AI POC Development

Prove Your AI Concept
Works in 7 Days

Most AI projects fail not because the idea is bad but because the team never proved the technology works before building. An AI POC runs in 7 days, tests your core assumption against real data, and tells you exactly what to build next.

7 day delivery
Fixed price
Full source code

What This POC Proves

Eight specific technical questions answered with real data before you invest in a full build.

LLM integration: validate that GPT-4o, Claude, or Gemini can handle your specific task accurately
RAG pipeline: prove your documents can be retrieved, ranked, and answered correctly
Agent loops: confirm multi-step tool use works end to end without breaking
Classification accuracy: measure whether the model meets your quality threshold before committing
Latency and cost per request: real numbers from your actual workload, not benchmarks
PII handling: verify sensitive data can be redacted before it leaves your environment
Prompt stability: test whether outputs stay consistent across varied inputs
Fallback behavior: confirm the system degrades gracefully when the model fails or times out

Common AI POC Types

Each POC type tests a different set of AI capabilities against your actual data and requirements.

Chatbot and Conversational AI

Does the model answer your domain questions correctly? We build a working chat interface against your actual knowledge base or support history, measure accuracy, and identify where retrieval breaks down.

RAG Pipeline

Retrieval Augmented Generation requires testing chunk size, embedding model, reranking, and prompt assembly together. We run your real documents through a full RAG stack and report retrieval precision and answer quality.

Autonomous Agent

Agents that call tools, read APIs, and make decisions need to be tested against your actual system before you build the production version. We wire up the tools, run a controlled scenario, and measure completion rate.

Document Classification and Extraction

Feed the model 50 to 100 real documents from your dataset. We measure accuracy, edge case failure rate, and confidence thresholds so you know if the model is reliable enough for production use.

Build Timeline

Seven days from kickoff to a working pipeline and a written go/no go recommendation.

Day 1

Requirements and data setup

Define the exact question the POC must answer. Collect sample data. Choose the model and stack. Set the success threshold.

Days 2 to 3

Core AI pipeline build

Build the prompt chain, embedding pipeline, or agent loop. Wire up the data source. Run first end to end test.

Days 4 to 5

Evaluation and tuning

Run the pipeline against 50 to 100 real examples. Measure accuracy, latency, and cost per request. Tune prompts and retrieval.

Days 6 to 7

Report and delivery

Write the go/no go report. Package the source code. Record a walkthrough demo. Hand off with a recommended next step.

What You Get

Every AI POC ships with everything you need to decide whether to build and how to build it.

Working AI pipeline deployed to a live URL
Full source code with setup instructions
Evaluation report: accuracy, latency, and cost per request
Go/no go recommendation with reasoning
Prompt library and configuration files
Recorded demo walkthrough
Recommended architecture for the production build

AI POC Package

From $2,000

7 day delivery • Full source code • Go/no go report

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Frequently Asked Questions

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