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An AI agency demo is a hypothesis, not proof that the proposed system will work in your operation. Before signing, define the exact workflow and error limits, rerun it with representative and difficult cases, inspect every human and technical component, verify data and security practices, and attach measurable acceptance criteria to the contract. A convincing presentation should earn a controlled evaluation—not an unconditional commitment.
The safest decision comes from dated, reproducible evidence and clear remedies if performance, security, cost, or portability falls short. There is no universal pass/fail score; your threshold must reflect the workflow, the harm an error could cause, and the alternatives you have today.
What does an AI agency demo actually prove?
A demo proves only that the agency can produce a favorable result in the conditions it selected. It does not establish production accuracy, reliability under load, performance on messy inputs, or the amount of hidden human preparation behind the screen.
Treat the demonstration as a testable claim. Ask the agency to identify the exact workflow shown, the data used, the date and software versions, the people involved, and any steps performed before or after the model ran. If those details cannot be repeated, the result is a sales illustration rather than dependable evidence.
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Keep three distinctions clear:
- Capability: the system can perform a task at least once.
- Performance: it meets your accuracy, speed, quality, and safety thresholds across a defined test population.
- Operational fit: it can be deployed, monitored, secured, supported, integrated, and changed at a cost and risk you accept.
Do not let a polished interface substitute for evidence about the last two points.
How do I define the job before the demo?
Write a short job specification before the agency chooses examples. This prevents the demonstration from quietly redefining success around its strongest scenario.
Describe the user and trigger
State who will use the system, what event starts the workflow, and what authority the user has. For example, distinguish an internal analyst preparing a draft from a system allowed to send a customer response or approve a payment. The same model may be acceptable for one role and unsafe for another.
Specify inputs and outputs
List the formats, languages, fields, attachments, and data quality the system must handle. Define the required output structure, destination, review status, and audit information. Note whether the output is advice, a draft, a classification, a decision recommendation, or an automated action.
Record the baseline and target improvement
Document how the work is done now, who performs it, typical turnaround time, error patterns, rework, and current cost. Then state the improvement you want to test, such as shorter cycle time, fewer manual steps, or better consistency. A claim of savings is meaningful only when the baseline includes the staff time and controls that will remain after automation.
Set unacceptable outcomes
List errors that are tolerable, errors that require human review, and errors that must never occur. Include privacy breaches, fabricated facts, unsafe recommendations, missed deadlines, unauthorized actions, and discriminatory results where relevant. Tie each category to a response: block the action, route it to a person, request more information, or allow it with a warning.
Expose assumptions and exclusions
Ask the agency to state dependencies such as clean source data, a particular software version, a response-time limit, a connected system, or a minimum volume. Require an explicit list of excluded cases. An exclusion is not automatically a deal-breaker; it becomes a problem when it is hidden or conflicts with your real workload.
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How should I test representative work?
Use an approved test set that mirrors the variety and risk of production, then repeat the same workflow without hidden manual preparation. A single successful example cannot establish reliability.
Build three case groups
- Normal cases: routine inputs that make up most of the expected volume.
- Difficult cases: incomplete records, unusual wording, long documents, conflicting fields, poor scans, multiple languages, or unusually high workload.
- Known exceptions: cases that currently require specialist judgment, policy exceptions, escalation, or a different process.
Keep the test population and expected answers under your control where possible. If confidentiality prevents sharing real records, use approved redacted or synthetic examples that preserve the same structure, ambiguity, and failure risks. Record which cases were included and which were withheld.
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Run the workflow twice
Ask for a second run using the same inputs and instructions. Compare both outputs for consistency, not just average quality. If the system is stochastic, require the agency to explain the source of variation and show how production settings constrain it. Repeatability matters particularly when the output triggers an external action.
Remove hidden preparation
Have the agency show the complete path from the approved input to the delivered output. Ask whether a person selected documents, corrected fields, rewrote the request, rejected bad results, or edited the final answer. Those activities may be useful controls, but they must be visible in the operating model and cost.
Capture measurable results
For every case, record the expected result, system result, reviewer judgment, time taken, intervention, and reason for any correction. Separate accuracy from completeness, factuality, policy compliance, and response time. Ask the agency to provide the underlying evaluation design rather than only a headline percentage.
How can I expose failure modes during the demonstration?
Ask the agency to demonstrate what happens when the system should not proceed. Safe refusal, clarification, escalation, logging, and correction are part of the product, not embarrassing exceptions.
Missing or poor-quality data
Remove a required field, provide a corrupted attachment, or use an outdated record. The system should identify the problem, explain what is missing, and request an approved correction instead of silently guessing.
Ambiguous requests
Give two plausible interpretations or conflicting instructions. Look for a clarifying question, a stated assumption, or a handoff. A fluent answer that conceals ambiguity is a material risk.
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Use a case where the source does not contain the requested fact. Require the system to distinguish “not found” from an inferred answer and to show the source or evidence used when your workflow requires it.
Unsafe or unauthorized input
Test disallowed content, attempts to override instructions, requests for another person’s data, and actions outside the user’s authority. Verify that controls apply before a tool call or external write, not only after text is generated.
Human escalation
Ask who receives an escalated case, what information they see, how quickly they must respond, and how the decision returns to the workflow. An escalation path with no owner or service target is not an operational control.
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Logging and correction
Request a live view of input, output, model or prompt version, tool calls, reviewer changes, and final disposition. Then correct a deliberately wrong result and confirm that the correction is recorded, communicated, and prevented from recurring where appropriate.
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What components and dependencies are hidden behind the demo?
Require a component map. You need to know which parts you can configure, which are controlled by the agency, and which depend on subcontractors or external services.
| Component | Questions to ask | Why it matters |
|---|---|---|
| Model | Which model and version runs the task? What happens when it changes? Is customer data used for training? | Model changes can alter accuracy, cost, data handling, and repeatability. |
| Prompts and policies | Who writes and maintains system instructions, guardrails, and routing rules? Can you inspect or export them? | Prompt changes may materially change behavior even when the model is unchanged. |
| Retrieval sources | Which documents, indexes, permissions, and refresh schedules supply context? How are stale or conflicting sources handled? | Many apparent model errors originate in source selection or access rules. |
| Tools and integrations | What systems can the AI read or write? What authentication, rate limits, approvals, and rollback controls apply? | Tool access turns a text error into a business transaction or data change. |
| Human review | Where is review mandatory, optional, or bypassed? Who pays for it and what service level applies? | Human effort can determine both safety and the real total cost. |
| Post-processing | What rules format, filter, redact, score, or route the model output? Which subcontractor operates them? | Downstream code may introduce failures or make results difficult to reproduce. |
Request a diagram showing data flows and trust boundaries. Ask the agency to identify every configurable setting and every dependency you cannot replace without its assistance.
What evidence should an agency provide?
Ask for dated evidence that another competent person could reproduce and challenge. Marketing screenshots and unqualified averages are not enough.
| Evidence | Minimum detail to request | How to use it |
|---|---|---|
| Evaluation design | Objective, test population, sampling method, exclusions, expected answers, reviewer qualifications, and scoring rules. | Check whether the test represents your workflow and whether the scoring rewards the behavior you need. |
| Run record | Date, software and model versions, prompts, configuration, input set, interventions, and output files. | Confirm that the claimed result can be rerun and compared after changes. |
| Metrics | Accuracy or quality definition, response time, failure rate, intervention rate, cost assumptions, and uncertainty or variance. | Separate a measured result from a forecast and identify what was not measured. |
| Limitations | Known weak cases, unsupported formats, language limits, capacity constraints, and conditions that degrade performance. | Decide whether exclusions fit your risk appetite and process design. |
| Baseline comparison | The existing process, including human time, error correction, controls, and elapsed time. | Test whether the proposed gain is real rather than a comparison with an unusually poor baseline. |
| Operational evidence | Monitoring, incident handling, backup or recovery arrangements, support process, and change history from comparable deployments. | Assess whether the agency can run the service after the demonstration ends. |
Accuracy, speed, savings, and autonomy are propositions to verify, not promises to repeat. The Information Commissioner’s Office advises that, before procuring AI systems, datasets, or coding, buyers should conduct due diligence on accuracy, bias, and the trade-offs considered in design. Its accountability and governance guidance also says a procured system should align with the buyer’s chosen trade-offs. Use those principles to make the agency explain not only what worked, but what it deliberately optimized and what it sacrificed.
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Do this review before sending personal, confidential, regulated, or commercially sensitive information to the demo environment. The correct obligations depend on your jurisdiction, sector, and role in the data processing; obtain legal advice where the consequences warrant it.
Clarify roles and purpose
- Identify whether you and the agency act as controller, processor, or another legally defined role for each data flow.
- State the specific processing purpose and prohibit secondary uses, including training or product improvement, unless you expressly authorize them.
- List data categories, sensitive fields, data subjects, and any cross-border transfers.
Set retention and deletion rules
Ask where prompts, uploaded files, outputs, logs, backups, and support copies are retained. Define retention periods, deletion triggers, verification evidence, and what happens to residual copies in disaster-recovery systems. Confirm how you can retrieve or delete a record linked to a person or customer.
Check access and security controls
Request details on encryption in transit and at rest, identity and access management, least-privilege roles, administrator access, secrets handling, audit logs, vulnerability management, and incident detection. Ask how access is reviewed and revoked when staff or subcontractors change.
Map subprocessors and supply-chain risk
Obtain the current subprocessor list, locations, services, and notice process for additions or replacements. Ask which external model, hosting, analytics, support, or integration providers receive your data. A strong agency should explain how it evaluates those providers and how you can object or exit if the risk changes.
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Agree incident handling
Put notification timing, investigation cooperation, evidence preservation, customer communications, and remediation responsibilities in writing. Confirm who decides whether a regulator, customer, or affected person must be notified under the applicable law.
For an early evaluation, minimize exposure: use the least sensitive approved dataset, redact identifiers where possible, restrict access, and destroy the environment when the test ends. Do not assume a demo account has the same protections as a contracted production service.
What should the contract promise after the demo?
Attach the demo script, test description, component map, and evidence record to the agreement. The contract should convert the successful scenario into obligations that remain testable after deployment.
| Contract area | Terms to define |
|---|---|
| Scope and deliverables | Workflow boundaries, integrations, environments, documentation, training, migration work, and each milestone’s acceptance evidence. |
| Responsibilities | Who supplies data, maintains source content, approves outputs, operates integrations, handles escalations, and performs security tasks. |
| Performance KPIs | Case population, measurement method, minimum quality or accuracy, response-time target, availability, intervention rules, and review period. State what happens when a case is unmeasurable. |
| Security and privacy | Roles, purposes, locations, retention, deletion, access, encryption, audit rights, subprocessors, incident notice, and data-reuse restrictions. |
| Intellectual property | Ownership or license for prompts, workflows, evaluation sets, custom code, documentation, outputs, and improvements based on your data. |
| Portability | Export formats, access to your data and logs, configuration and prompt export, replacement assistance, and time limits for a transition. |
| Pricing assumptions | Included volumes, model or tool usage charges, human-review fees, integration and change fees, support tiers, overage rules, and taxes or regional differences. |
| Support and service levels | Support channels, response and resolution targets, maintenance windows, severity definitions, and escalation contacts. |
| Change control | Notice and approval for model, prompt, data source, subprocessor, location, or workflow changes; regression testing and re-acceptance requirements. |
| Remedies and exit | Correction plan, service credits or other remedies where appropriate, suspension rights, termination triggers, data return and deletion, transition support, and treatment of prepaid work. |
Acceptance should require the agreed test, not a subjective statement that the system “looks good.” Define whether a missed KPI triggers a cure period, additional testing, a reduced scope, or the right to terminate. Make the same standard apply when the agency replaces a model or subprocessor.
How should I compare two or more agencies?
Compare the proposals on the same evidence and operating assumptions. A lower quote can be misleading if it excludes review labor, usage charges, integration work, or exit support.
| Comparison axis | What a strong proposal shows | Questions that reveal gaps |
|---|---|---|
| Evidence and reproducibility | Dated runs, transparent test design, repeatability, and limitations. | Can we rerun the exact workflow and inspect the raw results? |
| Workflow and edge cases | Coverage of normal, difficult, and exception cases with defined escalation. | Which cases are excluded, and who handles them? |
| Privacy and security | Clear roles, locations, retention, access, subprocessors, and incident process. | What third parties receive data, and how are changes notified? |
| Explainability and portability | Traceable sources, visible interventions, exportable configuration, and usable logs. | Could another provider operate the workflow if this relationship ends? |
| Total cost and support | Complete pricing assumptions, human effort, service levels, and change fees. | What usage, review, or maintenance cost is not in the headline price? |
Use a common question set and require each bidder to label measured results, estimates, and commitments separately. Do not award a “winner” on a single score when a failure in privacy, safety, or portability is disqualifying.
Is there a universal pass/fail score for an AI agency demo?
No. A threshold suitable for drafting internal summaries may be unacceptable for a workflow that affects health, employment, finances, legal rights, or access to services. Set gates for your specific use case before reviewing the final commercial offer.
Use risk-based gates
- Safety gate: no unapproved external action, unsafe recommendation, or prohibited disclosure in the defined high-risk tests.
- Quality gate: the agreed minimum quality, factuality, completeness, or classification performance on each required case group, not merely an overall average.
- Operational gate: response time, availability, human coverage, logging, and recovery meet the process requirement.
- Data gate: roles, locations, retention, deletion, access, subprocessors, and incident duties are acceptable before production data is used.
- Commercial gate: total cost, change exposure, portability, support, and exit terms fit the approved business case.
A proposal that passes four gates but fails one non-negotiable gate should not proceed until the risk is removed, accepted by the accountable owner, or the scope is changed.
What red flags should stop or slow the signing process?
- The agency refuses to repeat the demo with your approved cases or will not disclose hidden preparation.
- Only curated successes are shown; difficult, ambiguous, or unsafe inputs are dismissed as outside scope without a written reason.
- Metrics have no test population, date, baseline, definition, or explanation of human intervention.
- The agency cannot identify the model, data sources, tools, subprocessors, or what happens when a dependency changes.
- Privacy answers are limited to a generic security statement, with no retention, deletion, location, access, or data-reuse terms.
- The price excludes usage, review, integration, support, or change costs that the workflow clearly requires.
- The contract promises “best effort” but contains no acceptance test, KPI, remedy, export right, or exit process.
- The sales team pressures you to provide sensitive production data before roles, controls, and deletion terms are approved.
One red flag may be explainable; a pattern of evasive answers is evidence about the future relationship, not just the demo.
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What is a practical pre-signature workflow?
Run the review in a fixed order so commercial enthusiasm does not outrun technical and legal checks.
- Define the job. Write users, inputs, outputs, baseline, target improvement, unacceptable errors, assumptions, and exclusions.
- Approve the test set. Select normal, difficult, and exception cases; decide who can see sensitive data and how it will be minimized.
- Run the complete workflow. Record preparation, model calls, tools, human review, timings, outputs, and corrections.
- Repeat it. Use the same instructions and inputs to test consistency and expose stochastic behavior.
- Attack the failure modes. Demonstrate missing data, ambiguity, unsupported requests, unsafe inputs, escalation, logging, and correction.
- Map the system. Identify models, prompts, retrieval, tools, integrations, humans, post-processing, configurable settings, and subcontractors.
- Request the evidence pack. Obtain evaluation design, dates, metrics, limitations, baseline comparison, and operational controls.
- Complete privacy and security review. Confirm roles, purposes, locations, retention, deletion, access, encryption, logs, incident duties, and data reuse.
- Negotiate the contract. Attach the test and evidence; define scope, KPIs, responsibilities, pricing, portability, support, change control, and exit remedies.
- Record the decision. Document which gates passed, which risks were accepted, by whom, and what must be completed before production access.
How do I govern the system after signing?
Acceptance is the start of verification, not the end. Keep a versioned record of prompts, models, retrieval sources, integrations, test cases, incidents, and KPI results. Rerun a smaller regression set after material changes and a broader evaluation on a schedule appropriate to the risk and volume.
Assign an owner for policy decisions and an owner for day-to-day operations. Review escalations, overrides, complaints, drift in input quality, unexpected cost, and differences between automated and human outcomes. Require the agency to notify you before changes to models, subprocessors, processing locations, or data use, and preserve a tested route to export data and move the workflow if the relationship ends.
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What should I ask in the final agency meeting?
- Which result from the demo is measured, and which is only an estimate?
- What is the weakest case you tested, and what did the system do?
- Where did a human intervene, and what would that intervention cost at our volume?
- What information does the system use, where is it processed, and how long is each copy retained?
- Which model, prompt, source, tool, and subprocessor versions produced the evidence?
- What changes require our approval and a new acceptance test?
- How do we retrieve our data, logs, prompts, and workflow configuration?
- What happens operationally and financially if the agreed KPI is missed?
- Who is on call for an incident, and what notification timeline applies?
- What is explicitly outside scope, and what would it cost to add?
Ask for written answers and reconcile them with the contract. If an answer cannot be made precise, treat the uncertainty as a dependency or risk to manage rather than an informal promise.
Frequently Asked Questions
Can an agency require us to reveal its exact model or prompt?
A supplier may protect trade secrets, but confidentiality does not justify a completely opaque service. You can require enough disclosure to assess dependencies, data use, version changes, auditability, and portability, with an NDA or restricted-access review for sensitive details.
Can I use real customer data in an AI demo?
Use the least sensitive approved data first, preferably redacted or synthetic records that preserve the workflow’s difficulty. If real personal data is necessary, document the lawful basis, processing roles, location, retention, access restrictions, and deletion evidence before transfer.
What should happen if the agency changes the model after acceptance?
The change should trigger advance notice, an impact assessment, and regression testing against the agreed cases. Your change-control clause should allow you to reject, delay, narrow, or terminate the change if it breaks a required KPI or creates an unacceptable data or security risk.
When is independent validation worth paying for?
It is especially valuable when errors could affect legal rights, safety, finances, employment, health, or a large population. An independent reviewer can examine sampling, bias, security, and operational assumptions without the delivery team judging its own work.
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