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Feasibility study for AI

AI Feasibility Study

An AI feasibility study clarifies before the project starts whether your initiative is technically feasible, economically sensible and permissible under regulation. Context Studios, an AI-native development studio in Berlin, examines your data, tests a mini prototype and typically delivers a clear go/no-go recommendation with a roadmap after 2–3 weeks.

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Meteorological measuring mast with instruments and a verdigris copper anemometer next to a small concrete hut under an overcast sky – a visual metaphor for a feasibility assessmentAI-generated image
Clear go/no-go recommendationMini prototype on your dataGDPR & EU AI Act checkedDirectly with the founder
  1. Workshop
  2. Setup
  3. Sprint
  4. Build & Support

Fixed price after scoping · proposal within 48 h

Last updated:

(01)

What is an AI feasibility study?

AI consulting service

An AI feasibility study systematically assesses whether a planned AI project is technically feasible, economically viable and permissible under regulation. It examines five dimensions – technology and data, business case, regulation, organisational readiness and risks – and ends with a well-founded go/no-go recommendation.

Duration
Typically 2–3 weeks
Deliverable
Study report + mini prototype + go/no-go recommendation
Dimensions
Technology, business case, regulation, organisation, risk
Data analysis
Quality check of your existing data
Next step
PoC (if go) or alternative recommendation (if no-go)

AI PoC developmentAI development processAI consultingAI development costAI development timeline

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What does the study examine in detail?

Systematic analysis on every relevant level

(01)

Technical feasibility

The study examines: do suitable AI models exist for your use case? Is your data available in sufficient quantity and quality? Which architecture fits best? We evaluate concretely rather than theoretically — with a mini prototype on your data.

(02)

Economic viability

Every study includes an ROI calculation with concrete figures: development costs, running costs, expected savings or revenue growth, payback period. No optimistic guesses, but robust calculations.

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Regulatory permissibility

GDPR review, risk classification under the EU AI Act and industry-specific regulation such as the MDR. We identify regulatory hurdles early – before they become expensive during development.

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Organisational readiness

We check: does your team have the skills for AI adoption? Are there internal champions? How open is the organisation to change? This often underestimated dimension decides long-term success more than the technology does.

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Risk assessment

Systematic assessment of technical, economic and regulatory risks. Every risk receives a probability, an impact rating and a concrete mitigation strategy.

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Go/no-go recommendation with roadmap

The study does not end with vague statements but with a clear recommendation: go (with a concrete roadmap and budget), go with conditions (and what has to be done first) or no-go (with reasons and alternative approaches).

Frequently asked questions about the feasibility study

(01)What does a feasibility study cost?
The effort depends on the scope of the initiative, the data situation and the depth of the regulatory review. Compared with the later project budget, the study is an inexpensive safeguard against bad investments. We quote per project: fixed price after scoping, proposal within 48 hours. If you first want to prioritise ideas, start with a workshop such as Light Discovery for €1,500.
(02)How long does a feasibility study take?
Typically two to three weeks. Simpler assessments with a clear data situation can be completed after around ten working days. Complex regulatory reviews, for example for medical applications, usually take up to four weeks. We set the exact timeframe during scoping, depending on how quickly data access and contact persons are available.
(03)What do I receive as a result?
You receive a detailed report with a technical assessment, data quality analysis, ROI calculation, regulatory review and risk analysis. On top of that come a clear go/no-go recommendation, a concrete roadmap for the next step and a working mini prototype on your data. We present the results to your team and answer open questions directly in the meeting.
(04)Do I need a feasibility study before every AI project?
Not always. For small, clearly defined initiatives, a proof of concept can replace the study because it answers the core technical question directly. For larger budgets, sensitive data or regulatory questions, we recommend doing the study first. It prevents you from investing in a project that fails because of data, law or acceptance within the team.
(05)What happens with a no-go result?
A no-go is a valuable result because it saves you an expensive failed project. In that case we always provide an alternative: sometimes a simpler rule-based solution, sometimes a plan for which data prerequisites have to be created first. It often turns out that a smaller use case can be implemented right away and paves the way for the actual initiative.
(06)Can I use the study to secure budget internally?
Yes, that is one of the most common use cases. The study with ROI calculation, risk analysis and a working prototype gives management and budget owners a sound basis for decisions. Instead of abstract slides, you show what works with your own data, what it costs and what benefit can realistically be expected.
(07)Do I need to provide data for the study?
Ideally yes, because access to representative data enables a meaningful prototype. We work data-minimising, in secured environments and on request with pseudonymised extracts. If data access is not possible, we use synthetic data and assess feasibility based on structural and sample analyses; the findings are then somewhat less conclusive.
(08)How does a feasibility study differ from a PoC?
A feasibility study is broader: besides technology, it also examines the business case, regulation, organisation and risks. A proof of concept, by contrast, focuses on the purely technical question of whether a specific approach works with your data. The PoC often follows directly after a positive study and builds on its prototype.
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Tools for the feasibility assessment

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Model evaluation

ClaudeGPTGeminiOpen-Source (Llama/Mistral)Custom Benchmarks
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Data analysis

Python (Pandas)Great ExpectationsDVCSQL-ProfilingEmbedding-Analyse
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Prototyping

LangGraphStreamlitJupyterFastAPIVercel Previews
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Compliance review

EU AI Act Risk MatrixDSGVO-ChecklisteDPIA-FrameworkBranchen-Compliance-MapsAudit-Templates
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Feasibility studies by industry

Financial services

For financial service providers, the feasibility study covers BaFin admissibility, a preliminary regulatory assessment and risk classification under the EU AI Act — before you start developing.

Healthcare

Assess clinical data quality, check MDR requirements and document ethical implications — before the first line of code is written. Especially critical for patient-related AI applications.

Manufacturing & industry

For manufacturing and industry, we evaluate sensor data quality, check real-time requirements and calculate the ROI based on concrete production KPIs. Often the study reveals that a simpler solution is sufficient.

Retail & e-commerce

Analyse product data quality, quantify personalisation potential and define an A/B testing strategy. This is where the ROI can often be demonstrated fastest — a good argument for the investment.

Public sector

In the public sector, AI feasibility studies are often a prerequisite for budget approval. We assess along the BSI IT baseline protection, support the data protection impact assessment (DPIA) and check procurement requirements such as EVB-IT.

Energy & utilities

Assessment of sensor data from SCADA systems, analysis of real-time requirements and review of critical infrastructure (KRITIS) requirements. Feasibility studies for critical infrastructure require particular care.

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Example projects

Examples we can build for you

Customer service

AI-powered support agent

An AI agent that understands customer requests in natural language, accesses internal knowledge bases and delivers answers automatically — around the clock.

Automated first response · Multilingual · Available 24/7
Knowledge management

RAG-based document system

An intelligent knowledge system with a RAG architecture whose feasibility is checked in advance: it searches large document collections and delivers source-based answers in seconds.

Source-based answers · Fast search · Scalable
Process automation

Workflow automation with AI agents

Autonomous AI agents that automate recurring business processes — from data extraction to report generation.

End-to-end automated · Fewer errors · Time savings
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Feasibility study — consulting from Berlin

Founder AI-native since
2024
Email
info [at] contextstudios [dot] ai
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How does the feasibility study work?

  1. (01)

    Briefing and scope definition

    After a free 30-minute initial call, a half-day workshop follows via video call: we understand your business problem, define the scope of the study and identify relevant data sources and stakeholders.

    Days 1–2
  2. (02)

    Data and infrastructure analysis

    We analyse your existing data for quality, completeness and usability. In parallel we review the IT infrastructure and identify integration points and hurdles.

    Days 3–7
  3. (03)

    Mini prototype and model selection

    A compact prototype tests the central hypothesis with your real data. We evaluate 2–3 model candidates and measure performance, cost and latency concretely.

    Days 5–10
  4. (04)

    Results report and recommendation

    Presentation of the results to your team: technical assessment, ROI calculation, regulatory review, risk analysis and a clear go/no-go recommendation with a roadmap for the next step.

    Days 10–15

Safeguard your AI investment

Our study typically gives you a well-founded basis for decisions after 2–3 weeks. Discuss your initiative in a 30-minute call directly with the founder.