Enterprise AI Solutions
From business diagnosis and PoC validation to integration, private deployment, and operational improvement.
We do more than connect a model. We help connect AI to workflow, knowledge, governance, and real delivery.
Boao Intelligent is a better fit for teams that already know where time is being lost and where manual effort is creating friction. We help turn that into a practical AI implementation path, starting with one scenario and expanding only when results justify it.
Who this page is most relevant for
If your team can already describe which workflows are slow, repetitive, inconsistent, or difficult to scale, you are usually in a strong position to start.
Customer service and sales teams
Best for organizations with heavy inbound traffic, repetitive questions, and a need to improve response speed and lead conversion.
Compliance, legal, and document teams
Useful for teams that review contracts, policies, bidding materials, industry rules, or internal documentation at scale.
Operations and management teams
Strong fit for businesses that want to automate reports, meeting summaries, recurring analysis, and internal coordination tasks.
Companies with private deployment requirements
Suitable for enterprises that need clear data boundaries, local infrastructure, and controlled model usage.
Common reasons companies start here
Most projects do not start with “we need AI.” They start with “this workflow is already slowing the business down.”
Too much repetitive work is still handled manually, creating slow response times and rising labor cost.
Knowledge is scattered across files, spreadsheets, and chat history, so staff struggle to find reliable answers quickly.
The company wants to use AI but needs a clear first scenario with measurable ROI instead of a vague platform project.
Leadership wants a contained pilot first, followed by broader rollout only after results are visible.
Composable solution modules
We do not assume every client needs the same stack. We usually combine modules based on business priority, then decide what should integrate with existing CRM, ERP, OA, knowledge, or private infrastructure.
AI customer service and sales assistant
Supports website, WeCom, social, and private-domain touchpoints with inquiry handling, triage, transfer, and follow-up reminders.
Enterprise knowledge base and Q&A assistant
Turns policies, product material, FAQs, and training documents into a searchable, maintainable knowledge system.
Document review and content generation
Improves review speed for contracts, policies, reports, proposals, and other structured business documents.
Workflow automation copilot
Connects AI into recurring tasks such as summaries, reporting, task flows, approvals, and internal collaboration.
Private deployment and workstation support
Provides local deployment and AI workstation options for organizations with stronger control, security, or infrastructure requirements.
Management dashboard and performance visibility
Tracks usage, hit rate, human handoff, time savings, and business indicators so teams can optimize after launch.
Typical delivery outputs
The goal is not just a demo. The goal is a working deliverable that a real team can adopt, measure, and extend.
Engagement models
Different organizations need different starting points. Some need fast validation, others need full delivery, and some need a longer-term capability partner.
Fast PoC validation
Best when the team wants to validate one high-value use case first.
Usually starts with customer service, knowledge retrieval, document review, or reporting, and aims to produce a clear signal within 2 to 6 weeks.
Project-based delivery
Best when goals are already defined and system integration is required.
Covers discovery, design, implementation, training, and go-live as one focused delivery cycle.
Annual capability partnership
Best when the organization wants to scale AI across multiple scenarios over time.
Useful for quarterly rollout planning, internal enablement, operational optimization, and phased capability building.
Implementation path
We define success metrics first, then move through delivery, training, and post-launch optimization instead of stopping at deployment.
Business diagnosis
Identify the high-frequency, high-cost, high-value workflows most suitable for AI implementation.
Solution design
Define model use, knowledge sources, workflow integration, permission boundaries, and success metrics.
Delivery and launch
Complete configuration, integration, testing, onboarding, and release so business teams can actually use the system.
Performance operations
Track usage and business outcome data, then iterate prompts, knowledge assets, and workflow logic.
Representative outcome examples
The scenarios differ, but the business target is consistent: faster execution, more stable quality, and more repeatable delivery.
AI quality inspection and reporting support
Production efficiency improved by 40%, while quality reporting became faster and issue feedback became more timely.
Intelligent review and risk support
Risk identification accuracy improved by 35%, while manual review cycles were reduced by about 70%.
Operational analysis and inventory coordination
Inventory turnover improved by 30%, and stockout rates dropped by around 60%.
Budget ranges and buying paths
Most teams do not need a single price number. They need to know whether they should validate first, buy a delivery scope now, or plan as a longer capability program.
Fast PoC validation
Usually budgeted at RMB 30k to 100k
Best for validating one high-frequency scenario such as service, knowledge retrieval, document review, or reporting before a broader rollout.
Project delivery
Usually budgeted at RMB 100k to 400k+
Best when integration, permissions, knowledge preparation, and launch support are already in scope and procurement needs a clearer delivery boundary.
Annual capability partnership
Quoted by quarterly or annual rollout rhythm
Best for organizations that want to expand across multiple scenarios over time and need a longer operating partnership instead of a one-off build.
FAQ
Q1 What is the best first step for a company introducing AI for the first time?
Start with a high-frequency, repetitive, measurable scenario such as customer service, knowledge retrieval, document review, or report generation. That creates a faster path to visible ROI and a better basis for wider rollout.
Q2 How long does a typical project take to launch?
A PoC often produces meaningful results within 2 to 6 weeks. A fuller implementation usually takes 4 to 12 weeks depending on integrations, knowledge preparation, and internal coordination complexity.
Q3 Do you support private deployment and local compute?
Yes. We support private cloud, local server, and AI workstation-based delivery paths for organizations that need stronger control over data and infrastructure.
Q4 How do we judge whether a use case is worth doing?
A good use case is usually high-frequency, time-consuming, easy to standardize, and easy to measure. We help prioritize scenarios using those factors before implementation begins.
If you already have a concrete business scenario, we can help evaluate the right first move.
Bring us the workflow problem, not a fully formed technical spec. We can help judge priority, PoC feasibility, delivery path, and whether the scenario is worth scaling.