Choosing an AI application development company is essentially choosing a partner who will “take on the technical uncertainty for you”. This article provides a general judgment framework that has nothing to do with region for enterprises selecting companies across the country: Three types of service providers, 7 ability assessments, 9 clauses that must be written clearly in the contract, and a phased selection process. It also explains why “the lowest bid” is often the starting point for project failure.
1. Three types of AI application development companies
| Type | Core capabilities | Suitable for customers | Risk points |
|---|---|---|---|
| Ecological partners of large model/platform manufacturers | Build industry implementation based on mainstream large models and cloud platforms | Already have a cloud strategy and need to go online quickly | Easy to be bound to a single model and cloud ecosystem |
| Industry custom development company | RAG, fine-tuning, system integration, privatized deployment | Core business scenarios, sensitive data | Quality varies, case verification is the key |
| Low-code/SaaS implementer | Quickly built based on Coze, Dify and other platforms | Customer service, knowledge Q&A and other standard scenarios | Weak in-depth customization capabilities, data on the platform side |
To determine what kind of needs you have, you can first ask: Can the data leave the intranet? Are processes standardized? Do I need to go online within three months? The more “no/non-standard/yes” the three answers are, the more you should prefer an industry custom development company.
2. Evaluate the 7 capabilities of AI development companies
1. Architectural design capabilities, not just API adjustment
Let the other party explain what happens inside the system when a user asks a question: intent recognition, retrieval, rearrangement, prompt assembly, model calling, and result verification. Teams that cannot explain this link cannot make adjustments when encountering answer hallucinations and knowledge base conflicts. It is required to deliver a complete architecture diagram and technical selection instructions.
2. RAG engineering and data processing experience
80% of current enterprise AI applications are retrieval augmentation (RAG) scenarios. Key inspections: document segmentation strategy, vector library selection, hybrid retrieval and reordering, citation tracing, and evaluation set construction. Whether there is a sense of “evaluation set” is the watershed between professional teams and shelling teams.
3. Verifiable industry cases
Required to provide 2-3 cases in the same industry: online system demonstration, quantitative indicators, and customer contact back-up authorization. Case indicators should be specific to numbers, such as “contract review was reduced from an average of 4 hours to 40 minutes, and the accuracy of clause extraction was 92%” rather than “significantly improving efficiency.”
4. POC verification mechanism
Professional teams usually recommend 1-3 weeks of POC, verify with real customer data, and jointly confirm acceptance indicators before POC. Those who refuse the POC or require direct signing of a full payment contract should be downgraded.
5. Security compliance and privatized deployment
Confirm: supported deployment methods (public cloud/private cloud/pure intranet), whether the model can be localized, log retention policy, and data ownership terms. Scenarios involving personal information must comply with the “Personal Information Protection Law” and “Interim Measures for the Management of Generative Artificial Intelligence Services“.
6. Delivery and operation and maintenance system
See if there is a standard delivery process (demand freezing, prototype confirmation, iteration review, UAT acceptance), as well as the SLA after launch: fault response timeliness, knowledge base update method, model version upgrade strategy, and annual operation and maintenance cost caliber.
7. Team stability and company fundamentals
Verify the age of establishment, number of people with social security, and lawsuits involved through the enterprise information platform; during the interview stage, it is required to get to know the future project managers and core engineers to avoid “sales orders and new people delivering.”
3. 9 clauses that must be written clearly in the contract
- List of deliverables: Source code, model files, prompts, knowledge base documents, and deployment manuals are listed one by one.
- Intellectual Property Ownership: The customized code and training results belong to Party A, and the supplier only retains the license for general components.
- Quantitative acceptance criteria: Test methods and passing lines for accuracy, recall, concurrency, and response latency.
- Payment rhythm: It is recommended to bind the four nodes of 3:4:2:1: demand confirmation, online trial operation, final inspection, and warranty expiration.
- Data and confidentiality: Commitment that data will not leave the domain, deletion mechanism and liquidated damages after the project is completed.
- Key Personnel Terms: Changes to core members require advance notice and consent.
- Warranty period: Free bug fixes shall be no less than 6 months.
- Delay liability: Clarify the proportion of penalty for delay and refund for termination of contract.
- Operation and maintenance pricing: The operation and maintenance costs after the warranty period are locked in advance with the SLA.
4. Selection process: from demand letter to contract signing
- Write a requirement letter: Complete six elements: scenario, data, quantitative goals, deployment environment, budget, and time. The quality of the demand letter directly determines the comparability of the quotation.
- Issue an RFP: At least 4 companies are required to respond according to a unified template to avoid each company telling its own story.
- Proposal review: Independent scoring in four dimensions: structural rationality, case matching, team input, and quotation split.
- POC competition: 2-3 families use the same batch of real data and the same set of indicators to PK, and the fees are agreed in advance.
- Due diligence and contract signing: Customer background investigation, company fundamentals verification, and 9-clause contract approval.
For preparation work at the strategic level, please refer to “Complete Guide to Enterprise AI Application Development: From Strategy to Implementation“, for a technical self-study perspective, see “Practical Guide to AI Application Development“.
5. Frequently Asked Questions
There are so many AI development companies across the country, how to narrow down the scope?
First use a demand letter to issue an RFP to collect 4-8 proposals, and then shortlist 2-3 proposals based on the four dimensions of structure, case, team, and quotation. Then use the same set of indicators to conduct a paid POC competition. The negotiation method of “quote the lowest price first” is not accepted throughout the process.
How much does an AI development project generally cost?
Standard intelligent agents usually cost 10,000-100,000 yuan, industry customized systems commonly cost 100,000-500,000 yuan, and complex middle platforms involving multi-system integration can cost more than 500,000-1 million yuan. Be sure to ask for separate quotations for development fees, model and cloud resource fees, and operation and maintenance fees.
How to choose between custom development and purchasing SaaS?
If the process is standard, the data is not sensitive, and if you want to go online quickly, choose SaaS or low-code; if the core process, the data is sensitive, and you need in-depth communication with the internal system, choose self-development or customization. You can also use SaaS run-through to verify the business value before deciding whether to customize it.
What should be accepted during the POC stage?
Accept business indicators instead of demonstration impressions: knowledge question and answer hit rate, key information extraction accuracy, rejection rate, single round cost, response delay, and use the same test set to compare different suppliers.
How to prevent being deceived by “shell” companies?
Three questions: Ask them to draw a complete technical structure, explain the search and evaluation plan, and provide customers in the same industry who can be adjusted back. Points will be deducted for any ambiguity.
If you are doing nationwide selection, it is recommended to copy the terms of this article directly into your RFP and contract template. If local enterprises in Guangzhou need on-site services at the same time, they can refer to Guangzhou AI Application Development Company’s Localization Evaluation Dimensions; Juzhao Data is also welcome to be included in the comparison according to the same set of standards and speak with POC results.
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