1. Hypothesis
State what must be true for the opportunity to remain viable.
Prove Before You Scale
AI Proof of Concept Development for Real Implementation Decisions.
An AI proof of concept should answer a specific question before you commit to a larger build. PlanckCyber designs bounded prototypes that test the uncertain parts—model behavior, data, retrieval, integrations, workflow fit, evaluation and controls—then records the evidence needed for a build, revise, stop or learn decision.
The Question
“We think this AI opportunity is worth pursuing. How do we prove it before full implementation?”
Start by identifying the decision that will follow the proof. The proof is then designed around the minimum evidence required to make that decision responsibly.
Good POC Candidates
- Can retrieval answer representative questions from approved sources?
- Can a model extract the required fields from real document samples?
- Can an agent use approved tools within defined permissions?
- Can the workflow meet latency or cost constraints?
- Can users evaluate the system against a measurable baseline?
Proof Design
Test the assumptions that could change the investment decision.
2. Outcome
Define the business or technical decision the proof will support.
3. Scope
Bound the workflow, users, systems, data and prohibited actions.
4. Prototype
Build only enough software to test the material uncertainty with representative inputs.
5. Evaluation
Measure quality, failures, latency, cost, exceptions and human escalation where relevant.
6. Decision
Compare evidence with thresholds and choose build, revise, stop or learn.
Data & Integrations
Mocked dependencies can create false confidence.
Where feasible, the proof should exercise the highest-risk real dependency: authorized data, supported APIs, identity, permissions, retrieval pipelines or representative user inputs. The objective is not to recreate production infrastructure; it is to test what could invalidate the production path.
Evaluation
A demo is not an acceptance test.
Use representative cases that include normal work, edge cases, known failures and human escalation. Define thresholds before the final demonstration so the result is not judged only by appearance.
Use the LLM Evaluation TemplatePath to Production
Design the proof so useful decisions carry forward.
- Document architecture decisions and disposable components
- Identify production identity and security requirements
- Record data permissions and retention constraints
- Define monitoring and operating ownership
- Estimate implementation dependencies and change requirements
Before You Start
Confirm the pilot has a reason to exist.
The preserved PlanckCyber Go / No-Go checklist helps verify sponsor, decision, baseline, workflow boundary, evaluation and control requirements before kickoff.
Open the AI Pilot Go / No-Go ChecklistRelated Guidance
Why AI pilots fail
Understand the operating and evaluation gaps that prevent pilots from reaching production.
Read guideAI consulting
Prioritize the opportunity and define the decision before a proof begins.
Explore AI consultingCustom AI development
Move from validated proof into a production-focused application or system.
Explore custom AI developmentFAQ
Proof-of-concept questions.
What is an AI proof of concept?
An AI proof of concept is a bounded engineering effort designed to test one or more material feasibility assumptions before a larger implementation decision.
What should an AI POC prove?
It should prove the assumptions that matter to the decision—such as model behavior, retrieval quality, data access, integration feasibility, latency, cost, human controls or user acceptance—not merely that a model can generate output.
Does a successful proof of concept mean the system is production ready?
No. Production readiness also depends on architecture, security, identity, observability, data handling, operating ownership, support and representative acceptance evidence.
What happens if the proof fails?
A failed assumption can be a useful result. The proof should end in a build, revise, stop or learn decision rather than continuing as an indefinite pilot.
Start with the problem
Prove the risky part before you scale.
Tell us the opportunity and what remains uncertain. We can help define the smallest proof that can support a real implementation decision.