Aifu Agent runs on AIFU Agent OS and brings a privatized AI Agent — one that never lets your data leave the building — into your corporate intranet. Knowledge Q&A, workflow automation and decision support, delivered end to end: assessment, architecture, on-site deployment and ongoing operations. Not just another SaaS login.
The AI Agent runs in your own data center or private cloud. Data never leaves the building, meeting the compliance bar for finance, healthcare and government.
Business assessment, architecture design, model selection and application rollout — one team accountable for a working system, not a POC demo.
Engineers on site complete deployment and integration inside your intranet, connecting to existing ERP / OA / knowledge bases to lower the IT barrier.
Continuous operations, effect monitoring and iterative improvement after go-live, with measurable business metrics for long-term stability.
Connect documents, databases and business systems to build a governed private knowledge base with traceable, auditable answers.
Support for mainstream open-source and commercial models deployed privately, with compute sized to your workload for the best balance of quality and cost.
Work alongside frontline teams to identify high-value, quantifiable AI scenarios with clear acceptance criteria and boundaries.
Design a private architecture around your existing IT landscape, settling model, knowledge sources, integration and compliance strategy.
Deploy inside your intranet, connect existing systems, then run integration testing, load testing and security hardening.
Monitor results, gather feedback and iterate after go-live, consolidating everything into a reusable enterprise AI asset.
Cases are anonymized samples; replace with real customers and authorized metrics before launch.
Challenge: QC standards live in veteran engineers' heads and scattered PDFs. New hires ramp slowly, the plant network cannot reach the cloud, and standards go stale.
Approach: Deploy QC specifications, the historical defect library and drawings as an offline Agent embedded in shop-floor terminals, supporting text-and-image Q&A and verdict suggestions — working with no network.
Outcome: New hires reach independent work faster, with consistent QC criteria across shifts.
Challenge: Rules and clinical guidelines are scattered across systems, staff struggle to look things up, patient data must never leave, and audit requirements are strict.
Approach: Deploy a governed knowledge assistant on hospital servers, wired to internal document permissions, with cited sources and no data ever leaving the hospital.
Outcome: Faster lookups for admin and clinical staff, lower compliance risk, fully traceable audits.
Challenge: Credit due diligence depends on manual research and drafting — slow, inconsistent, and the client data is too sensitive for a public cloud.
Approach: Deploy a private due-diligence Agent connected to internal data and sanitized external feeds, auto-generating structured drafts and risk summaries.
Outcome: Drafting time for a single report drops sharply, with far more consistent output.
Challenge: Public services span many departments with heavy inquiry volume and fragmented guidance — all of which must run inside an intranet and stay auditable.
Approach: Consolidate multi-department service guides and deploy a navigation Agent on a domestic stack, supporting item recommendations, document pre-checks and path planning.
Outcome: Less counter pressure, shorter handling times and higher public satisfaction.
Challenge: Equipment manuals and fault cases are scattered, troubleshooting relies on individual experience, and some sites have no external network at all.
Approach: Build an offline knowledge hub consolidating manuals and historical work orders, supporting natural-language troubleshooting and remediation advice without network access.
Outcome: Faster average fault localization and knowledge retention that no longer depends on individuals.
Challenge: Many stores and many standards make inspections and training hard to standardize, and operational knowledge is hard to accumulate.
Approach: Deploy an operations assistant on a private cloud consolidating store SOPs and best practices, supporting real-time frontline Q&A and inspection checklist generation.
Outcome: More standardized store openings and inspections, with higher supervisor productivity.
Challenge: First-pass contract review is slow, risky clauses get missed, and client files are strictly confidential and cannot leave the intranet.
Approach: Deploy a private contract review assistant connected to the firm's clause library and templates, auto-tagging risk points with revision suggestions — data never leaves the firm.
Outcome: Faster first-pass review and fewer missed high-risk clauses.
Challenge: Handling incidents such as delays or temperature excursions relies on manual phone coordination — slow to respond and poorly recorded.
Approach: Connect order and temperature data to a disruption response assistant that assesses impact and drafts response messaging and work orders.
Outcome: Faster incident response and fewer customer complaints.
Model and data both run inside your intranet — sensitive information never leaves the building, holding the compliance line.
Meets MLPS, industry regulation and data-localization requirements, with traceable audits.
No dependency on public connectivity; runs stably offline, fitting production floors and classified environments.
Models, compute and knowledge sources all stay in your hands, insulated from third-party outages or policy shifts.
Connects to existing ERP / OA / data platforms, fitting into current workflows rather than starting over.
One-time deployment plus an operations subscription, avoiding usage-based uncertainty and cross-border data risk.
AIFU is a delivery team focused on on-prem AI Agent deployment for enterprises. Our product is Aifu Agent, built on AIFU Agent OS. We believe valuable AI is not a demo floating in the cloud, but productivity running inside a corporate intranet on real business workloads. Through on-site delivery (FDE) we put the Agent into your data center, keep the data in your hands, and keep the results measurable.
Leave your requirements and we will reach out after assessing the scenario. During this demo phase, submissions are stored locally only and no email is sent.