AI Vendor Due Diligence: Questions to Ask Before Selecting a GenAI Partner

Selecting a GenAI vendor is no longer a “tool choice.” It is a design decision that will shape your data posture, your operating model, and how quickly teams can ship new experiences without creating hidden risk.

A good partner can help you modernize legacy systems, strengthen knowledge workflows, and turn scattered content into reliable, governed automation. The wrong partner can lock you into opaque pricing, brittle integrations, and safety gaps that are expensive to unwind.

Why GenAI vendor due diligence feels different than traditional software

GenAI systems do not behave like deterministic applications. Output quality shifts with prompts, retrieved context, model updates, and even traffic patterns. That makes “trust, but verify” more than a saying. It becomes a procurement requirement.

Due diligence also has a wider blast radius. GenAI touches regulated data, brand voice, employee workflows, customer conversations, and IP. A vendor that is excellent at demos can still struggle with production realities like evaluation design, audit trails, change control, and incident response.

One sentence that helps teams stay grounded: you are not buying a model, you are buying a production system.

Start by pinning down your “definition of done”

Before sending questions to vendors, get internal clarity on what success must look like in production, not in a pilot. This is where many evaluations drift, because teams compare vendors on general capability instead of the requirements that actually matter.

A practical internal pre-brief can be captured on one page:

• Primary use cases

• Data classes involved (public, internal, confidential, regulated)

• Target users and channels (agents, customers, analysts, field teams)

• Deployment boundaries (your cloud, vendor cloud, hybrid, on-prem)

• Non-negotiables (compliance, residency, SSO, auditability, uptime)

• Measurable KPIs (quality, latency, cost per task, adoption, safety)

With that in place, the checklist below becomes much easier to apply consistently.

Model and solution fit: what are we actually building on?

Vendor conversations often start with “which foundation model do you use?” That is useful, yet incomplete. You need to know what is fixed, what is configurable, and what you can change later without rewriting the whole solution.

Ask for a crisp architectural walkthrough that includes the model layer, retrieval layer (RAG), orchestration, tool calling, observability, and policy enforcement.

Key questions to ask, phrased to surface real details:

• Model choice and portability: Which foundation models are supported today, and can we switch models without rewriting prompts, tools, and evaluation harnesses?

• Customization approach: Do you recommend prompt engineering, retrieval augmentation, fine-tuning, or a combination? What are the decision rules?

• Data boundaries: What data is used only at inference time, what is stored, and what (if anything) is used to improve models?

• Multilingual and domain depth: Which languages, document types, and domain vocabularies are proven in production?

• Agentic behavior: If the solution includes agents, how are tools approved, scoped, and constrained to prevent runaway actions?

It also helps to ask what the vendor will not do. Clear “no” answers are often a sign of maturity.

Data privacy, security, and compliance: prove it with artifacts

Security claims without artifacts are just marketing. Treat this section like you would for any platform that touches sensitive data, then add GenAI-specific controls: prompt and response logging, redaction, policy filters, and output governance.

Request documentation, not only verbal assurances, and ensure your security team can review it early. A strong vendor can share a security packet with clear boundaries.

After a paragraph like the above, a short list keeps the security request concrete:

• SOC 2 Type II or ISO 27001 evidence

• Encryption at rest and in transit details

• Data residency options and defaults

• Retention and deletion policies for prompts, files, embeddings, logs

• SSO/MFA and role-based access model

• Pen test cadence and the latest summary report (with remediation approach)

If you operate in regulated environments, ask exactly how HIPAA, GDPR, CCPA, or sector-specific controls map to the system design, not just to the company’s policies.

Reliability and performance: benchmark what matters in your workflow

GenAI quality is multi-dimensional. Accuracy alone is rarely sufficient, especially for generative tasks. You want repeatability, groundedness, safe behavior, and the ability to degrade gracefully.

A useful vendor conversation focuses on measurable signals in production:

• Latency at P50 and P95 under load

• Throughput at peak traffic

• Uptime and historical incident patterns

• Error rate, timeouts, and fallback behavior

• Safety outcomes (refusals, policy blocks, unsafe content rate)

• Retrieval quality (coverage, citation accuracy, stale content risk)

Ask vendors to run a pilot with your representative data and your constraints. If possible, run two vendors in parallel on the same inputs. Side-by-side results cut through subjective impressions quickly.

A scorecard you can actually use (with evidence and red flags)

A weighted scoring matrix turns a long list of questions into a decision you can defend. Keep it simple enough that stakeholders will use it, and strict enough that vendors cannot “hand-wave” past gaps.

Here is a practical structure you can adapt:

Category	Typical weight	What to request as evidence	Common red flags
Security and compliance	20 to 30%	Certifications, data flow diagrams, access controls, retention policy	Vague answers, “we’re working on SOC 2,” unclear residency
Output quality and safety	20 to 25%	Evaluation plan, test results on your data, safety policy design	No eval harness, no groundedness strategy, inconsistent outputs
Integration and deployment	15 to 20%	API docs, reference architectures, supported connectors, IaC patterns	Hard dependency on one model or one cloud, brittle auth
Observability and governance	10 to 15%	Audit trails, prompt logging options, redaction, policy enforcement	No traceability, limited admin controls
Commercials and TCO	10 to 20%	Pricing model details, usage reporting, support costs, egress fees	Hidden fees, unclear overages, cost volatility
Roadmap and support	10 to 15%	Roadmap view, SLA terms, escalation model, training plan	“No SLA,” unclear ownership after go-live

Weights should reflect your reality. A healthcare workflow may overweight compliance. A high-volume customer support bot may overweight latency and cost per resolved ticket.

Transparency and auditability: can you explain what happened later?

In many organizations, the hard part is not generating an answer. It is defending the answer after the fact. This is where transparency features become operational necessities.

Ask vendors to show how an output can be traced end-to-end:

• What prompt was sent?

• What retrieved passages were used?

• Which tools were called?

• What policy checks ran?

• What model version produced the response?

• What data was written back to systems of record?

Then ask how those traces are stored, who can access them, and how they are redacted when they contain sensitive information.

After that foundation, use questions that force specifics with two-part prompts:

• Audit trails: What events are logged by default, and what is configurable per use case?

• Model changes: How are model or prompt updates tested, approved, and rolled back?

• Explainability: Do you provide citations, confidence cues, or rationales, and how do you prevent those from becoming misleading?

If your use case is high-stakes, ask whether human review is supported at key points, including queueing, sampling, and exception handling.

Bias, safety, and misuse resistance: what happens when users push boundaries?

GenAI systems will be probed by curious users, frustrated users, and sometimes malicious users. Your due diligence should treat misuse as a normal operating condition.

A vendor should be able to describe both proactive and reactive controls: policy filtering, jailbreak resistance, prompt injection defenses, sensitive data detection, and monitoring for anomalous behavior.

It is reasonable to ask whether they run fairness testing or bias audits, and how they handle sensitive attributes. Even when you cannot measure every possible bias dimension, you can demand a disciplined approach to testing, reporting, and mitigation.

Delivery and operations: who owns success after go-live?

Many GenAI programs stumble after launch because the vendor’s “implementation” stops at integration. Real value comes from ongoing iteration: prompt improvements, retrieval tuning, evaluation updates, and governance.

Ask the vendor to describe their post-launch operating model in plain language. Who responds to incidents? Who tunes performance? Who manages changes safely? What does a month-two optimization cycle look like?

A short set of operational expectations can be captured in a numbered list that stakeholders can sign up to:

1. Named owners for platform, data, and model behavior

2. A documented escalation path with response targets

3. A routine cadence for evaluation review and improvement releases

4. Shared dashboards for cost, quality, latency, and safety signals

If a vendor cannot describe operations clearly, they may be relying on heroics rather than process.

Commercial model and total cost: make volatility visible early

Token-based pricing can look attractive and still produce budget surprises. Cost drivers include context length, retrieval strategy, tool calls, concurrency, logging, environment duplication, and traffic spikes.

To keep commercials grounded, request a model of your expected usage with sensitivity bands:

• What happens to cost if traffic doubles?

• What happens if average context length grows?

• What is the cost impact of adding citations and richer traces?

• How are overages priced, and how quickly are they reported?

Also ask about contract flexibility: scaling down, switching models, and terminating without losing access to your data.

Exit strategy and lock-in: plan the “break glass” path

A healthy vendor relationship still needs a clean exit path. This is not pessimism. It is prudent engineering and procurement.

Focus on portability at three layers: data, retrieval assets, and application logic.

Ask questions that get to practical migration steps:

• Can we export embeddings, indexes, prompts, evaluation datasets, and logs in standard formats?

• If we terminate, what is the deletion timeline and proof mechanism?

• Can we run the solution in our own cloud or environment if requirements change?

Vendors that welcome these questions tend to be confident in long-term value rather than contractual friction.

What a strong GenAI partner looks like in practice

Across industries, strong partners share a few consistent behaviors: they propose a measurable pilot, they insist on governance, they benchmark with your data, and they treat security and operations as part of the product.

Organizations often benefit from a vendor that can cover the full lifecycle, from strategy through engineering and managed services, especially when modernizing legacy platforms into AI-ready systems. Firms like EC Infosolutions, which focus on custom GenAI solutions, cloud implementations, and legacy modernization, are typically evaluated on their ability to deliver production-grade architectures, integrate across enterprise systems, and keep outcomes stable as models and requirements change.

The best next step is simple and energizing: take the scorecard, select one high-impact workflow, run a production-like pilot with clear KPIs, and let real evidence choose your partner.