[ SOLUTIONS ]

Integrated Pipelines, Delivered

Not just deploying individual tools — we integrate multiple open-source projects into production-grade pipelines that solve real business problems.

PIPELINEAI Knowledge Pipeline3 core components

FULL-MEDIAFull-Media Knowledge Base3 core components

CX-HUBAI Customer Support Hub3 core components

WORK-ASSISTInternal Support Assistant3 core components

RESEARCHResearch & Intelligence Assistant3 core components

EVAL-LOOPLLM Quality Feedback Loop3 core components

REDTEAMAI Security Red Team3 core components

FLOW-OPSMulti-Agent Workflow Automation3 core components

FINTECHPrivate Financial Data Hub3 core components

[ PIPELINE ]

AI Knowledge Pipeline

From Information Noise to Structured Knowledge

An enterprise-grade knowledge management pipeline built on open-source tools, covering the full lifecycle from trend discovery to knowledge retrieval.

TrendRadarTrend Discovery

47.9k

RAGFlowSmart Parsing

25.3k

LangfuseQuality Monitoring

21.6k

Trend Discovery

TrendRadar aggregates trends from Twitter, HN, RSS — AI-powered filtering and keyword matching, auto-push noteworthy topics.

Content Collection

Multi-source crawling (web articles + video transcription), anti-bot measures, format normalization, incremental collection.

Smart Parsing

RAGFlow deep document understanding (PDF/Word/PPT/OCR), semantic chunking + vectorized storage, multi-knowledge-base isolation.

Knowledge Retrieval

Semantic search + keyword hybrid recall, ask in natural language to find relevant knowledge fragments, with category browsing.

Quality Monitoring

Langfuse full-stack observability, tracking retrieval relevance, answer accuracy, token usage — continuous RAG optimization.

End Info Overload

AI filters automatically, see what matters

Semantic Search

Not keyword matching — intent understanding

Full Data Ownership

Open-source self-hosted, data stays private

Measurable Quality

Full-stack monitoring, continuous improvement

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[ FULL-MEDIA ]

Full-Media Knowledge Base

Text · Images · Audio · Video — Unified Understanding & Retrieval

Unify text documents, images, audio and video into a single semantically searchable knowledge base. Each media type is processed by the best-fit open-source component, then fed into a unified vector retrieval layer.

RAGFlowDocument Parsing + Unified Search

25.3k

QwenVLM Image Understanding

vLLMModel Inference Engine

52.8k

Text Processing

RAGFlow deep document understanding: PDF / Word / PPT / Excel / Markdown / HTML. DeepDOC layout recognition + semantic chunking + vectorized storage.

Image Understanding

Qwen-VL / CLIP vision-language models auto-generate structured descriptions, tags and classifications. Batch scanning, incremental processing, multi-backend switching.

Audio Transcription

OpenAI Whisper (local GPU) or Qwen3-Omni (cloud API). Multi-language auto-detection, outputs timestamped SRT subtitles + plain text.

Video Analysis

FFmpeg keyframe extraction + ASR transcription dual-channel. Keyframes analyzed by VLM, audio transcribed to text. Both merged into knowledge base.

Unified Retrieval

All media types flow into RAGFlow vector index. Cross-media semantic search — text queries can find video and image content.

Searchable Video

Video-to-text + keyframe analysis, never lose what you watched

Image Understanding

VLM auto-identifies image content, search by description and tags

Cross-Media Search

One query covers all media types, break information silos

Fully Offline

Whisper + Ollama + RAGFlow — entire stack runs locally

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[ CX-HUB ]

AI Customer Support Hub

Multi-channel intake, knowledge-driven replies, controlled boundaries, smooth human handoff

Unify website chat, WhatsApp, Telegram, enterprise messaging and helpdesk entry points into one support control layer. AI handles repetitive questions first, while complex conversations escalate to humans with full context attached.

OpenClawMulti-channel AI Gateway

RAGFlowKnowledge Retrieval & Citations

25.3k

LangfuseConversation QA & Optimization

Channel Integration

Connect website chat, WhatsApp, Telegram, Slack and enterprise messaging into one unified conversation layer.

Knowledge Loading

Import FAQs, pricing boundaries, policies and after-sales playbooks into a retrievable support knowledge base.

Boundary Control

Encode what the assistant can answer, what it must avoid, and when escalation is mandatory.

Human Handoff

Transfer high-risk or high-value conversations to human agents together with context, tags and suggested replies.

Optimization Loop

Track resolution rate, handoff rate, satisfaction and failure cases so real conversations keep improving the system.

One Support Surface

Multiple channels, one control plane, fewer missed conversations

More Consistent Replies

Pricing and policy boundaries are enforced upstream

Faster Agent Onboarding

AI answers first, humans step in only when needed

Real Conversations Become Assets

Failures turn directly into the next optimization cycle

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[ WORK-ASSIST ]

Internal Support Assistant

Knowledge Q&A plus workflow triggering for IT, HR, Finance and Legal teams

Not just answering policy questions. The assistant can also check procedures, trigger requests, generate forms and push reminders, turning internal support into a result-oriented workflow.

AnythingLLMWorkspace Isolation & Q&A

n8nWorkflow & System Automation

LangfuseObservability & Improvement

Knowledge Ingestion

Load policies, SOPs, onboarding material, templates and historical FAQs into department-level workspaces.

Access Isolation

Apply role-based permissions so each employee sees only the data and processes they are allowed to access.

Self-Service Q&A

Employees ask in natural language and get source-grounded answers with next-step guidance attached.

Workflow Triggering

Connect approvals, tickets, calendars, messaging and email so the assistant can move from answering to acting.

Usage Review

Analyze frequent questions, unresolved issues and bottlenecks to keep improving both docs and automation.

Faster Onboarding

New hires can resolve repetitive questions without chasing people

Lower Support Load

HR, IT and Finance spend less time answering the same requests

Clear Permission Boundaries

One assistant can serve many teams without overexposure

From Answers to Actions

The system does not stop at explanation; it triggers follow-up tasks

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[ RESEARCH ]

Research & Intelligence Assistant

Continuous monitoring for competitors, policy updates, sentiment and industry signals

Capture signals from public information streams, summarize them, archive them, and push the useful parts to the team. Less doomscrolling, more decisions.

TrendRadarMulti-source Signal Discovery

47.9k

RAGFlowArchival Search & Retrieval

25.3k

LangfuseAnalysis QA Loop

Signal Discovery

Monitor Twitter, HN, RSS, media sites, videos and industry sources with topic and keyword tracking.

Collection

Fetch articles, attachments, transcripts and comments, then normalize them into one processing pipeline.

Summarization & Tagging

Generate summaries, tags, event cues and impact hints so analysts spend less time on first-pass filtering.

Intelligence Archive

Store conclusions together with original evidence for shared search, auditability and historical lookup.

Digest Delivery

Push daily digests, weekly reports and thematic briefings to email, Slack, Feishu or Telegram.

Less Noise

AI filters before it pushes, so the team sees signal first

Earlier Detection

Competitor moves and policy changes surface faster

Reusable Intelligence Assets

Today’s feed becomes tomorrow’s searchable institutional memory

Shared Team Visibility

Useful information stops living inside a few private chats

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[ EVAL-LOOP ]

LLM Quality Feedback Loop

Move from “feels better” to testable release criteria for prompts, agents and RAG

Connect traces, datasets, automated evaluation, CI gates and trend dashboards so every iteration has evidence behind it.

LangfuseTracing & Dataset Capture

PromptfooRegression Testing & CI Gate

DeepEvalMetrics & Judge Framework

Trace Collection

Capture real production calls, tool usage and failure paths instead of relying on anecdotal feedback.

Dataset Building

Turn bad cases, edge cases and high-value requests into reproducible evaluation datasets.

Metric Design

Define thresholds for accuracy, faithfulness, structured output quality, robustness and safety.

CI Gating

Run evaluations in PR and release pipelines so obvious regressions are blocked before deployment.

Trend Review

Track model swaps, prompt updates and fixes over time so optimization decisions are evidence-based.

Clear Release Gates

Define what good looks like before shipping anything

Reproducible Failures

Bad cases become stable tests instead of fuzzy memories

Better Prioritization

See whether the problem is retrieval, prompting or tools

Quality and Safety Together

Govern reliability and risk inside the same operating loop

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[ REDTEAM ]

AI Security Red Team

Baseline scanning and retesting for jailbreaks, injections, leakage and tool abuse

Attack your model APIs, agents and application entry points systematically, then turn the findings into evidence-backed remediation work.

GarakBaseline Scanning

PyRITAdversarial Test Orchestration

LangfuseAttack Trace Review

Attack Surface Mapping

Inventory exposed interfaces including APIs, system prompts, tool calls, uploads and permission boundaries.

Baseline Scanning

Run jailbreak, prompt injection, leakage and denial-style probes against the real system.

Adversarial Orchestration

Build scenario-based multi-turn attacks to see whether the system holds under realistic pressure.

Remediation Report

Produce risk ranking, reproduction steps and fix priorities that engineering teams can act on.

Retest & Gate

Verify fixes after remediation and move critical attack cases into release gating when needed.

Expose Risk Earlier

Find weak points before they become production incidents

Reports Drive Action

Evidence and priorities help teams fix the right things first

Retestable Fixes

You can verify whether risk actually dropped after changes

Fits Release Governance

Security testing becomes an operating mechanism, not a one-off event

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[ FLOW-OPS ]

Multi-Agent Workflow Automation

Turn Q&A, collection, approvals, reporting and tool use into auditable execution pipelines

This is not a chatbot demo. It is an operating workflow where multiple agent roles, tools and human approvals collaborate to deliver a real business outcome.

EigentMulti-Agent Role Coordination

n8nBusiness Workflow Orchestration

MCPTool Access & Context Integration

Workflow Decomposition

Break a complex process into collection, decision, generation, approval and notification units that agents can own.

Role Design

Assign clear responsibilities and context boundaries so one large model is not forced to do everything at once.

Tool Integration

Connect databases, forms, CRM systems, documents and browser actions through MCP, APIs and workflow nodes.

Human-in-the-Loop

Keep critical approvals, budget confirmations and sensitive operations under human control.

Operational Governance

Track cost, latency, failure reasons and human intervention rate to keep the pipeline stable and efficient.

Less Manual Handoff

Reduce copy-paste and repetitive coordination across systems

Parallel Execution

Multiple roles can work at once, shortening turnaround time

Human Safety Valve

Important decisions stay governed instead of fully delegated

Auditable End to End

You can trace who did what, when, and where failures occurred

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[ FINTECH ]

Private Financial Data Hub

OpenBB · Qlib · vnpy — Private Deployment & Deep Customization of Open-Source Financial Tools

Mature open-source financial tools like OpenBB (open-source Bloomberg), Qlib (Microsoft AI quant) and vnpy exist, but none are ready out of the box. We handle private deployment, local data source integration, localization and AI enhancement to deliver a system that actually works.

OpenBBFinancial Data Terminal

66k

QlibAI Quantitative Research

41k

RAGFlowResearch Report KB

25.3k

Solution Design

Select the best open-source base and component combination based on your use case: research analysis, quant strategies, live trading, or report retrieval.

Private Deployment

Docker Compose one-click deployment to your private network. Dependency resolution, known bug fixes (e.g. OpenBB #7379), data persistence configuration.

Local Data Sources

Build custom data providers connecting AKShare, Tushare and other local APIs. Coverage includes equities, futures, bonds, funds and exchange filings.

AI Enhancement

Integrate RAGFlow research report knowledge base + LLM natural language queries + sentiment analysis, adding deep AI capabilities to the data terminal.

Ongoing Support

Data source health monitoring, version upgrade assessment, ticketed SLA response to keep the system running reliably long-term.

Data Stays Private

Fully self-hosted deployment, meeting financial compliance requirements

Native Localization

Local data sources + localized UI + local-language report search

AI-Powered

Beyond data queries — AI analysis and report generation included

Cost-Effective

Built on open-source, significantly cheaper than commercial terminals

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[ COMING SOON ]