An AI prototype or agent, tested in weeks.
A four-step method, three formats with clear scope, explicit go/no-go criteria. At the end of every sprint you know whether to keep going, change direction, or stop.
The Validation Loop
Four phases, each with a deliverable that decides whether we continue. No phase is optional. No phase is theatre.
Validate
We stress-test the AI use case against technical feasibility and business value before writing a single line of code. Sometimes the right answer is don't build, and that is a deliverable.
Build
Rapid development of the core: model, orchestration, tools, minimal interface. We focus on the minimum viable intelligence that proves the concept against real data.
Iterate
Refining model behaviour on the edge cases that emerge in the first tests. One visible increment every week, reviewed with your team.
Measure
We define the trajectory to scale with numbers: accuracy on the whole execution, latency, cost per call, adoption. No production decision without metrics.
Three scopes, fixed timelines, pricing on request.
Pick the depth that matches your decision moment.
BASIC
Technical feasibility
1 week
- ✓1 scoped use case
- ✓UX mock and feasibility report
- ✓Rapid sprint with final demo
- ✓Short deck for internal stakeholders
- Agent variant: we check whether the process really needs an agent or whether a deterministic automation is enough.
STANDARD
Interactive prototype
3-4 weeks
- ✓Full UI + backend logic
- ✓Model integration (LLM / CV)
- ✓Validation on real data
- ✓Quality and cost metrics
- Agent variant: a working agent on a real process, with at least one live integration and measurement over the whole execution.
SCALE
Pilot ready
6-8 weeks
- ✓API-first modular architecture
- ✓Advanced validation loops
- ✓Post-sprint production roadmap
- ✓Documentation and team enablement
- Agent variant: a multi-agent system with designed handoffs, guardrails, action traceability and human escalation.
- You have an AI use case with a measurable outcome.
- You need a go / no-go answer on a fixed timeline.
- You want a prototype that can become production, not a demo.
- Governance and security matter from the prototype stage.
- You have to decide whether a process should be automated or handed to an agent.
- The business problem is not yet clear.
- No internal owner has been named for the project.
- The data needed to test the hypothesis is not accessible.
- You want a demo for a meeting, not a decision tool.
Part of a chain, not a one-off vendor.
protot.ai is the fast prototyping unit of ZeroFive.AI, an ICONI.CO company. AI Rating and AI Strategy upstream measure maturity and set the route. AI Agents and AI Shift downstream take to production and govern what has passed validation.
Straight answers on our formats.
The questions we hear in discovery calls. Bring yours if it is missing.
- How long does an 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. Six to eight weeks for a pilot-ready build with production-grade architecture.
- How much does an AI prototype cost?
- Pricing depends on the format and scope. We define it together after the discovery call, in a clear proposal covering scope, timeline, deliverables and exit criteria. No hidden costs, no commitment before the proposal.
- What data do you need to start?
- A real sample of the use case data, even a small one. If real data is not yet accessible, phase one becomes a data readiness assessment: we figure out what is missing before we build anything.
- Do you only work on LLMs or on other AI models too?
- Both. LLMs for document, support and agent use cases; computer vision for quality inspection; predictive models for demand forecasting. We pick the model after validation, not before.
- What happens if the prototype works?
- The code, metrics and roadmap are yours. You can take them to production with your own team, or continue with ZeroFive.AI (AI Shift) for scaled production rollout.
- What if the prototype proves the idea does not work?
- That is the most valuable outcome. It saves you the budget of a wrong project and gives you concrete evidence on why the use case does not hold, so you can restart from a better hypothesis.
- Do the formats apply to agents too?
- Yes, same three scopes. What changes is the content of the sprint: in BASIC we check whether the use case is actually agentic, in STANDARD we build the agent on a real process, in SCALE we tackle multi-agent systems with handoffs and guardrails.
- Who takes the agent to production after the sprint?
- The code, metrics and roadmap are yours and you can proceed with your own team. If you prefer to continue with us, production rollout and ongoing governance move to ZeroFive.AI with AI Agents and AI Shift. Same team, no handoff to third parties.
Let's talk about your use case.
30 minutes, no cost, to see whether your AI idea is worth prototyping, and how fast.
Book a discovery call