Start with a decision, not a technology
A useful POC answers a decision the business needs to make. Should we automate this document classification step? Can an assistant find the right policy passage for a support team? Can a CAD workflow generate a first-pass LSP or .NET automation result that a designer can review? Those questions are specific enough to test.
A request such as "put AI across the business" is not a two-week POC. Neither is a request to replace a complete process, connect every system, or train an entire organization. The first scope should name one user, one workflow, one input set, and one observable output.
UTS approaches this as AI consulting and AI strategy work before it becomes implementation. AI training can help a team understand the options, but training does not replace a bounded experiment with real examples.
- User: who will use or review the result?
- Workflow: which repeatable step is being tested?
- Inputs: which files, records, prompts, or drawings are in scope?
- Output: what will the POC produce, and where will a person inspect it?
- Decision: what will the team decide if the test passes or fails?
Check the prerequisites before promising a timeline
One to two weeks is a delivery target for a narrow experiment, not a universal guarantee. The clock can start when the prerequisites are available. If the team is still searching for data, waiting for an owner, or deciding what success means, that is discovery time rather than build time.
The minimum practical checklist is a named business sponsor, a technical contact, representative sample data, permission to use that data, a test environment or safe working boundary, and a reviewer who knows what a good answer looks like. The POC also needs an explicit out-of-scope list.
Data quality does not need to be perfect. It does need to resemble the work the POC is meant to test. A polished sample that omits the awkward files, missing fields, or ambiguous cases can make the result look better than the real workflow.
- Name the sponsor and the person who can approve the scope.
- Collect a representative, authorized sample rather than a hand-picked demo set.
- Confirm where the sample may be stored and which users may access it.
- Write five to ten acceptance examples, including at least one failure case.
- Reserve review time during the POC, not only at the end.
A useful readiness test
If the team cannot provide a representative input and explain what a correct output looks like, the next step is usually discovery. Tightening that definition is more valuable than starting a rushed build.
Pick a use case that fits a short experiment
Short POCs work best when the task has bounded inputs, a repeatable pattern, and a human review point. Document extraction, internal question answering over a defined set of material, intake classification, and a first-pass automation script can fit. A task that requires broad organizational judgment, a large migration, or many external dependencies usually needs a longer discovery phase.
The first use case should expose the largest uncertainty. If the concern is whether the model can extract fields from real forms, test extraction. If the concern is whether employees can find an answer in a controlled knowledge set, test retrieval and citations. If the concern is whether an AutoCAD workflow can reduce repetitive work, test one drawing family and one reviewable output, not every possible drawing.
Avoid choosing a use case only because it sounds impressive. A small workflow with a clear baseline teaches the team more than a broad showcase that cannot be evaluated.
- Good fit: one workflow, few input types, clear review criteria.
- Possible fit: a narrow use of an existing UTS product such as AccessIQ or CaptivaHQ, if the relevant content and access are ready.
- Poor fit for two weeks: a full enterprise rollout, open-ended autonomous decisions, or an integration program with several unknown dependencies.
Define the smallest working result
A working POC should be something a reviewer can run against the agreed sample and inspect. It may be a small web interface, a command-line workflow, a report, a structured export, or a controlled demonstration inside an existing UTS product. The format matters less than repeatability and honest evaluation.
Write the output contract before building. State the required fields, acceptable citation or source behavior, human approval step, expected response format, and what happens when the system is uncertain. If the system should abstain, say so. A POC that admits uncertainty is more useful than one that always returns a confident answer.
Keep integrations narrow. A file upload or controlled input may be enough to test the core behavior. Production identity, monitoring, billing, retention, failover, accessibility review, and full workflow integration belong in a later production plan unless one of them is the specific hypothesis being tested.
- Day 1: confirm the use case, inputs, output contract, security boundary, and acceptance examples.
- Days 2–3: prepare the sample, implement the smallest path, and record a baseline where practical.
- Days 4–7: run the POC, inspect failures, and adjust the prompt, retrieval, rules, or transformation logic.
- Days 8–10: evaluate against the agreed examples, document limits, and present the production decision options.
Measure it with examples, not a demo script
A demo script shows the path that was prepared. An evaluation set shows whether the path holds up. Use representative examples and record the output, reviewer decision, failure reason, and any manual correction. The goal is not to manufacture a success rate. The goal is to learn whether the use case deserves more engineering.
Useful measures depend on the task. They can include field-level accuracy for extraction, citation correctness for a knowledge assistant, routing agreement for intake, time saved per reviewed item, or the percentage of cases that require a human correction. Set a threshold before looking at the results where possible.
A POC can succeed by disproving the idea early. If the output is unreliable on the real sample, the team can change the use case, improve the data, or stop before paying for a production build.
Be explicit about what the free POC is not
A scoped free working POC is a way to evaluate a specific idea. It is not full production. It does not automatically include a production security review, formal compliance sign-off, complete integration, load testing, uptime commitments, ongoing support, or a finished training program. Those are separate decisions that depend on the intended deployment.
The POC should leave behind a short record: the hypothesis, sample and permissions, approach, evaluation results, known failure modes, and a recommendation. If the team proceeds, that record becomes input to a production scope. If it does not proceed, the organization still has a clear answer instead of a vague impression from a vendor demo.
UTS provides rapid prototyping and proof-of-concept work as part of its consulting approach. The next step after a successful POC may include architecture, integration, security controls, user training, or an AI strategy roadmap. It should not be described as production until those requirements have been addressed.
Keep security work inside its authorization boundary
AI POCs sometimes touch sensitive documents, internal applications, or security workflows. Access must be limited to the approved data and environment. Use least privilege, remove unnecessary personal information from samples, record who can view the results, and define retention and deletion expectations before sharing files.
Penetration testing and ethical hacking are separate security engagements, not an implied feature of an AI POC. UTS should only test systems with written authorization, a named scope, approved methods, a schedule, and an emergency contact. Do not scan or exploit production targets just because they were mentioned during an AI discovery call. A POC may document a security question or build a safe test harness, but it must not cross the agreed boundary.
Authorization comes first
No written permission, no penetration test. Keep AI experimentation, security assessment, and production access as separate scopes unless the owner has explicitly approved the combined work.
Connect the POC to the next decision
At the end of the one-to-two-week window, the team should be able to choose among a few concrete paths: stop because the hypothesis failed, run a better-scoped experiment, improve the data and repeat, or define a production phase. The recommendation should name the remaining risks and the work required to address them.
Relevant next steps may include AI advisory and strategy, AI training for the people who will use or govern the system, software architecture review, or a production implementation. For an automation workflow, the next question may be whether the team needs AutoCAD .NET/LSP automation, a custom API, or an existing product surface such as AccessIQ or CaptivaHQ. UTS also owns C3POperator and UTS Chat; those products should only be discussed as part of a specific, verified scope rather than as a promise that every POC includes them.
The strongest POC is modest in size and precise about what it proves. If the use case, data, access, and reviewer are ready, one to two weeks can be enough to produce a working result that earns a serious production conversation. It still takes a separate production plan to turn that result into a dependable service.