LLM self-endorse. You need to separate evaluation from generation and support multi-round verification. ### Multi-LLM Education Parsing Engine - 怎么解: Complex question-type routing network, separating evaluation and generation, with multi-round auto-verification for rigor - 标签: Multi-LLM, Engine ### Community monetization & office automation Hand moderation, leads, and collaboration to bots and automation - 读者画像: If you run a community/private-domain: group scale outgrew manual ops - 一句话: Hand moderation, leads, and collaboration to bots and automation #### D1 · Moderation, risk & escrow · Verification, anti-spam, escrow, and support tickets cannot stay manual After growth, join verification, anti-spam, escrow trades, and support tickets cannot stay manual. Focus: stability, reconciliation loop, and operational control. ### Community Ops & Monetization Hub - 怎么解: All-in-one community ops solution covering anti-fraud risk control, escrow trading, and smart customer service as a monitorable loop - 标签: Bot, Escrow #### D2 · Lead insight · Opportunities in chat slip through the flood of messages Tens of thousands of messages a day are impossible to scan manually; complementary opportunities are lost. You need 24/7 listening plus LLM intent extraction plus targeted delivery. ### Community Lead Insight Radar - 怎么解: 24/7 full-volume monitoring of community dynamics, LLM-extracted business intents, capturing complementary opportunities in real time - 标签: Bot, LLM #### D3 · History search · Chat history is a black box - valuable info cannot be found Group history is a huge information mine, but full-text search cannot find semantically similar content. You need full-text plus vector dual-track recall. ### Community History Smart Search - 怎么解: Dual-track retrieval fusing full-text search and vector similarity, mining valuable info from massive chat histories - 标签: IM, Hybrid search #### D4 · Office sync & desktop · Feishu/email/desktop data is siloed - manual reconciliation is costly Feishu sheets and backend systems are disconnected, requiring manual order-status sync; desktop work is repetitive. You need bidirectional data pipelines plus desktop AI enhancement. ### Enterprise Data Synergy Pipeline - 怎么解: Bridges Lark multidimensional tables and backend systems for bidirectional real-time sync of orders and client info, eliminating manual reconciliation - 标签: Lark, Automation ### AI-Enhanced Smart Clipboard - 怎么解: Rust-based desktop tool that listens and reshapes clipboard content with LLMs in real time, bridging workflows - 标签: Tauri, Rust ## Track record Production delivery paths — not a demo gallery ### 2024 — Present — Enterprise AI architecture and delivery Led agents, complex RAG, and multimodal apps from POC to HA production — with eval, observability, rollback, and handoff, not just demos. 标签: Agents, RAG, FastAPI, Observability ### 2022 — 2024 — Document intelligence and unstructured data Built million-scale finance document pipelines — complex layouts, OCR, chunking — so retrieval rests on correct parsing. 标签: PDF, OCR, ETL ### 2020 — Present — High-concurrency IM bots and backend Designed commercial community bots and ops hubs; async frameworks and queues protect trading and support stability. 标签: Bot, Feishu API, Redis ### Ongoing — MCP and Agent engineering Embed tool orchestration, code-act, and Text2SQL into auditable, reproducible flows — Agents that ship, not just demo. 标签: MCP, Agent, Text2SQL ## Core capabilities Not just model wiring — shipping AI that belongs in production : observable, rollback-ready, maintainable. ### AI application engineering Full RAG path / Agents in prod / LLM services / Multi-Agent / MCP toolchains 标签: RAG, Agent, MCP, Eval ### Domain text and knowledge bases Report/filing chunking and vectors, recall rerank, chart recall, tagging, Q&A gateways 标签: Vectors, Rerank, Reports ### Document intelligence PDF parsing / OCR-VL / watermark cleanup / tables / TOC / structured extract 标签: PDF, OCR, Pipeline ### Platform integration and automation IM bots / Feishu / email RPA / Celery scheduling and reconciliation loops 标签: Bot, Feishu, Celery ## Ways to work together ### 15-Min Problem Triage & Fit Check [Free · Time-boxed] Align on problem scope and mutual fit. Bring your raw scenario. Direct guidance on whether an audit or build makes sense. 技术: 1-on-1 Call, Feasibility ### Production Readiness Audit Package [$2,000 · High Intent] Bring your dirtiest 30 documents for a closed-door benchmark test. Receive an architecture sketch, risk radar, and prioritized backlog. 100% credited toward full delivery. 技术: 3-5 Days, Benchmark, 100% Credited ### Turnkey Engine Delivery [$18K~$22K · Recommended] Abu implements the hardest pre-ingestion & rerank pipelines, shipping a private Docker image. Replaces 3-4 analyst roles with >200% first-year ROI. 技术: 2-3 Wk Agile, Private Docker, FTE Replacement ### Fractional AI Advisory Retainer [$2.5K~$3K/Mo · 2 Slots] Act as your external fractional CTO. Weekly architecture reviews, design sign-offs, 2-hour emergency escalation, and strict anti-scope-creep defense. 技术: Monthly Advisory, Escalation, Code Review ### Modular Pipeline Delivery [Priced Modules] Fixed-scope modules: PDF coordinate parsing pipeline, temporal authority Rerank, or Bot reconciliation. Sealed 50-doc benchmark acceptance. 技术: Parse Pipeline, Rerank Gateway, Benchmark Sign-off ## Technical Philosophy AI value is production — observable, rollback-ready, maintainable. I do not sell concepts; I deliver systems that actually run in the business. ## 机器可读数据源 - 站点: https://briskygates.github.io - 中文配置 JSON: https://briskygates.github.io/assets/data/homeConfig.json - 英文配置 JSON: https://briskygates.github.io/assets/data/homeConfig.en.json - llms.txt: https://briskygates.github.io/llms.txt - 语言: en