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AI Proof of Concept & MVP Development.

Validate your AI approach before committing production budget — with a structured POC or MVP that proves the concept works, identifies the real obstacles, and gives leadership the evidence base to make a confident go/no-go decision.

Artificial Intelligence Services

The Situation — When Clients Come to Us

Organizations come to us for AI POC and MVP development when:

  • Leadership has identified a high-priority AI use case and approved exploration budget — and needs a disciplined POC process that produces credible evidence of feasibility and business value rather than a demo optimized for approval
  • A technology or business team has a promising AI idea but needs to validate the approach against real organizational data before committing the resource and time required for full implementation
  • An organization has experienced AI project failures where significant production investment was made without adequate feasibility validation — and wants a rigorous POC process that surfaces the real obstacles early, when they are still cheap to address
  • Organizations with multiple competing AI investment proposals that need a structured, evidence-based evaluation approach — using POC results rather than vendor claims or consultant presentations to make investment decisions

What We Do — Our Approach

  1. // Phase 1

    POC Scoping & Problem Definition

    We formally define the POC scope — the specific hypothesis to be tested, the success criteria that will determine the go/no-go decision, the data requirements, and the minimum viable demonstration needed to validate the approach. We establish what the POC will and will not prove, so stakeholders have calibrated expectations from the start.

  2. // Phase 2

    Data Assessment & Preparation

    We assess the data available for the POC — evaluating quantity, quality, representativeness, and the labeling or annotation requirements — and prepare the POC dataset with sufficient quality controls to produce meaningful results.

  3. // Phase 3

    POC Development

    We build the proof of concept — selecting the model approach, training on the available data, and developing the minimal demonstration that tests the core hypothesis. We use iterative development with regular client checkpoints to surface unexpected findings early.

  4. // Phase 4

    Validation & Performance Assessment

    We validate the POC against the success criteria defined in Phase 1 — measuring technical performance, assessing the gap between POC performance and production requirements, and identifying the obstacles (data, integration, governance, organizational) that must be addressed in full implementation.

  5. // Phase 5

    Go/No-Go Recommendation & Implementation Brief

    We deliver the go/no-go recommendation — with an honest assessment of POC performance, identified risks, the investment required to reach production, and the implementation brief that specifies what full implementation will require if the decision is to proceed.

What You Get — Deliverables

  • POC scope definition — hypothesis, success criteria, data requirements, and demonstration specification
  • Data assessment and POC dataset preparation
  • Working proof of concept — model trained and demonstrated against real organizational data
  • POC validation report — performance metrics against success criteria, gap analysis, and obstacle identification
  • Go/no-go recommendation with supporting evidence
  • Implementation brief — scope, resource requirements, risk assessment, and investment estimate for full production
  • MVP development (where in scope) — minimum viable product suitable for controlled user testing before full-scale deployment

Validate before you invest — with a rigorous AI POC process that produces real evidence, not polished demos.

Iknow's POC methodology is designed to surface the truth about AI feasibility quickly and cheaply, so production investment is made with confidence.

Contact Iknow