AI proof of concept

AI PoC Development

AI PoC development answers one question before you commit a large budget: can AI solve your specific business problem with your real data? Context Studios, an AI-native development studio in Berlin, builds a focused, demo-ready prototype, measures the results and gives you a well-founded go/no-go recommendation. Goal: results in about 2–4 weeks.

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Goal: results in about 2–4 weeksYour real data, measurable resultsWell-founded go/no-go decision

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Fixed price after scoping · proposal within 48 h

Last updated: October 1, 2026

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What is an AI proof of concept?

AI project phase

An AI proof of concept (PoC) is a focused, working prototype that tests the central hypothesis of an AI initiative with real data. Unlike a feasibility study, it answers a single question – can AI solve exactly this problem well enough? – and delivers measurable metrics for deciding on the next phase.

Duration: Typically 2–4 weeks
Deliverable: Working prototype + performance report
Data: Real customer data or representative samples
Goal: Technical feasibility + ROI validation
Next step: MVP development (on go) or pivot (on no-go)

MVP development AI feasibility study AI prototype development AI development process AI development cost

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What must a good PoC deliver?

Six quality criteria for meaningful proofs of concept

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Real data, not toy data

A PoC with dummy data proves nothing. We work with your real data — only then does it become clear whether data quality and structure are good enough for AI.

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Measurable performance metrics

Not 'looks good', but quantified results — accuracy, processing time, automation rate, error rate. These figures are the basis for your go/no-go decision.

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Demo-ready prototype

No Jupyter notebook that only developers understand. Our PoC has a simple but functional interface that you can demonstrate live to stakeholders, investors or your management.

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ROI forecast based on data

Based on the PoC results, we calculate the expected ROI. You get concrete figures instead of gut feeling — how much time and cost the AI saves.

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Architecture built to scale

PoC code is not throwaway code. We set up the architecture so that the prototype can move straight into MVP development — without starting over.

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Documented results report

You receive a complete report: technical assessment, performance metrics, identified risks, ROI forecast and a concrete recommendation with a roadmap and budget for the next phase.

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How does PoC development work?

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Kick-off and hypothesis
After a free 30-minute initial call, we define the central hypothesis in a short workshop: what exactly should the AI be able to do? Which data is available? Which metrics measure success?
Days 1–2
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Data analysis and model selection
We analyse your data for quality and suitability, compare several candidate models and choose the right approach: API-based (Claude, GPT), a RAG architecture or fine-tuning.
Days 3–7
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Prototype development
Building the working prototype: AI backend, data connection and a simple frontend. Regular progress updates give you the chance to give feedback early and correct course.
Days 7–14
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Evaluation and results presentation
Systematic performance evaluation, preparation of the results report and a live presentation to your team. Including a go/no-go recommendation and a roadmap for the next steps.
Days 14–20

Frequently asked questions about PoC development

(01)What is the difference between a PoC and an MVP?

A PoC tests whether an AI solution works technically and delivers the expected benefit – with a lean prototype and real data. An MVP, by contrast, is a production-ready product that real users work with every day. The PoC is therefore the first step: its results provide the basis for the go/no-go decision and for a reliable MVP plan.

(02)What does an AI PoC cost?

Compared with an MVP budget, a proof of concept is an inexpensive safeguard against bad investments. Costs depend on the question, the data situation and the integrations required; ongoing API costs depend on the model and volume. Fixed price after scoping, proposal within 48 hours. If you want to sharpen the idea first, start with a fixed-price workshop such as the Light Discovery for €1,500.

(03)Do I need my own data for the PoC?

Ideally yes – only real data makes a PoC meaningful, because that is the only way to see whether quality, volume and structure are sufficient for AI. If data access is not yet possible, we work with anonymised extracts, synthetic or publicly available data. The results are then less conclusive, and we point this out clearly in the results report.

(04)Can I reuse the PoC code?

Yes. We design the PoC so that it can move straight into the MVP phase: a clear API structure, modular design, versioned code and documented prompts. A PoC deliberately stays lean – topics such as scaling, permission management or monitoring are added in the MVP. You receive the exclusive rights of use to the code in accordance with section 5 of our terms.

(05)What happens if the PoC turns out negative?

A negative result is valuable too, because it prevents an expensive bad investment. We analyse the reasons – such as data quality, the scope of the task or model limitations – and show alternatives: different data, a narrower use case, another technical approach or deliberately no AI project at all. You receive this assessment in writing so you can make a well-founded internal decision.

(06)How do I convince my management with the PoC?

The PoC delivers exactly what decision-makers want to see: a running prototype for a live demo, measurable metrics such as accuracy or automation rate, a data-based ROI estimate and a concrete roadmap with the effort for the next phase. This lets you replace gut feeling with evidence and makes the investment decision easy to follow.

(07)Can a PoC be carried out remotely?

Yes, most PoCs run completely remotely. The kick-off takes place via video, we share progress regularly via Slack or email, and the results presentation happens via screen sharing. For sensitive data, we set up access within your environment or work with anonymised extracts, so your data never has to leave your company.

(08)How quickly can the PoC start?

If data is available and the task is clear, we can usually start at short notice, often just a few working days after the order. The most common delay occurs when data access is only clarified after the kick-off. That is why we agree on data sources, contacts and access rights already in the 30-minute initial call and during scoping.

Industries

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Technology stack for PoC development

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AI models (evaluated)

ClaudeGPTGeminiLlamaMistral

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Rapid prototyping

StreamlitGradioNext.jsFastAPIVercel Previews

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Data processing

LangGraphUnstructured (Parsing)PandasEmbedding-APIs

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Evaluation

ragas (RAG-Eval)Custom BenchmarksConfusion MatrixA/B-TestsHuman Eval

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PoC use cases by industry

Customer service

Can the AI correctly answer a relevant share of requests? The PoC tests with a representative sample of real customer requests and measures the achievable automation rate.

Document processing

Can the AI extract relevant data from contracts or invoices? The PoC compares the results on real documents with manual processing.

Knowledge management

PoC for RAG-based enterprise search: does the AI find the right answers in your internal documents? Tested with typical employee questions and evaluated for hit accuracy and source attribution.

Product recommendations

PoC for intelligent recommendations: can the AI suggest more relevant products than the current algorithm? An A/B comparison with your product range shows whether the click-through rate improves.

Quality control

Does the AI detect defects reliably? A PoC with images from your production measures precision and recall compared with manual inspection.

Compliance & regulation

PoC for automated compliance checks: can the AI detect regulatory issues in documents? Tested on real examples and compared with the manual compliance review.

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Example projects

Examples we can build for you

Customer service

AI-powered support agent

PoC of a support agent: validating whether the AI agent understands natural-language customer requests and automatically delivers correct answers — with measurable quality metrics.

Automated first response · Multilingual · Available 24/7

Knowledge management

RAG-based document system

PoC for a RAG document system: proof that the system can search large document collections and deliver source-based answers in seconds.

Source-based answers · Fast search · Scalable

Process automation

Workflow automation with AI agents

PoC for autonomous AI agents that automate recurring business processes – from data extraction to reporting, with a measurable comparison against the current process.

End-to-end automated · Fewer errors · Time savings

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