What is an AI POC and how does it differ from an MVP?

An AI POC (proof of concept) is a bounded, working implementation that answers one central question: can AI perform this task well enough to justify further investment? An MVP is broader and oriented towards production; it also covers UX, scalability, security and operational handling. A POC is purposefully focused and deliberately limited: usually a handful of test cases, no front end, and no integration with production systems.

Why do so many AI POCs fail?

The most common reasons are: scope that is too broad (one POC is expected to prove everything), a missing eval (nobody can say how well it works), the wrong data (the test set is not representative of production data) and a loose stack that does not scale. These problems can almost always be identified in advance through sharp scope definition and an eval set that evolves alongside the project.

Which models and stack do you use for AI POCs?

Pragmatic and task-specific. For reasoning-intensive tasks, Claude (Anthropic) or GPT-4-class models. For cheap batch operations, GPT-4o-mini or similar. For sensitive data or high-volume use cases, self-hosted models (Llama, Mistral). Tool layer via MCP, function calling or LangChain. Eval layer via Promptfoo, RAGAS or custom pytest runners, depending on the task.

How long does an AI POC take?

Depending on complexity, two to six weeks. A simple RAG POC or classification POC with clear data and use case can be done in two to three weeks; a more complex agent flow with multiple tool calls or multi-step reasoning takes four to six weeks. If it runs longer than six weeks, there is almost always something that was scoped incorrectly in the scoping phase.

What do we get at the end of a POC?

A working implementation that runs your use case on real (anonymised) data; an eval set with test cases and metrics that make quality measurable; a tracing system that shows what the model did for each request; cost insight per run; and an architecture document describing the path to production, including security, compliance and cost considerations.

Can a POC move into production afterwards without a rebuild?

This is a deliberate design choice we make upfront. POCs we build without a production path are quick and inexpensive, but they have to be rebuilt. POCs with a production path cost 20 to 40 percent more in the POC phase, but save weeks when building the production version. During the intake we discuss which option suits your situation.

Can you also use self-hosted models?

Yes. For clients with data residency or compliance requirements, we run the entire POC process on self-hosted Llama, Mistral or Qwen models. We have experience with vLLM, Ollama and custom inference servers. Performance and cost become part of the evaluation so that we can compare fairly with API models.

What are the typical costs of an AI POC?

A typical AI POC at Appfront starts at around €15,000 and runs up to €40,000, depending on scope, data complexity and self-hosted requirements. The rate is the same as our standard development rate; the scope of the POC determines the duration. A detailed quote follows a brief intake in which we establish scope and objectives.

AI POC development Eval-driven Production path

AI POC development: from idea to working proof of concept

Most AI pitches end in a report. Appfront builds POCs that work on your own data, measured against an eval set, with an architecture that scales to production without a rebuild. For CTOs, product leads and innovation teams who want to get beyond the demo.

Discuss your POC See our approach

2-6 wkslead time

3 layersdata, model, eval

1 verdictproduction-ready or not

Open stackyour code, your choice

Why 70% of AI POCs never reach production

Research from Gartner and MIT repeatedly shows that most enterprise AI proofs of concept never make it to production. Not because AI doesn't work, but because the POC phase is set up the wrong way. We see four recurring patterns.

Scope creep

The POC started as a "chatbot for invoice questions" and ended up as "replace our entire customer service team". Model quality dropped, and it became impossible to draw a conclusion.

No evaluation set

The team reviews ten sample outputs, decides it "looks good", and stops there. At production launch, it turns out to fail dramatically on the 10,000 edge cases nobody looked at.

Data gap

The POC works beautifully on a polished test set of 50 records. Production data contains unstructured notes, old formats and anomalies: exactly what the POC model was never trained or tested on.

Stack mismatch

The POC runs in a notebook with hard-coded keys, synchronous API calls and no logging. For production, everything has to be rebuilt: orchestration, monitoring, security, scalability. In effect, you build it twice.

Three types of AI POC we develop

Not every AI question calls for the same answer. By choosing the right type of POC upfront, we avoid discovering halfway through that the approach doesn't suit the task.

Type 1

RAG POC

Retrieval-augmented generation for questions about your own documents, policies or knowledge base. Evaluation focus: faithfulness, citation accuracy, retrieval recall. Typical timeline: 2-3 weeks.

Vector store and embeddings choice
Chunking strategy and retrieval tuning
Citation validation
RAGAS evaluation as baseline

Type 2

Agent POC

An agent that plans, calls tools and acts: research flows, workflow automation or multi-step tasks. Evaluation focus: success rate per step, hallucination rate in tool calls, cost per run. Typical timeline: 4-6 weeks.

Tool design (not an API mirror)
Plan-and-execute with human-in-the-loop
Trace system and run comparison
Approval gates for high-risk actions

Type 3

Classifier POC

Classification, extraction or structured output from text (emails, tickets, contracts). Evaluation focus: precision, recall, F1 per class. Typical timeline: 2-4 weeks, often with fine-tuning or structured-output models.

Golden dataset (often 300-3,000 cases)
Pydantic/JSON schema for output
Multi-model comparison
Production monitoring with drift detection

How Appfront builds an AI POC

A fixed rhythm across four phases. No lengthy discovery loops; we work in scope-aware milestones so that at each phase you know whether to continue or adjust course.

Phase 01

Scope and evaluation design (week 1)

Together with you, we define: which specific task the AI should perform, for whom, on which data, and against which success criteria. Then we design the evaluation set: between 50 and 500 test cases covering the happy path and the most important edge cases.

Scope document with explicit boundaries
Evaluation set v1 (validated by your experts)
Definition of success per test case

Phase 02

First working version (weeks 2-3)

We build the minimal working implementation: data pipeline, model calls, output. No UI yet, no integrations: just the core. At the end of phase 2 we run the evaluation set and establish a baseline score.

Working pipeline (data → model → output)
First evaluation report with baseline
Cost-per-run estimate

Phase 03

Iteration and improvement (weeks 3-5)

Based on the baseline, we know where it breaks. Iteration targets the bottleneck: a better prompt, a different chunking strategy, another model, better tool design, extra context. Every iteration is measured against the same evaluation set, so improvement is objective.

Iteration reports with evaluation diff
Trace system live
Edge-case report

Phase 04

Production path and handover (weeks 5-6)

We build the architecture blueprint that shows what production requires: orchestration, monitoring, security, cost control, fallbacks. We deliver working code, the evaluation suite, the trace system and the architecture document.

Production architecture blueprint
Working POC codebase and tests
Evaluation suite that can run in CI
Go/no-go verdict with cost impact

From POC to production without a rebuild

The biggest cost spike in AI projects comes in the move from POC to production. With us, that transition is prepared during the POC, not after it.

Eval suite that fits into CI

The eval set we design in phase 1 doesn't just run on your laptop. We build it to run on GitHub Actions or GitLab CI, with output sent to your own dashboards. Every time a prompt, model or pipeline changes, you know straight away whether quality has improved or declined.

Separable orchestration layer

Model calls go through a thin abstraction layer that stays the same in both POC and production. Switching between Claude, GPT-4 and self-hosted models is a config change, not a rebuild. For agent flows, we use MCP so that tools can be reused across environments.

Observability from day one

Logging, tracing and cost attribution are built in from the POC onwards. When rolling out to production, we don't have to build monitoring infrastructure after the fact. From day one, you can see performance and costs per user, per use case and per model.

Security and compliance documented

Data flows, prompt injection mitigations and log retention are explicitly documented in phase 4. Audit questions during the production phase are therefore answered the same week, not months later after a second engagement.

Example POCs from our work

Anonymised examples of POCs we recently delivered, including the baseline, the improvement journey and the production outcome.

RAG · Finance

Policy Q&A for the compliance team

Question: can an AI answer questions about internal compliance documents with citations? Eval: 200 questions, assessed on faithfulness and citation accuracy. Baseline: 62% correct with direct citations. Final status week 4: 91% correct after chunk tuning and hybrid retrieval. Production: rolled out to 80 employees, around 400 questions per week.

Agent · Logistics

Invoice matching agent with approval

Question: can an agent match incoming invoices with purchase orders and involve a human when in doubt? Eval: 500 historical invoices, measured on match rate and false positives. Baseline: 71% auto-match, 12% false positives. Final status week 5: 86% auto-match, 2% false positives after tool redesign. Production: 4 months later, saves the team around 16 hours per week.

Classifier · Healthcare

Triage of incoming patient messages

Question: can a classifier route patient emails to the right department? Eval: 1,500 labelled emails, 12 classes. Baseline: 79% accuracy with GPT-4o-mini. Final status week 3: 92% accuracy with a fine-tuned smaller model that runs 8x cheaper. Production: running for 6 months, 2,000 emails per day, error margin stable.

Common mistakes and how we prevent them

Lessons from AI POCs we have delivered for a range of clients, and the patterns we build in beforehand to avoid them.

Mistake: building a POC without an eval set

Us: the eval set is phase 1, written before a single line of code

Mistake: only seeing production data in week 4

Us: data research sits in phase 1, as anomalies would otherwise block the entire POC

Mistake: one long demo at the end

Us: an interim result per phase, with go/no-go decision points built in

Mistake: only calculating costs after production rollout

Us: cost per run is part of the eval, visible at every iteration

Mistake: applying AI to a use case where it adds no value

Us: we're honest during the scoping phase: "an SQL query is better here"

Frameworks and stack we deploy

Pragmatic, depending on the use case. No vendor evangelism; instead, a list of tools we have experience with and know when they are the right fit.

Model layer

Claude (Anthropic): reasoning, long context
GPT-4o/o1 (OpenAI): agent flows, structured output
GPT-4o-mini: batch work, low-cost classifiers
Llama 3.1 / 3.3 (self-hosted): sensitive data, high volume
Mistral / Qwen: cost-efficient self-hosting

Tools and orchestration

MCP (Model Context Protocol): tool layer
OpenAI function calling / structured output
LangChain / LangGraph: agent orchestration
LlamaIndex: RAG pipelines
Pydantic-AI: typed agent output

Evals & observability

Promptfoo: prompt regression suites
RAGAS: RAG-specific metrics
Langfuse / Arize: tracing and monitoring
OpenAI Evals: standardised evals
Custom pytest runners: for specific metrics

Production layer

vLLM / TGI: high-throughput inference
Ollama: local deployment
Pinecone / Qdrant / Weaviate: vector stores
Postgres + pgvector: for existing Postgres stacks
Kubernetes deployments with Helm charts

Why choose Appfront for your AI POC

Engineering first

We build, we evaluate, we deliver working code. No consultancy decks, but a POC that your own team can take over or that we can build on towards production.

Production experience

The team has taken AI systems into production for clients in finance, healthcare, logistics and the public sector. We know the pitfalls that only become visible once real traffic arrives.

You own the code

The code is yours. We work with open frameworks and mainstream cloud tooling where that fits. You can maintain it yourself, have it developed elsewhere, or continue with us.

An honest verdict

If the POC shows that AI is not the right solution here, we will say so. It has never cost us a client; on the contrary, that is why clients come back for the next project.

Ready to test your AI POC honestly?

Describe your use case in a few sentences. We will schedule a 45-minute intake in which we assess whether an AI POC is suitable, which type would work best and what scope is realistic within your timeline.

Discuss your POC Email Appfront