AI Prototyping & Agent Studio

AI prototypes and agents
validated in days, not months.

>agent.evaluate()

We validate the use case, build the system and stress-test it on real data. At the end you have the numbers to decide whether to go to production, and with whom.

Book a discovery callSee how we work
The stack we assemble, not one we resell. No lock-in, the code is yours.
OpenAIAnthropicLangChainpgvectorSupabaseLovableVercel

Built for speed, architected for scale.

Usable from Day 1

We don't ship black boxes. Our prototypes come with functional UX that your users can actually test and break.

Embedded Team

We work as an extension of your product unit, attending standups and sharing code through transparent sprints.

Strategy + Code

We don't just build what you ask. We validate the use case, map feasibility, and define the next 12 months.

Agile Sprints

High-visibility cycles with weekly demos. You see progress in real time, allowing for rapid course correction.

API-First Foundation

Every prototype is modular. When you're ready to scale, the core architecture is ready to move into production.

No Overhead

No heavy agency layers. You connect directly with the builders making your AI vision functional.

What you get

Four concrete things by the end of the sprint.

01

Working prototype in 3-4 weeks

Not a Figma mock. A live artifact your users open, click, and stress-test on real data.

02

GO / NO-GO with evidence

Accuracy, latency, cost per call, adoption. Numbers to defend the investment, or stop it, in front of the board.

03

Architecture ready for production

API-first and modular from day one. Moving to pilot is a continuation, not a rewrite from scratch.

04

Same team from PoC to scale

The people who validated the use case take it to production with ZeroFive.AI. No handoff, no lost context.

The Validation Loop

A data-driven process that prioritizes evidence over assumptions. Every sprint produces something measurable.

Step 01

Validate

We stress-test the AI use case against technical feasibility and business value before a single line is written.

Step 02

Build

Rapid development of the core engine and UI. We focus on the minimum viable intelligence that proves the concept.

Step 03

Iterate

Refining model performance based on real-world inputs and edge cases discovered during initial testing.

Step 04

Measure

Defining the path to scale using hard metrics: accuracy, latency, cost per call, and user adoption signals.

Agents and agentic processes

An agent in a demo tells you nothing about an agent in operation.

Gartner expects more than 40% of agentic projects to be canceled by the end of 2027, driven by rising costs, unclear value and inadequate controls. The model is rarely the problem. The wrong use case, the handoffs between agents and the absence of a measure over the whole execution are.

01

First we check whether you need one

Many processes labeled as agentic are deterministic. A classic automation solves them better and costs less. We verify it in a week.

02

Then we build it on real data

A working agent on a real process, with at least one live integration and scoped permissions. No fake environments, no cleaned-up datasets.

03

Finally we stress-test it

We measure the entire execution and not just the answer: intermediate steps, tool calls, edge cases, cost per run.

How we stress-test an agentAgents and the AI Act from August 2, 2026 →
Cases

What we've built.

Client projects and products we built ourselves, with the same method: hypothesis, fast build, ship, real users.

Names and details of client projects available on a call. Three new products are in development. The first client agent cases arrive in the coming months.
Enterprise · UtilitiesClient

Electricity, gas and telco bill analysis across sites

Challenge
A large multi-site company received hundreds of bills from different providers every month, with no simple way to compare consumption, costs and anomalies across sites.
What we did
Ingestion and parsing pipeline for electricity, gas and telco bills, per-site and per-provider data normalization, dashboards for consumption, cost and anomaly alerts.
Outcome
A single view of energy and telco costs across all sites, a solid base for renegotiation and optimization.
Stack
PythonOCR + LLMPostgresNext.js
Gaming · ConsumerOur own product

whos.it, the gaming score you can share

Challenge
Stats for Roblox, Clash Royale, Clash of Clans and Brawl Stars live on separate platforms. No easy way to sum them up into a single, comparable, shareable number.
What we did
Aggregation of data from multiple sources, unified scoring engine, mobile-first experience designed for sharing. Hypothesis, build and release with the same method we use with clients.
Outcome
A live product with real users that verified in weeks whether the sharing hypothesis worked.
Stack
ReactNodePostgresGaming API
AI Quiz · ConsumerOur own product

whos.pro, multiplayer quiz generated by AI

Challenge
A multiplayer quiz usually needs a pre-built question catalog, with topic constraints, editorial cost and repetitiveness after a few games.
What we did
Real-time question generation via LLM on any topic, consistency checks on the correct answer, synchronous multiplayer engine, sessions shareable by link.
Outcome
An LLM in production with real users, where latency is a product constraint and not a slide metric.
Stack
ReactLLMRealtimeSupabase
UtilityOur own product

da.je, short links without friction

Challenge
Verifying whether a lean shortener, with click tracking and no extra layers, still stands on its own as a product.
What we did
Full service on a short domain: link creation in seconds, tracking, essential dashboard. From first line of code to live in sprint time.
Outcome
A product live and used, built and released without going through a six-month project.
Stack
ReactEdgePostgresAnalytics
Brand · EngagementClient

Interactive game for a brand campaign

Challenge
A consumer brand wanted to increase interaction time during a campaign, beyond the usual static form or landing page.
What we did
Lightweight, mobile-first game mechanic integrated with the brand's CRM for lead capture and rewards. Design, build and release in a few weeks.
Outcome
A reusable engagement channel across multiple campaigns, with qualified leads captured natively.
Stack
ReactNodePostgresCRM API
Brand · LoyaltyClient

Gamification & loyalty for on-the-ground activations

Challenge
A brand running physical activations on the territory had no structured way to recognize, reward and re-engage the people met during events.
What we did
End-to-end gamification and loyalty system: user profile, points, missions tied to physical touchpoints, reward catalog, dashboard for the field marketing team.
Outcome
Activations tracked consistently and a user base reusable for CRM and follow-up communications.
Stack
Next.jsPostgresSupabaseTwilio
EventsClient

Event management system

Challenge
Running recurring events meant juggling spreadsheets, forms and email: scattered info, error-prone workflows, little control over registrations and attendance.
What we did
Platform covering the full event lifecycle: publication, registration, ticketing, check-in, communications, post-event reporting.
Outcome
One shared tool for organizers, staff and attendees, with consistent data from signup to final report.
Stack
ReactNodePostgresStripe
Travel · EducationClient

Operations platform for study-abroad trips

Challenge
A study-abroad operator handled bookings, students, families, partner schools and suppliers across disconnected tools, with many manual steps.
What we did
Custom back-office for the entire booking process: master data, packages, payments, documents, communications with families and schools, internal ops area.
Outcome
Standardized operations, fewer booking errors, and real-time visibility on the status of every file.
Stack
Next.jsPostgresSupabaseStripe
Customer OnboardingClient

Dynamic surveys for onboarding and personalization

Challenge
Sales and product wanted to profile new customers in a smarter way, without pushing static, repetitive forms.
What we did
Dynamic survey engine with conditional logic and scoring, integrated with the CRM: questions adapt to answers and feed segmentation and experience personalization.
Outcome
Shorter, sharper onboarding, with structured data immediately usable by sales, product and marketing.
Stack
ReactNodePostgresCRM API

Who we work with.

We don't sell ourselves as a single-vertical specialist. We bring a method that works wherever the AI use case has measurable value and accessible data.

Financial ServicesHealthcareManufacturingRetailPublic SectorB2B SaaSLegal Ops
Teams we collaborate best with
Corporate innovation units·Mid-market companies in transformation·B2B product teams·IT leadership escaping the eternal-PoC loop

Engagement Formats

Choose the speed and depth that matches your innovation cycle.

BASIC

Technical Feasibility

  • ✓1 scoped concept
  • ✓UX mock + feasibility report
  • ✓1-week rapid sprint
  • ✓Internal feedback deck
On request
Book Sprint
Recommended

STANDARD

Interactive Prototype

  • Everything in Basic, plus:
  • ✓Full UI + backend logic
  • ✓Model integration (LLM / CV)
  • ✓Validation with real data
  • ✓3-4 week delivery
On request
Get Started

SCALE

Pilot Ready

  • Everything in Standard, plus:
  • ✓API-first modular architecture
  • ✓Advanced validation loops
  • ✓Post-sprint roadmap
  • ✓Team enablement & docs
On request
Talk to Us

Part of a chain, not a one-off.

protot.ai is the fast-prototyping unit of ZeroFive.AI, an ICONI.CO company. AI Rating and AI Strategy sit upstream, AI Shift sits downstream.

14 shipped
Projects completed and released
4 shipping
New prototypes and products going live
22 days
Average time from kickoff to a working system

Straight answers.

The questions we hear from founders, product teams, and enterprise innovation units before a first call.

What's the difference between a PoC, a prototype, and an MVP?
A PoC answers one narrow question: is this technically feasible? A prototype is a working artifact your users can test end-to-end. An MVP is a launchable product with real users and revenue. We build the first two so you decide whether the third is worth funding.
Why do most AI prototypes never reach production?
They're built as demos, not as foundations. When it's time to plug in real data, security, monitoring, and scale, the shortcut architecture collapses. We design the prototype API-first and modular from day one, so the path to production is a continuation, not a rewrite.
How long does a validated AI prototype take?
One week for a feasibility check with a UX mock. Three to four weeks for an interactive prototype with real model integration. Longer engagements exist, but if a use case can't be proven or disproven within a month, the scope is wrong.
When does it make sense NOT to build a prototype?
When the use case has no measurable business outcome, when the data doesn't exist or can't be accessed, or when the same problem is already solved by an off-the-shelf product. Saying stop early is part of the deliverable.
What happens if we decide not to move forward after the prototype?
The deliverable is still yours: working prototype, technical report, clear roadmap, and the evidence to justify the decision internally. A well-argued NO-GO is worth as much as a GO, it saves months of build on the wrong hypothesis.
Who owns the code, models, and data?
You do, always. We hand over the repository, service credentials, and documentation at the end of the sprint. No lock-in on proprietary tools of ours: the stack is what we assemble together, not a platform you'll keep paying us for.
How does protot.ai fit with ZeroFive.AI's AI Rating and AI Shift?
protot.ai is the fast-validation step in ZeroFive's chain. AI Rating measures your organization's readiness. protot.ai proves a specific use case works. AI Shift takes the validated prototype into production at scale. Same team, three stages.

Stop theorizing.
Start validating.

Join the product teams building real AI with clarity and evidence.

Schedule your discovery call