Define the outcome before requesting proposals
Write down the user task, current process and improvement you want to test. Specify the data the application may access, the systems it must integrate with and the person who accepts the result. A document assistant that prepares drafts has a different delivery scope from an application that publishes decisions or updates customer records.
Give each prospective supplier the same brief and a representative, authorized sample. State constraints such as required languages, response time, hosting preferences and expected workload. Ask suppliers to identify missing information and assumptions in writing. This makes proposals comparable before discussions turn to frameworks or model brands.
Ask for evidence behind the portfolio
Request a walkthrough of a relevant implementation: what the team built, what the customer supplied, how quality was evaluated and what happened after release. Distinguish a demonstration, an illustrative blueprint and an operating customer system. Where a supplier quotes an outcome, ask for the baseline, measurement period, workload and permission to share the evidence.
Check who will actually perform the work. Ask the delivery team to explain one failure they encountered and how they investigated it. A list of tools or certificates can describe experience, but does not establish that the proposed application will meet your requirements. Review references where the customer has authorized contact.
Evaluate a complete slice of the application
Use a bounded pilot that connects an input to a reviewable output through the intended integration. Include missing records, ambiguous requests, unavailable services and unauthorized access attempts. For a document assistant, test whether answers use the approved source and whether the application can decline when evidence is missing.
Separate development examples from final acceptance examples. Compare the proposed system with the current process on the same tasks. Record completion, errors, review corrections, latency and cost per accepted result. Ask who owns the evaluation set and how it will be rerun after a model or data change. Avoid accepting a polished demo as the only evidence.
Compare itemized scope and cost
Separate discovery, data preparation, application development, integration, evaluation and deployment fees. List recurring inference, hosting, storage, monitoring and support charges. State currency, quote validity, taxes where applicable and provider charges paid directly by the customer. A fixed fee is useful only when the included work and change process are explicit.
Ask what the pilot delivers if its acceptance gate is not met. Agree payment milestones around reviewable artifacts and specify which assumptions could change the estimate. Compare the same workload across suppliers rather than treating an unsourced regional price band as a project quote. For training-specific costs, use the fine-tuning budget guide.
Review ownership and operational responsibility
Document access to source code, configuration, model artifacts and data exports. Identify third-party dependencies and license responsibilities. Confirm who holds production accounts, who can deploy a release and how access is removed when the engagement ends. Obtain the relevant contract terms from the supplier rather than relying on a marketing description.
Map data flow and permissions before connecting production systems. Agree retention, incident escalation, human review and the fallback when an AI component fails. Assign responsibility for support hours, provider outages and later upgrades. Ask the team to demonstrate rollback and task recovery, including writes that may already have completed.
Make the handover a delivery requirement
The final package should include setup instructions, versioned configuration, evaluation results, integration contracts, monitoring and an operating runbook. Run an acceptance session using the agreed tasks and record remaining limitations. Have an internal owner reproduce a release or recovery procedure so continued operation does not depend on undocumented knowledge.
Choose the partner whose evidence, scope and operating plan fit the task. Delivery speed, accuracy and return on investment need measurement for your project; no generic multiplier establishes them. Explore AI implementation planning or discuss a scoped application project with TensorBlue.