ZhiHua Tech designs and delivers production-ready enterprise AI applications that connect knowledge, customer service, document processing and workflow automation with existing business systems. We can begin with a focused PoC or improve an existing platform, with scope, cost, acceptance criteria and handover assets agreed before development.
You do not need a complete requirements document for the first conversation. Tell us the business problem, your current systems and preferred timeline; we will help assess the scope, delivery approach and whether a PoC is appropriate.
Add knowledge questions and answers, document processing and smart assistants to existing SaaS, worksheets, projects or enterprise systems to check interfaces and data privileges and retain available systems.
View ScopeDesigning AI competencies with product interfaces, account privileges, subscription billing and business processes, moving from the first version to operational applications.
View ScopeSelect a duplicate process that identifies the input, the results to be generated, the points of manual confirmation and the business system to which the results are to be written back.
View ScopeAssess existing codes, samples, privileges and deployment conditions, complete interfaces, error processing, assessment and handover of information, and do not treat presentations directly as production software.
View ScopeThe first phase can be limited to “info-sorting and drafting” and does not automatically send quotations or substitute professional judgement.
Clients receive not only a dialogue window, but also agreed interfaces, interfaces, source codes, testing and deployment information.
Cases have been disclosed, including knowledge base, orders and member queries, manual takeovers and online assessment processes. It helps understand how AI enters the business system, with the effect and the evidence calibrated to the case page.
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Demand is described as "do an AI", lack of real tasks and acceptance calibration
Modelling is good, but it does not stabilize the connection to business knowledge and business systems.
Psychs, knowledge, interfaces and privileges are managed by different tools and production risks are not controlled
AI output is not re-detectable, errors, costs and manual interventions are not recorded on an ongoing basis
Only pages or accounts are received at the end of the project, and there are no source codes, evaluation and deployment assets
Custom AI Development Needs Diagnosis, Task Dismantling and First Range Planning
Corporate business construction product interfaces, account privileges, task flow and business data management; provision of customized services for enterprise AI, AI manual smart customization development and AI system customization development, as required.
Enterprise AI application, large model software, RAGknowledge base and smart question and answer custom development
AI Agent, tool call, multistep tasks and manual approval organization
Smart passenger service, document recognition, quotation aids, data analysis and visual voice applications
ERP, CRM, OA, MES, database and third-party API integration
Model selection, model gateway, cloud, mixing and pilvate deproyment
Identity clearance, sensitive data protection, audit, abnormal retreat and security testing
Real task assessment, greyscale upline, cost monitoring and AgentOps ongoing operations
The final delivery boundaries are defined according to the scope of services, the construction phase and the modalities of cooperation, and are described below as common results.
Is the first issue only a document or draft programme generated, or is it multisectoral collaboration, approval and systematic rewriting? First, it is clear that the business closed circle that must be completed is followed by a comparison of inputs under the same scope.
Level of integrity of existing codes, data, systems, equipment and documents, and scope of coverage to be audited, relocated or re-engineered
Number of third-party interfaces, coordination responsibilities, data quality, unusual compensation and external supplier cooperation
Non-functional requirements such as performance, availability, security, authority, audit, compliance and access windows
Delivery depth and long-term responsibility: test and assessment reports, deployment packages, online and rollback programmes, operations, maintenance, data update, cost governance and knowledge transfer files, and quality assurance, peacekeeping continuity ranges
Project objectives, responsible persons and acceptance criteria are not established
Key accounts, data, interfaces or business authorizations not available
Only the maximum price or very short cycle is sought, and the necessary tests and quality control are not accepted
A complete review of the current task: who provides the information, who judges, who approves the results and where they go. The exclusive offer, customer information privileges, cross-system echo and multi-player collaboration often requires software engineering; simply changing the hint does not mean redevelopment. Demand interviews should also retain the option of "Abstain or solve ", avoiding access to the model for each button.
For example, the first phase of the Customer Program Assistant can cover only the import of information, authorized knowledge retrieval, draft programmes, manual review and retention of the version. Automatically sent to the client, contract signing, refund judgement may be withheld.
The development fee should be broken down into research and prototype, AI mission engineering, business software, system interface, assessment go-live and interface. Model calls, cloud resources, commercial components and ongoing transport stand alone. The supplier is requested to provide a first-range quote on which terminals, organizations and interfaces are included; the change in demand is recorded for additional work, test impact and confirmation of results by both parties, rather than a comparison of the total price.
Software line check privileges, processes, interface consistency, deployment and take-over; A line check is based on correctness, refusal, manual modification, processing time extension and single task cost. Use an authorized sample that is not debugged and maintains a failed sample. The source code, relying list, hint configuration, evaluation method and account exchange should be included in the range and not replace a runable result with a demonstration video.
The following is a recommended assessment of the performance of the customer, not of the customer, nor of the uniform commitment to meet the standard.
| Checkpoint | How do you check it? | Avoid miscalculation. |
|---|---|---|
| Operational completion rate | Number of tasks to complete the agreed closed circle / included in the evaluation | Single list of rejected answers, manual reworks and interface failures |
| Manual review costs | Recording of total time for reading, checking and modification | You can't just count the time of the model generation. |
| Handoverability | Rebuild and run the handover document in the agreed environment | Recording of missing accounts, dependence and failure to reproduce |
De-sensitization real cases: chaining of Ai's client service: Cases exist that illustrate knowledge, interfaces and manual collaboration; the scope of verification of specific clients, operational data and original records needs to be confirmed.
When the difference is derived from exclusive business processes, knowledge rights, system actions or ongoing operating requirements, customization is of clear value. If the demand is for generic writing, summary or personal questions and answers, it is usually more reasonable to test the ready-made tool. First communication should first identify the user, the outcome of the mission and the consequences of the error, and then decide whether to make the prototype or the official software.
The implementation boundaries and acceptances for this category of projects are described below.Look directly at the details.
The first issue is to select a closed loop that can be quantified, a real sample is available and error-ridden to verify key unknowns and complete the production project after adoption.
The level of uncertainty is reduced by stages before deciding on the scale of inputs and the modalities of cooperation.
Recovery of existing processes, volume of processing, manual baselines, knowledge data, system interfaces, error consequences and initial boundary.
Use of real task set comparison models, RAG, rules and tools to record quality, delay, cost and manual intervention.
The product interface, identity privileges, systems integration, log monitoring, abnormal retreats, testing deployment and continuous evaluation are completed.
Models API, reasoning algorithms, third-party software licences and cloud resources are usually presented separately by actual programme; clients are responsible for business rules, data authorizations, professional findings and high-risk action approvals. AI output is probabilities, with formal commitments, amounts, compliance and security-related decisions implicitly retaining manual confirmations.
The use of custom and artificial intelligence, the Enterprise AI Custale Development, the Custom AI Software, and the Enterprise AI application, are essentially looking for a team that can make proprietary business tasks into production software. The vendor should not be compared with a model demonstration, but rather check its ability to complete business diagnostics, real sample assessments, product development, systems development, access security, source delivery and continuous operations.
The team is required to demonstrate the required boundaries, technical routes, delivery of assets and acceptance methods with a real mission and to verify that it has the complete capability for software engineering and AI engineering.
The first phase should identify users, tasks, data, knowledge, models, interfaces, authority, manual clearance, deployment modalities and operational responsibilities after they are online.
The estimates are split between diagnostics, PoC, production development, systems integration, deployment and transport, and then the prices and cycles under the harmonized scope.
Fixed real task set to check quality, delay, cost, authority, abnormal retreat and transfer source code, configuration, assessment collection, deployment and transport data.
The project will be defined in terms of the construction form, the scope of the first phase, the team configuration and the acceptance and acceptance modalities.
The first issue can start with intelligent retrieval, document processing, business assistant and controlled backwriting, retaining the data lead of the system.
A high-frequency mission suitable for a customer, sales, operation, finance, procurement or project team.
In addition to modelling capacity, you need to consider multi-tenant, account number, billing, content safety and product operations.
The platform should be built with real application to pull it off, avoiding the creation of empty platforms.
There is no need to write a complete demand letter; how much is prepared. The more specific the information, the easier it is to judge whether it is to buy mature tools, do systems integration, or start custom development.
Each stage has clear objectives, participatory roles and assessable outcomes, and important decisions are not left to the end of the project.
The following is the original teaching content of the project, not proof of the results of the client's project.
Enterprise AI should start with business tasks and process results, not with the first procurement model. When processes are confused and data are not subject to liability, AI will only magnify inconsistencies. First, it will identify users, input, expected results, manual clearance and system actions, then select models, RAGs, rules or automated routes using real samples.
For more information.Original video courseChecking web pages only for return 200 does not prove that registration, login, tabulation, payment or data synchronization is really available. The Cordex workflow can execute key user paths by script, collect intercepts, responses, logs and results evidence, and notify those responsible when failure is classified. The inspection account number should use isolation data and minimum permissions, and must be set off when real payments or production changes are made.
For more information.The most common issues before cooperation are clearly stated in advance.
The complete Custom AI Development usually includes scene diagnostics, task and sample preparation, PoC validation, product and architecture design, model and RAG or Agent development, operations systems integration, rights security, assessment testing, deployment on line and continuous operation.
In addition to product, front-end, data and interface engineering, AI custom development processes model selection, knowledge data, probability output assessment, manual takeover, call cost and version changes. The certainty business rules remain the responsibility of the normal software logic, and AI only assumes the elements that are appropriate for understanding, generating and supporting judgement.
Generic tools are suitable for standardized, low-risk missions; customized development will build exclusive applications around enterprise identity, knowledge, data, business rules, systems interfaces and acceptance indicators, and clarify source code, deployment and ongoing operational responsibilities.
When there are key unknowns in model effects, data quality, business rules or systems interfaces, the validation should be done with a real task PoC; if the capability is validated on the same data and the range is stable, direct access to the production design is possible, but a baseline for evaluation is still needed.
Yes. Usually, the primary data and privileges of the original system are maintained through independent AI services, API, news, model gateways or embedded modules, avoiding the overall reconstruction of the system to add AI.
In addition to the source code and deployment documents, models should be delivered with vendor configurations, tips and rules, knowledge processing, assessment of collections and results, tool interfaces, competency audits, operational monitoring and known limitations.
Yes. It is recommended that needs diagnostics or PoCs, production applications development, system connection and go-live, and continuous transport be separated into coverage and acceptance conditions.
The ready-made tools are usually sufficient if only generic writing and personal questions and answers are available; application development, system integration and production governance are still needed if there is a need to connect business knowledge, competencies, client data, business processes or existing systems.
The model is not always available for cost-free replacement, but should be able to assess the impact of migration using the same task set.
The project scope should be defined around a closed operating loop. Ultimately, it should also be delivered with the source code, configuration, assessment, interface, deployment and maintenance.
View full answerCustom AI Development, AI app customization and construction of enterprise AIThe price is not uniform by page number or model name only. The price is mainly subject to business tasks, sample and knowledge quality, model routes, system interfaces, role privileges, product terminals, deployment patterns, depth assessment, performance security and ongoing operations. It is recommended that diagnostics, PoC, production development and transport be estimated in stages. Any precise total price given without knowledge of the real task is used as a marketing reference.
View full answerCustom AI Development, AI app customization and construction of enterprise AIThe Custom AI Development cannot only look at several successful demonstrations, but should also verify the AI effects, software engineering, business results and project assets. Use the frozen real task set to check the correct, wrong, rejected, ultra-abnormal and abnormal scenes; check interfaces, privileges, performance, logs, regressions and manual takeovers; recheck adoption rates, processing cycles, manual modifications and running costs.
View full answerCustom AI Development, AI app customization and construction of enterprise AIFirst, the team can translate the AI vision into operational tasks, real samples, technical risks, and acceptance methods, rather than model names and demonstration effects. A qualified vendor should have both AI applications, software engineering, systems integration, data clearance, testing deployment and ongoing operations. It is required to explain the scope, failure sample, delivery of assets and up-line responsibility of a similar project.
View full answerCheck for quality, safety, clearance, greyscale, back-up and operational responsibilities to be online
For more information.Questions and answers on costsIdentify complete inputs from data, models, interfaces, assessments, deployments and long-term operations
For more information.Open-source AI platformProduction transformation from prototype, deployment, authority, plugin to enterprise systems integration
For more information.Operational AI systemConnecting request for quotations, products, costs, approval, CRM and ERP to a closed round of quotations
For more information.Procurement of AI systemsHarmonization of requirements, quotations, vendor price comparison, risk and evidence of procurement approval
For more information.Automation platformBuild an inter-systemable, reimbursable and receiver-up AI automated process
For more information.The service boundaries, budget bases and modalities of implementation for different phases of the project are not identical and can be further assessed in conjunction with the following.
Collapse requirements · Description of the knowledge tool. The tool is not needed to provide direct micro-credit; the summary is generated only in the browser and is not automatically submitted.
The first goal and the current status need not be complete.
