AI PoC & MVP Development Services

An AI Proof of Concept (PoC) is a rapid, focused technical test designed to prove that an AI idea is feasible using real or representative data.

It typically lacks a polished user interface and focuses entirely on validating the core algorithm, logic, and data quality. PoCs help stakeholders answer "Can this be built?" while minimizing upfront financial risk, usually taking 2 to 4 weeks to complete.

An AI Minimum Viable Product (MVP) is the first functional version of an AI solution intended for real users.

Unlike a PoC, an MVP includes a usable interface, core essential features, and backend integrations. It connects to live systems (like Salesforce or Azure) to test market demand and user adoption. An MVP answers "Will people actually use this?" and typically takes 6 to 10 weeks to develop and launch.

A PoC focuses strictly on technical feasibility; a Prototype focuses on user experience and design.

A PoC proves that a specific AI model can parse your data and generate accurate outputs. A Prototype demonstrates how the final product will look and feel, often using mock data rather than live logic. In short: a PoC proves the math; a prototype proves the workflow. Enterprise AI development often requires both before advancing to an MVP.

AI MVP development costs vary significantly based on data complexity, LLM selection, and integration requirements.

Simple GenAI MVPs may start around $15,000–$30,000, while complex enterprise MVPs requiring custom RAG architecture, strict security compliance, and multiple API integrations can scale beyond $75,000. All costs are strictly scoped during our Phase 1 Discovery Workshop.

A standard AI PoC takes between 2 to 4 weeks to complete, heavily dependent on your data readiness.

If your data is clean and accessible, we can rapidly test an LLM or custom model against it. If data requires heavy cleansing, structuring, or vectorization before testing, the timeline may extend slightly. We strictly time-box PoCs to ensure rapid go/no-go decisions.

You should build an AI MVP only after successfully validating technical feasibility through a PoC.

An MVP is appropriate when you need to put a working AI tool in the hands of early adopters to gather behavioral feedback, test user adoption, and demonstrate real-world ROI before committing millions to a full enterprise rollout.

Absolutely. As a Salesforce Ridge Partner, Kizzy Consulting specializes in building AI MVPs that integrate natively with Salesforce CRM, Data Cloud, and Agentforce.

Instead of building disconnected sandbox apps, we engineer MVPs that read/write directly to your CRM, ensuring your AI agents respect existing permission sets, security models, and data governance frameworks from day one.

Yes, AI Agent Development is a core service. We build autonomous and semi-autonomous AI agents capable of multi-step reasoning and tool-calling.

Utilizing frameworks like LangGraph, CrewAI, and AutoGen, we engineer agents that can access APIs, execute enterprise workflows (like updating ERP records or drafting contracts), and collaborate seamlessly with human employees.

We validate AI ROI by establishing clear, quantifiable success metrics during the Discovery Workshop.

For a PoC, metrics are technical: prediction accuracy, hallucination rate, or retrieval speed. For an MVP, metrics are operational: hours saved per workflow, reduction in support ticket resolution time, or user adoption rates. We implement telemetry (e.g., Langfuse or Datadog) to track these metrics in real time.

Scaling an MVP involves transitioning from rapid prototyping frameworks to enterprise-grade infrastructure via comprehensive AI Operations (AIOps).

We implement robust LLMOps pipelines, transition to scalable vector databases (like Pinecone), set up API gateways for load balancing and token cost control, enforce RBAC security, and ensure continuous compliance with frameworks like the EU AI Act.

For a successful AI PoC, quality trumps quantity. We need a representative sample of the exact data the AI will encounter in production.

This includes historical support tickets, sanitized financial logs, or technical manuals. It does not need to be perfectly clean; part of the PoC process is assessing your data readiness and defining the preprocessing pipelines required for the MVP.

Security is engineered into our PoCs and MVPs from day one, adhering to ISO 27001, SOC 2, HIPAA, and the NIST AI RMF.

We implement zero-trust architectures, strict API access controls, PII redaction before data hits LLMs, and prompt injection defenses. For highly regulated industries, we deploy models in private cloud environments (Azure OpenAI, AWS Bedrock) to ensure your proprietary data never trains public models.

The Model Context Protocol (MCP) is an emerging open standard that dictates how AI models securely connect to external data sources and tools.

We utilize MCP in advanced MVP development to allow AI assistants (like Claude) to securely pull context from your local databases, Slack channels, or enterprise APIs without requiring brittle, custom integration scripts for every new data source.

If your workflow is standard (e.g., generic meeting transcription), buy off-the-shelf. If it relies on proprietary data or provides a competitive advantage, you must build custom.

Our discovery services always include a "Build vs. Buy" evaluation to ensure you aren't reinventing the wheel if a viable, secure SaaS solution already exists for your specific problem.

A "failed" PoC is still a highly successful business outcome - it prevents you from wasting millions on an unviable product.

If the AI PoC shows the idea isn't feasible (due to poor data quality, LLM limitations, or extreme costs), it gives you a clear, evidence-based answer before a full budget commitment. We then pivot strategy to address the root blockers (e.g., fixing data pipelines) rather than pushing forward blindly.