AI features that ship.
Not ones that demo.
We validate your AI use case in 4 weeks: feasibility assessment, framework selection, working prototype integrated with your existing system, and a clear path to production.
The AI Feature Validation Sprint is a 4-week engagement for teams with an AI use case but no internal ML team. You get a clear feasibility answer in Week 1 and a working prototype by Week 3, integrated with your actual system, not a sandbox.
The hard questions get answered upfront: Can this use case work? Which framework fits? What will it cost at real scale? You leave with a production roadmap, not a proof-of-concept that stops there.
The process.
Define use case and success criteria, evaluate existing data and systems, framework and model selection, architecture design
Prototype development, integration with your system, prompt engineering, performance testing
User testing with real scenarios, cost and latency benchmarking at projected scale, production readiness assessment, documentation
We work across the modern AI stack daily.
OpenAI · Anthropic Claude · Google Gemini
LangChain · Google ADK · AWS Strands · MCP
RAG pipelines · Embeddings · Fine-tuning · Agentic workflows
Claude Code · Codex · Cursor
What you get.
AI mandate, no internal ML team.
You have an AI mandate and a specific use case in mind, but no internal ML team to execute it. You need a team that can move from feasibility to prototype in weeks, not months.
You want AI features without pulling your engineering team off the product roadmap. We run the sprint in parallel and hand off a working prototype with a clear path your team can follow.
Common questions about the AI Validation Sprint.
Then you find out in Week 1, not after three months of development. The feasibility assessment is the first deliverable for a reason. If the use case doesn't work in your context, we'll tell you exactly why and what the alternatives are.
We start with the simplest approach that meets your accuracy requirements. RAG works for most retrieval and question-answering use cases and requires no training data. Fine-tuning makes sense when you need specific behavior the base model doesn't have and you have sufficient quality training data.
The prototype connects to your actual system: your API endpoints, your data sources, your authentication. It's not a standalone demo. The goal is to validate that the AI feature works in your real environment, not a controlled sandbox.
It depends on usage volume, model choice, and architecture. We benchmark cost per request, infrastructure requirements, and response time during Week 4. You get actual numbers at projected scale, not estimates based on ideal conditions.
You get a production implementation roadmap your own team can execute, or you transition to Continuous Agentic Development where we productionize the validated features and extend them over time.
Ready to validate your AI use case?
Tell us what you're trying to build. We'll tell you whether it's viable and how we'd approach it.