AI Solution Lab
Turn AI ambition into alpha-ready software.
Liminal Produkt gives teams senior product capacity, AI-native build velocity, and disciplined execution to transform one high-potential use case into a functioning prototype, solution, or alpha app.
What We Do
We close the capacity gap between AI ambition and usable product.
Companies are under pressure to show AI progress, but the bottleneck is rarely the model. It is choosing a painful enough use case, shaping it into a workflow, and using AI coding tools to quickly build something people can actually use. We operate as a compact solution lab to get that evidence into your hands.
AI-Coded Alpha Builds
From blank page to a functioning internal app, customer-facing prototype, agentic workflow, or AI-assisted operational system, built with modern AI coding tools.
AI Tool Integration
Practical use of modern AI APIs, automation platforms, retrieval, data workflows, and human-in-the-loop review where reliability matters.
Vibe Coding, Governed
Fast AI-assisted coding guided by product judgment: usable interfaces, application logic, data capture, and core features built to prove the product thesis.
Backend Setup
Databases, API routes, deployment-ready structure, and technical documentation so your team can continue after handover.
Why Teams Use Us
A practical way to create AI product momentum without distracting your core team.
Surplus Production Capacity
Add a focused build pod when you do not have in-house product or engineering resource, or when your best people are already at capacity.
Speed to Evidence
Get to a working prototype fast enough to brief leadership, test with users, unlock budget, or decide whether the idea deserves deeper investment.
Lower-Risk AI Experimentation
Validate the workflow, model behavior, data path, and operating constraints before committing to a full internal build or production program.
Technical Momentum Without Hiring
Move now without waiting for recruitment, procurement, or a long vendor onboarding cycle. Start with a contained alpha and scale only after proof.
Owned Asset, Not Agency Lock-In
The output is yours on completion: source code, implementation notes, and a continuation path your team or future technical partner can extend.
How We Think
AI product-market fit is not found in a demo. It is found in a working workflow.
The real market gap is no longer access to AI tools. It is knowing what to build with them. We combine vibe coding speed with product discipline: convert a real business pain into a usable workflow that people trust, repeat, and can justify funding.
Evidence Over Output
A prototype is only useful if it answers a decision. We define what the alpha must prove before writing code: demand, workflow fit, data readiness, risk, or budget confidence.
Workflow First, Model Second
Most AI projects fail in the handoff between model capability and day-to-day operations. We design around the user, job-to-be-done, review loop, and system of record.
Small Surface Area
AI coding tools make speed possible, but restraint makes speed useful. We isolate the highest-friction workflow, ship the narrowest functional version, and keep scope tight enough to learn quickly.
Trust Built In
Reliability is not a later feature. From alpha, we account for data quality, permissions, cost, human validation, auditability, and failure modes.
Services
Consultancy and alpha development for teams that need evidence, not noise.
AI Opportunity Sprint
A focused consultancy engagement to find the right wedge: the AI use case with enough pain, reachable data, clear users, and a practical path to alpha evidence.
Alpha Product Development
Fixed-fee development of a functioning first version using AI coding tools when your team needs speed, lacks build capacity, or cannot pull core engineers away from the roadmap.
Product-Market Fit Validation Design
Define what to measure once the alpha is live: user pull, saved hours, improved quality, operational trust, willingness to pay, or internal budget readiness.
AI Workflow & Agent Prototypes
Use AI coding tools to design and build AI-assisted workflows for research, operations, customer support, knowledge retrieval, document processing, reporting, or decision support—fast enough to create evidence.
Backend & Data Foundations
Set up the technical spine behind the alpha, including database structure, API routes, authentication patterns, storage, logging, and deployment-ready configuration.
Handover & Continuation Roadmap
At completion, you receive the source code, implementation notes, and a clear roadmap for iteration, hiring, internal ownership, or the next production-grade build phase.
Proof of Build
Early-stage, but not unproven.
Liminal Produkt is a new venture, so the strongest proof today is direct build experience. Mentioning five zero-to-alpha solutions is worth doing—position them transparently as internal and founder-led case studies until external client work is available.
5
AI solutions built from zero to alpha
Fixed
Project fees agreed before build starts
100%
Source code transfer on completion
Alpha
Built for validation before full-scale investment
Founder-Led Case Studies
Knowledge Workflow
AI-assisted research and synthesis alpha
Built a working product flow for turning scattered inputs into structured outputs with human review points.
Operations Automation
Internal tool with backend data capture
Converted a manual process into a usable alpha with forms, records, AI-assisted processing, and admin visibility.
Customer-Facing Prototype
Interactive AI product experience
Shaped a raw concept into a testable application with interface, prompt logic, and deployment-ready structure.
Process
A fixed-scope path from idea to evidence.
01
Choose the Wedge
We identify one urgent workflow where AI can remove friction, create leverage, or compress time—not a vague transformation theme.
02
Define the Proof
We agree what the alpha must prove: user pull, time saved, quality improved, decision speed, data readiness, or budget confidence.
03
Build the Alpha
We use AI coding tools to deliver the working interface, AI integration, backend setup, data path, review loop, and deployment-ready structure within a fixed scope.
04
Transfer & Decide
You receive the source code, implementation notes, and a practical recommendation: iterate, harden for production, integrate internally, or stop.
FAQ
Practical questions before you build.
Have an AI idea worth testing?
If your team is resource-constrained, at capacity, or racing to prove an AI use case, let's scope a fixed-fee alpha you can use, evaluate, and own.