Let's talk about your project
Tell us the use case, the data you have, and the accuracy you need. We'll scope the PoC and give you a fixed cost and a defined success criterion.
AI vendor promising transformative results but no way to verify before committing budget?
Board or executive team asking for proof that AI will work before approving the full project?
AI PoC Development
Most AI projects fail not because the technology doesn't work -- but because nobody proved it would work for their specific data and use case before committing to full development.
An AI proof of concept tests the core assumption: can AI do this task, on this data, at this accuracy level, within this cost? A focused PoC answers that question in 4--8 weeks, before you spend $100,000+ on a system that might not deliver.
AI proof of concept in 4--8 weeks with defined success criteria and measurable outcomes
Works with your actual data -- not synthetic test data that doesn't reflect production reality
Clear go/no-go recommendation with cost and timeline estimate for full development
20+ AI systems shipped -- we know what signals indicate a PoC worth building out
RaftLabs builds AI proof of concepts (PoCs) in 4--8 weeks to validate whether AI can solve a specific business problem before committing to full development. An AI PoC tests the core assumption -- accuracy targets, data requirements, and integration feasibility -- on real data with defined success criteria. We've shipped 20+ AI systems and use PoC results to provide honest go/no-go recommendations with full development cost and timeline estimates. AI PoC projects typically run $8,000--$25,000 at fixed cost.
Prove it works before you build it
Every AI project starts with an assumption: that AI can solve this specific problem, on this specific data, at an accuracy level that actually helps your business. That assumption is often wrong -- and finding out after 6 months and $150,000 of development is the expensive way to learn.
An AI PoC tests the assumption early, cheaply, and with real data.
What we build in an AI PoC
LLM and RAG PoCs
Test whether a language model approach -- document Q&A, knowledge retrieval, content generation, or data extraction -- works on your specific documents and data. Evaluate accuracy, hallucination rate, and cost per query against a representative sample of real-world inputs before committing to full RAG pipeline development.
Computer vision PoCs
Train a vision model on a sample of your labelled images and measure performance against your accuracy requirements. Defect detection, document classification, OCR, and object detection -- tested on your actual images, not generic datasets. Establishes whether the data volume and quality supports a production-grade system.
Predictive analytics PoCs
Test whether your historical data contains sufficient signal to predict the outcome you care about. Demand forecasting, churn prediction, anomaly detection -- evaluated against your actual prediction task with the performance metric that matters for your business decision. Includes feature importance analysis and data quality assessment.
AI agent PoCs
Prototype an AI agent that can execute a multi-step workflow -- document processing, data enrichment, automated research, or decision routing. Test the reliability of tool use, the accuracy of reasoning steps, and the error rate in real workflow scenarios before investing in production agent infrastructure.
Data assessment and feasibility
Before building anything, assess whether your data is sufficient for the AI approach you're considering. Quality analysis, volume assessment, labelling gap identification, and realistic accuracy range estimation based on what's actually in your data. Prevents starting a PoC that can't succeed with the current data state.
Multi-approach comparison
Test two or three different AI approaches against the same problem -- fine-tuned model vs. RAG vs. prompt engineering, or different model families -- and compare performance, cost, and maintainability before committing to one direction. Useful when the right architecture isn't clear and the cost of choosing wrong is high.
AI PoC in 4--8 weeks. Go/no-go verdict before you commit to full development.
Fixed cost. Real data. Defined success criteria. Clear recommendation.
How we run AI PoCs
Success criteria first
Before any development, we agree on the specific metrics that define success for your use case -- the accuracy threshold, error rate tolerance, and business performance requirement. We build against these criteria, not generic benchmarks. At the end of the PoC, you get a clear verdict measured against them, not a subjective assessment.
Real data, not synthetic
PoCs fail when they're tested on clean, synthetic data that doesn't reflect production reality. We work with your actual data -- your documents, your images, your historical records -- so the PoC performance predicts production performance. If your data is messy, we test on messy data and tell you what data cleaning is needed.
Honest go/no-go verdict
At the end of the PoC, we tell you whether the approach works -- and if it doesn't, why not and what would need to change. We've had PoCs where the data wasn't sufficient, the accuracy ceiling was too low, or the inference cost was too high to be viable. That's a successful PoC -- you learned what you needed to know before spending the budget.
Full development estimate included
A PoC that recommends proceeding includes a full development scope, cost estimate, and timeline. You move from PoC to full development with a clear plan -- not another round of scoping and negotiation. If you worked well together on the PoC, you already know how we work and what to expect.
Most AI projects that fail skipped the PoC
4--8 weeks and $8,000--$25,000 to know if your AI project is worth building. Before the $100,000+ commitment.
Related services
Custom AI Development -- full AI system development after PoC validation
Generative AI Development -- LLM-powered product development
Computer Vision Development -- production vision AI systems
Predictive Analytics -- production forecasting and risk models
RAG Pipeline Development -- knowledge retrieval system development
Frequently asked questions
An AI PoC is a time-boxed development sprint that tests whether a specific AI approach can solve your business problem at acceptable accuracy and cost -- before committing to full system development. A PoC validates: (1) Technical feasibility -- can the AI approach work on your data type and quality? (2) Performance targets -- what accuracy level is achievable, and does it meet your business requirement? (3) Data sufficiency -- is there enough labelled or training data, or does data collection need to be part of the project? (4) Cost of inference -- what will it cost to run the AI system at your transaction volume? (5) Integration complexity -- how difficult is it to integrate the AI with your existing systems? A PoC does not build a production system -- it builds the minimum version needed to answer these questions.
Data requirements depend on the AI type. For LLM-powered PoCs (RAG, chatbots, document Q&A), we need a sample of your knowledge base, documents, or product data -- typically 50--500 documents. For computer vision PoCs, we need labelled images of the specific problem -- typically 200--1,000 labelled images per class to establish whether a full-scale model is feasible. For predictive analytics PoCs, we need 6--24 months of historical data with the outcome you're predicting. If you don't have labelled data, data preparation can be scoped as part of the PoC. We assess your data during the initial scoping call and tell you honestly whether it's sufficient.
Before starting the PoC, we agree on the specific metrics that determine success -- not generic AI benchmarks but metrics that reflect your business requirement. For a document extraction PoC, that might be 95% field extraction accuracy on a set of 100 real documents. For a classification PoC, that might be 85% precision and 80% recall on your specific categories. For a predictive model PoC, that might be a 20% improvement in prediction accuracy over your current approach. Success criteria are agreed before development starts. After the PoC, we measure against them and give you a clear verdict: the approach meets the threshold and is worth building out, or it doesn't and here's why.
A focused AI PoC -- one use case, one AI approach, tested against defined success criteria -- typically runs $8,000--$25,000. More complex PoCs involving multiple AI approaches, significant data preparation, or integration with existing systems run higher. PoC cost depends on the AI type (vision PoCs require more infrastructure than LLM PoCs), data preparation required, and the number of iterations needed. We quote a fixed cost before starting and provide a full development cost and timeline estimate at the end of the PoC as part of the deliverable.