Make Your AI Feature Measurably More Reliable

An AI Reliability Assessment is a fixed-scope review of one AI-powered workflow. Jones Digital designs and runs representative, edge-case, and failure-oriented tests to measure how the feature behaves across accuracy, consistency, latency, token cost, regression risk, and unexpected inputs.

Good-fit workflows include chatbots, document assistants, AI search, customer-support agents, classification systems, extraction workflows, summarization tools, internal copilots, automated agents, and other generative AI features that need more structured reliability evidence.

Jones Digital is built for small B2B SaaS and software teams that have shipped, piloted, or are preparing to launch an AI-powered feature but do not yet have dedicated AI evaluation or AI QA coverage.

ChatGPT can generate test ideas, and teams should use AI tools where they help. The value of Jones Digital is structured test design, realistic coverage, repeatable scoring, failure categorization, latency and cost measurement, independent review, and prioritized recommendations your team can act on. The work is not just generating prompts; it is knowing what to test, how to score it, and what the results mean for release risk.

Yes. Jones Digital maintains internal testing patterns, rubrics, failure categories, and challenge-case methods. Client-specific test cases and findings are delivered to the client, while internal methodology and generalized test libraries remain proprietary.

Yes. The assessment is designed to leave your team with reusable artifacts, including representative test cases, failure examples, scoring notes, and recommendations that can inform future regression checks or internal evaluation suites.

Usually a test account, sample inputs, expected behavior, relevant prompts or configuration, and a safe way to exercise the AI workflow. Production access is not required for initial discussion and is often unnecessary for the pilot assessment.

Not necessarily. Many assessments can use staging environments, sanitized examples, synthetic-but-realistic cases, exported prompts, or sample documents. If sensitive data is involved, access boundaries should be defined before work begins.

Yes. If the workflow, prompts, product behavior, or test data are confidential, Jones Digital can work under an NDA before reviewing sensitive implementation details.

The pilot assessment is designed for approximately 10 business days after scope, payment, and access are confirmed. Larger or multi-workflow assessments may require a separate timeline.

The pilot covers one AI-powered workflow, scoped before work begins. It includes discovery, test design, measurement, failure categorization, prioritized recommendations, a written report, reusable test artifacts, and a scheduled results-review meeting.

The $2,500 offer is an introductory pilot price for a focused one-workflow assessment. Standard future assessments are expected to fall around $4,000-$6,000 depending on scope, with larger engagements scoped separately.

Jones Digital uses scope-based pricing rather than a public sliding scale. Smaller assessments stay focused on one workflow and a limited test set. Larger assessments may include more workflows, deeper coverage, additional reporting, or follow-up regression checks.

The assessment is designed to be less expensive than pulling a senior engineer or QA lead away from product work for one to two weeks. It also helps reduce the risk of shipping an unreliable AI feature that damages customer trust, support quality, or release confidence.

For pilot engagements, payment is typically 50% before work begins and 50% at delivery. Larger engagements may use milestone-based payment terms defined in the statement of work.

Bring one target workflow, a description of the intended users, examples of good and bad outputs, known failure cases, success criteria, and a safe way to test the feature. If those are not fully defined yet, the discovery step can help clarify them.

Yes. Pre-launch assessment is often the best time to test because the team can address reliability problems before customers depend on the feature.

Yes. A live feature can be assessed using a safe test path, staging environment, test account, sanitized examples, or agreed-upon workflow boundaries that avoid disrupting real users.

Where appropriate, yes. Jones Digital can compare prompt versions, model choices, retrieval changes, or workflow changes against the same test set to help show whether a change improved or degraded reliability.

Yes, when those metrics are available. Cost and response time matter because a technically better answer may still be too slow or expensive for production use.

Yes. Hallucination risk can be evaluated through expected-answer checks, source-grounding checks, retrieval-failure cases, adversarial prompts, and examples where the correct behavior is to say that the answer is unknown.

Yes. Document assistants and AI search workflows can be tested for retrieval quality, answer grounding, missing context, citation behavior, irrelevant matches, and failure cases where the right document exists but the system does not use it properly.

No. Jones Digital provides reliability evidence, findings, and recommendations. The assessment can support release decisions, but it is not a formal certification, warranty, or guarantee that an AI system is always accurate or safe.

No. Jones Digital does not provide formal penetration testing, compliance certification, SOC 2 work, HIPAA certification, or legal assurance. The focus is AI behavior, reliability, regression risk, and engineering-readiness evidence.

Jones Digital can include basic adversarial and misuse-oriented prompts as part of reliability testing, especially when they affect product behavior. This is not a replacement for formal security testing by a qualified penetration testing provider.

Yes. The business is designed around scheduled, fixed-scope, mostly asynchronous work with a final results-review meeting. This keeps the engagement efficient for busy software teams.

No. Jones Digital can advise on monitoring strategy and scheduled follow-up validation, but does not provide 24/7 production monitoring, incident response, or emergency support retainers.

Your team can use the findings to prioritize fixes, update prompts, improve retrieval, add guardrails, adjust product expectations, or build internal regression checks. A follow-up retest can be scoped after your team makes changes.

Yes. Follow-up regression checks can be scoped separately to rerun the test set, compare before-and-after behavior, and confirm whether reliability improved.

No. Jones Digital is focused on AI reliability testing and assessment. The work may produce recommendations for prompts, retrieval, product behavior, or evaluation coverage, but it is not a full AI product development engagement.

No. Jones Digital adds focused AI evaluation coverage where normal QA may not be enough. The best results happen when the assessment complements your existing product, engineering, and QA process.

Poor fits include undefined ideas with no working feature, requests for guaranteed accuracy, emergency production incidents, broad AI transformation strategy, formal compliance certification, or projects where no safe test access or expected behavior can be defined.

Start with a discovery call or email. Share the AI workflow, who uses it, current testing method, known concerns, desired timeline, and whether the feature is pre-launch or already live.