Validate your AI vision in weeks, not months
Test AI feasibility before committing to full-scale development. Our rapid proof-of-concept approach validates technical viability, measures real-world impact, and gives you the confidence to invest — or pivot — early.
- Rapid delivery
- Working prototype in 4-8 weeks
- Real data
- Validated on your actual use cases
- Clear ROI
- Measurable business impact metrics
Why proof of concept
De-risk AI investment before you scale
AI projects fail when assumptions aren't tested early. A well-executed PoC separates promising ideas from expensive dead ends — giving you data-driven confidence to move forward.
Validate feasibility
Confirm that AI can actually solve your problem with your data, constraints, and accuracy requirements before committing resources.
Quantify ROI
Get real performance metrics — accuracy, speed, cost savings — that justify investment and set benchmarks for production systems.
Reduce risk
Identify technical blockers, data gaps, and integration challenges early — when they're cheap to fix or pivot around.
Align stakeholders
A working demo speaks louder than slides. Build consensus across technical and business teams with tangible results.
Accelerate to production
Our PoCs are built with production in mind — the code, architecture, and learnings carry forward, not thrown away.
Discover opportunities
The PoC process often uncovers adjacent use cases and optimizations that multiply the value of your AI investment.
When to use AI PoC
The right time for a proof of concept
A PoC is essential when you're exploring new AI capabilities, facing technical uncertainty, or need stakeholder buy-in before major investment.
Typical timeline: 4-8 weeks
From kickoff to a working prototype with documented findings, performance metrics, and clear next steps.
Exploring new AI capabilities
You want to leverage LLMs, computer vision, or ML but aren't sure what's technically achievable with your data.
Uncertain data quality
Your data is messy, incomplete, or you're not sure if it contains the signals needed for AI to work effectively.
Stakeholder buy-in needed
You need to demonstrate value to executives, investors, or cross-functional teams before securing budget.
Comparing approaches
You're evaluating multiple AI solutions, vendors, or architectures and need objective performance data.
Our approach
From idea to working prototype
Our structured PoC methodology balances speed with rigor — delivering actionable insights and a foundation you can build on.
Define success
Align on scope, success criteria, and key metrics. Identify the specific question the PoC will answer.
Data & design
Assess data quality, design the technical approach, and select the right models and tools for the job.
Build & iterate
Develop the prototype in focused sprints with regular demos. Incorporate feedback and optimize performance.
Evaluate & roadmap
Document results, assess production requirements, and deliver a clear roadmap for scaling or pivoting.
Deliverables
What you'll get from a PoC engagement
More than just a demo — you receive everything needed to make informed decisions and move confidently to the next phase.
AI technologies we validate
We select the right approach for your use case — from cutting-edge LLMs to proven ML techniques.
PoC types
Tailored to your stage and goals
Whether you need a quick feasibility check or a comprehensive pilot ready for production, we scale the engagement to match.
Feasibility check
Quick validation of core assumptions. Is the AI approach viable with your data? What accuracy can we expect?
- Data assessment
- Model benchmarking
- Feasibility report
Full prototype
Working AI solution with real data integration, performance optimization, and stakeholder demo capability.
- Everything in Feasibility
- Working prototype
- Data pipeline setup
- Performance optimization
- Production roadmap
Pilot program
Production-grade pilot with real user testing, integrations, and metrics tracking for executive decision-making.
- Everything in Prototype
- System integrations
- User acceptance testing
- ROI measurement
FAQ
Common questions
Everything you need to know about AI proof of concept engagements.
A PoC validates technical feasibility — can AI solve this problem? An MVP validates market/user fit — do people want this solution? PoCs focus on proving the core AI works; MVPs add the full product experience around it. We often recommend PoC first, then MVP.
It depends on the use case. Ideally, representative samples of the data you want AI to process — documents, customer interactions, images, transactions, etc. We can work with limited data initially and assess what additional data collection might be needed. We sign NDAs and can work within your security requirements.
That's a successful PoC — you avoided a costly failed project. We'll document exactly why it didn't work (data quality, task complexity, accuracy requirements) and recommend alternatives: different AI approaches, hybrid solutions, or process changes that might make AI viable in the future.
Yes — we build PoCs with production in mind. While they may need additional hardening (error handling, security, scalability), the core architecture, models, and data pipelines are designed to evolve into production systems, not be thrown away.
At minimum: a business stakeholder who understands the problem and can validate results, and someone with access to data. Ideally also: a technical contact for system access/integration questions, and whoever will champion the project if it moves to production.