Is the use case valuable?
Real impact, not novelty.
Specialist capabilities
AI MVP & POC Development
Hexmon builds AI MVPs, proof-of-concepts, pilots, and production-ready AI product foundations for startups, enterprises, and teams validating AI use cases.
Overview
Hexmon helps teams turn AI ideas into working MVPs, pilots, and proof-of-concepts that prove value, test feasibility, and create a clear path to production.
Hexmon will help you validate the use case, build the first version, and prepare it for real users.
Proves
Is the data usable?
Quality, access, and coverage.
Can AI improve the workflow?
Faster, cheaper, or clearer.
Can users trust the output?
Accuracy, grounding, safety.
Can the system scale?
Latency, throughput, cost.
Can cost and security hold?
Budget and access boundaries.
Services
AI Strategy & Roadmap
From idea to phased plan.
Use-Case Discovery
Find what's worth building.
Rapid Prototyping
Working demos in days.
Custom AI Products
Built around your data and team.
RAG & LLM Integration
Grounded answers from your sources.
AI Web & Mobile Apps
Real interfaces, not chat boxes.
Pilot Deployment
Limited rollout with real users.
Evaluation & Tuning
Measure, fix, improve.
Production Scaling
Infra, observability, controls.
System architecture
Business Goal
The outcome AI must move.
Data Sources
Internal, external, live.
AI Model / LLM
Hosted or open-source.
RAG & Tools
Retrieval, tools, agents.
Backend APIs
Contracts and orchestration.
UI / Dashboard
Where users meet AI.
Monitoring
Quality, drift, usage.
Deployment
Cloud or private hosting.
How we deliver
Discover
Goals, users, constraints.
Prioritize
Pick the right use case.
Prototype
Prove the core idea.
Engineer MVP
Build the real version.
Validate
Test with real data and users.
Pilot
Limited live deployment.
Optimize
Tune accuracy, latency, cost.
Scale
Move to production.
Built for
AI startup MVPs
Investor demos
Enterprise AI pilots
Internal AI tools
AI SaaS features
Workflow automation
Private AI assistants
Frequently asked questions
How is an AI MVP different from a normal MVP?
An AI MVP has to prove two things at once — that the product is useful, and that the AI behind it is reliable enough on real data. That means evaluation, grounding, and cost controls are part of scope from day one.
How long does an AI MVP take?
A focused proof-of-concept usually lands in 2–4 weeks. A pilot-ready MVP with real users, evaluation, and a hosted backend typically runs 6–12 weeks depending on data and integration depth.
Can you use OpenAI and open-source models?
Yes. We pick the model to fit the use case — hosted models (OpenAI, Anthropic, Gemini), open-source models (Llama, Mistral, Qwen), or a hybrid setup when cost, privacy, or latency demand it.
Can the MVP be deployed privately?
Yes. AI MVPs can run on your cloud, in a private VPC, or on-prem. We handle model hosting, vector stores, and data boundaries so nothing sensitive leaves your environment.
Can an AI MVP scale into production?
Yes. We architect the MVP so the same foundations — APIs, data layer, evaluation, monitoring — extend into a production system rather than getting thrown away after the pilot.
How do you control AI costs?
Through model selection, prompt and context discipline, caching, batching, and observability on token usage. Cost is treated as a first-class metric alongside accuracy and latency.