What you get
For startups: Build something people actually want.
For enterprise teams: Avoid waste, validate with evidence, unlock internal alignment.
Clear validation of your biggest assumptions
We put your riskiest product bets under the microscope. Through targeted research and fast experiments, you’ll know which assumptions hold true — and which don’t — so your team can move forward with evidence instead of guesswork.
Confidence in what to build (and what to kill)
Not every idea deserves development time. We separate signals from noise, giving you the clarity to double down on what creates value and cut features, messages, or concepts that drain resources.
Strategic inputs ready to fuel design, development, or stakeholder buy-in
Discovery outputs aren’t just reports—they’re decision tools. You’ll walk away with actionable recommendations, frameworks, and insights that give design and engineering teams a clear direction, while helping you win alignment from stakeholders or investors.
How We Work
Discovery is not a standalone phase. Our work blends research, experimentation, and strategy. These insights aren’t just interesting, they’re actionable. Each method is chosen to reduce uncertainty where it matters most-product-market fit, messaging, UX, pricing, and feature scope.
Understand
Conducting interviews, surveys, market mapping, and stakeholder sessions.
→ Map user needs, goals, blockers
Validate
Prototype testing, in-app experiments, A/B tests, fake doors, and concierge flows.
→ Test if people care, pay, engage.
Optimize
Running analytics (Google Analytics, Mixpanel), behavior tracking (HotJar, custom events), usability reviews.
→ Find where the product leaks value and fix it.
The Impact
We keep the process structured but flexible, adapting it to the specific challenges and context of your product. Each step is designed to cut uncertainty and translate findings into practical outcomes. You’ll always know what’s happening, why it matters, and how the insights connect back to your business goals. The result is a clear path forward—built on evidence, not assumptions.
01
Initial meeting
We'll ask you a series of questions about your business, products, uncertainties, assumptions, and goals. These will be translated into clear research objectives.
02
Budget & timing
Every problem can be approached using different methods. To recommend the most suitable options, we need to understand your timeframe and budget.
03
Research plan
Based on your requirements, we’ll come back to you with a custom research proposal and plan.
04
Conducting research
Depending on the method chosen, research may take anywhere from 2 days to 4 weeks. You’re welcome to observe certain sessions (e.g., focus groups, in-depth interviews, co-creation workshops).
05
Report & presentation
You'll receive a report with actionable recommendations and suggested next steps. The format will be customized to your needs. Whether that’s a simple Excel list or a polished pitch deck, we’ll make sure it fits your context.
Why Choose Nomtek?
We help companies release AI solutions faster and validate ideas sooner. Prioritizing outcomes, we propel your product to success with dedicated, goal-oriented teams of 2-30 specialists.
Mobile native products, cross-platform apps, AI solutions, and AR/VR products shipped.
Senior designers and developers supported by dedicated product managers.
Active users of a single app over the years.
Average team member experience
What You Get in a GenAI POC
Before committing to full-scale development, a GenAI PoC gives your organization a focused, low-risk way to evaluate opportunities. We design each engagement to deliver fast, actionable insights adapted to your business.
Duration: Typically 4–6 weeks
Engagement model: Time & Material or Fixed Price (depending on scope)
Functional GenAI prototype
We build a focused application customized to fit your business case, such as an assistant, retrieval tool, or content generator. The prototype runs on real data and workflows to show practical value in real-world conditions. This proof helps guide your next steps.
Multimodal LLM integration
We integrate large language models like GPT, Claude, or open-source alternatives—connected to your data and goals. The setup supports multiple input types and is adaptable to your environment, giving you flexibility without vendor lock-in.
Retrieval-Augmented Generation (RAG)
A GenAI setup combining your proprietary data with external sources. RAG uses custom embeddings and scalable vector search to retrieve relevant content. Designed for real-world deployment with modular pipelines to easily integrate with chatbots and enterprise workflows.
Light front-end interface
A simple UI lets stakeholders test and interact with the prototype. It’s designed for quick feedback, helping refine features and align the solution with business needs through real user input.
Feasibility check and ROI analysis
We deliver a summary of technical findings, business insights, and next-step recommendations. This helps your team assess feasibility, potential ROI, and whether to move forward with further development.
How a GenAI POC can help your organization
Discover how GenAI can leverage the power hidden in your organization's data.
Integrate Siloed Tools
Your organization relies on specialized tools that are powerful, but often siloed and difficult to navigate. We develop generative AI proof of concepts (POC) that show how AI can reduce that complexity through intelligent orchestration.
Improve Exising Systems
Using agentic architectures, RAG pipelines, and cross-system protocols like MCP and A2A, we design AI agents that adapt and augment your existing systems—without needing to replace them.
Unlock more value
We build working prototypes that demonstrate the potential of GenAI and RAG-based systems in helping your organization make informed decisions grounded in real data.
Our GenAI process
Our process is built on experience, precision, and a commitment to delivering results at every stage of development. We've chiseled out a process that is proven with low risk thanks to rapid iteration.
01
Discover & Ideate
We dig into your workflows, pain points, and goals. Together, we sketch out possible AI use cases, then narrow them down to the one with the clearest business upside.
02
Design the Architecture
We map user interactions, data flows, and integration points. This ensures the AI solution doesn’t just “work,” but actually fits how your teams operate.
03
Prototype & Test
We build a functional prototype using real data in a real environment. Early testing shows if the solution solves the problem and creates measurable impact.
04
Validate & Decide
We review performance against success metrics such as accuracy, efficiency gains, cost savings. You get the insight to decide: scale it up, adjust, abandon, or pivot.
05
Refine for Scale
When a use case proves its value, we harden it with monitoring, governance, and compliance so it’s production-ready and easy to maintain.
Case studies
Go beyond the obvious. Co-create with teams who value impactful experiences and products.
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Automating mortgage underwriting with multi-agent AI
Friday Harbor
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Helping candidates perform better at job interviews using AI
Monster AI
Helping students learn and understand basic math using artificial intelligence
Fibo — AI Math Tutor
Leveraging AI to build a content summarization app for better knowledge retention
taim
Creating a personalized audio listening experience with content curated by artificial intelligence
Audioburst
Considerations Before Building a Generative AI Solution
Generative AI holds great potential for many organization, but without a clear business case and a focus on measurable outcomes, it risks becoming just another tech experiment. Before you invest, make sure your AI initiative is grounded in solving a real problem, backed by a capable team, and aligned with ROI.
Why is making sure you're targeting the right problem key to GenAI development?
Many companies jump into GenAI without a clear use case, ending up with flashy tools that don’t solve a real business need. Start with a measurable pain point: whether it’s time wasted on manual tasks, lack of personalization, or customer support inefficiencies. The best GenAI use cases replace or improve a specific process.
Why is Full-Cycle, Cross-Functional Execution of your GenAI development Partner critical to success?
A good idea falls flat when there’s a gap between the concept, the tech, and the user experience. GenAI solutions need product thinking, machine learning know-how, solid backend/frontend engineering, and UX design. Look for a team that handles everything from discovery to deployment, not just prompt tweaking.
Why ROI-Driven Integration in all GenAI projects helps in long-term adoption?
GenAI is often treated as a bolt-on feature, making little impact on revenue or retention. That's why a skilled agency won’t just build a tool: they’ll help you inject AI where it moves the needle. Whether it’s sales enablement, process automation, or better decision-making, every GenAI feature should tie back to a business goal.
