Bring us a process. In 2 weeks we'll find out together if it's genuinely ready for AI, which solution is needed, and what value it can deliver.
We start from a single high-impact process. We analyze real data and workflows, measure the baseline, and define a concrete AI solution with KPIs, architecture, risk assessment, and an implementation roadmap.
Duration
2 weeks, from request to recommendation
What you get
An executive report with roadmap, KPIs and a Go/No-Go decision
Indicative investment
From €20,000, scoped to the process after an initial conversation
Best fit for
A single high-impact process, not a generic area
Before building an AI system, you must determine whether it is truly needed.
Many AI initiatives start with technology: an LLM, an agent, a platform, or an open-source model. We start with the process.
We analyze where manual labor is concentrated, what data is available, which decisions can be automated, and where human supervision must be maintained.
Only then do we define the most suitable technology: AI Agent, Document Intelligence, Enterprise Search, custom automation, or no AI at all if it is not the right choice.
When it makes sense to introduce AI
Not every process benefits from artificial intelligence. Recognizing the right signals keeps you from introducing it where it isn’t needed, and helps prioritize where it actually matters.
Long information-retrieval times
People know where to look, but spend too much time finding and verifying the correct information.
Frequent errors in checks
Manual reviews that, due to volume or complexity, produce an error rate that is no longer acceptable.
Knowledge concentrated in a few experts
Critical know-how that depends on a handful of people, with real risk in case of turnover or absence.
Need for auditability
Processes where every decision must be reconstructable, justified and verifiable after the fact.
How it works
Un percorso lineare in 2 settimane, dal problema alla roadmap
- 01
Selection
One process. One problem. One measurable objective. We identify together the workflow with the highest improvement potential.
- 02
Assessment
We analyze operational flows, data, documents, systems involved, cycle times, error rates, and human intervention points.
- 03
Baseline
We measure the process before AI: turnaround times, volumes, operating costs, accuracy, and friction points.
- 04
AI Design
We define the most appropriate solution: AI Agent, Document Intelligence, Enterprise Search, RAG, automation, or no AI if it is not the right choice.
- 05
Roadmap
We deliver an actionable roadmap to move from Assessment to production safely and measurably.
Cosa ricevi
Alla fine dell'Assessment hai una decisione documentata, non una demo effimera
AI Readiness
Rigorous assessment of technical, organizational, and regulatory feasibility.
Baseline
Analytical measurement of current performance, operating costs, and processing times.
AI Architecture
Proposed architecture, models, security guardrails, and required technologies.
KPI & ROI
Concrete metrics to measure the economic and operational value generated.
Risk & Governance
Security, data privacy, AI Act compliance, audit trails, and human oversight.
Roadmap
Phased implementation plan toward production release, with realistic estimates and priorities.
The answer can also be NO.
If AI does not create sufficient value, or if a deterministic rule delivers better results with less risk, we will tell you with complete transparency.
"The goal of the Assessment is not to sell a technology. It is to determine whether investing makes business sense."
What you take home
The Assessment deliverable is a structured executive report ready to support leadership decisions:
Process Map
How the analyzed process works today: activities, roles, and bottlenecks.
Baseline
What it currently costs in person-hours, turnaround times, and rework rates.
AI Opportunity Map
The exact stages where AI can eliminate repetitive manual work.
Architecture
How the system must be built: data pipelines, models, guardrails, and integrations.
Project KPIs
How to measure the result with unambiguous metrics agreed upon before development.
Business Case
What economic and operational value automation can generate.
Risk Assessment
Analysis of security risks, AI Act compliance, and data governance.
Implementation Roadmap
Step-by-step plan to reach production without disrupting daily operations.
Go / No-Go
The final data-backed decision before committing any development budget.
Who this is for
A process takes too much time
A specific activity absorbs hours that could be spent elsewhere, and you want to know whether AI can reduce that.
You manage large volumes of documents
Files, contracts, manuals or regulations that are hard to retrieve and query quickly.
You don’t know where to start
You’ve heard about AI for months, but don’t yet have a clear, prioritized use case to start from.
You need a solid case for leadership
You need an independent assessment, with clear priorities and verified metrics, before proposing an investment.
Who we involve, on your side
- The process owner of the analyzed process
- An IT or data contact, for systems and access to sources
- A security contact or DPO, if the process handles sensitive data
How it can conclude
- Proceed with a custom system or an AI Agent, if the process needs integrations, workflows or actions
- Proceed with MyGPT, if the main need is making a document base queryable
- Prepare the process or data first, then proceed
- Consider an alternative solution, not based on AI
- Don't proceed: AI isn't the right tool for this process
The limits we state upfront
A vendor who only talks about performance and automation isn't telling you everything. We'd rather state where an AI system is not the right answer before you find out in production.
When an LLM is not worth it
Processes with stable logic and few exceptions are better solved with rules or traditional automation: cheaper, more predictable, and without the risk of hallucinations.
RAG or fine-tuning
They are not interchangeable: RAG updates knowledge without retraining the model, fine-tuning changes behavior. The choice depends on the problem, not on what's trending.
Why 100% automation is often the wrong goal
Automating every last edge case costs more than it saves: the right threshold leaves people the cases that genuinely warrant a human decision.
How hallucinations and prompt injection are governed
They aren't eliminated with a promise: they are reduced with guardrails, source verification, confidence thresholds and human escalation on uncertain cases.
What data an Assessment needs
A representative sample, not the entire archive: a few dozen real cases are enough to see whether the process is ready.
Which decisions always stay human
High-impact actions — the ones that require justification, accountability or carry a risk to the person involved — remain subject to human confirmation, by design choice, not technical limitation.
We're probably not the right partner if...
- You're looking for a generic chatbot to launch in a week, not a system integrated into your processes
- There's no internal process owner willing to follow it from analysis to release
- You're looking for the cheapest provider on the market, not the one best suited to your risk level
- The project needs to stay an isolated experiment, without integration into the systems already in use
We don't publish a price list: the quote is built on the process, the volumes and the constraints, and we tell you after the first conversation, not before. As a reference point: an AI Assessment starts at roughly €20,000. On the cost structure, here is what we can tell you: a small core team, without the layers of a large consulting firm, keeps prices competitive against those who have that structure to cover.
Quanto può valere l'automazione del tuo processo?
Inserisci i parametri operativi attuali per calcolare una stima preliminare del costo baseline e del potenziale valore recuperabile.
Parametri del Processo Attuale
Stima Preliminare Annuale
Costo Operativo Attuale del Processo
€ 480.000 / anno
Pari a circa 15.000 ore-persona all'anno
Potenziale Valore / Ore Recuperabili
€ 288.000 / anno
Circa 9.000 ore operative riallocabili ad attività a maggior valore.
⚠️ Nota: Stima preliminare basata su parametri standard. I vincoli reali, la qualità dei dati e il ROI effettivo vengono misurati e validati durante l’AI Assessment.
Have a process that could be automated?
Bring us a real process. In two weeks we will tell you what is possible, how to build it, and whether it is worth doing.
Request an AI AssessmentNot sure you're ready yet? Try the self-assessment checklist first
Frequently asked questions
What is a Futura AI Assessment?
A focused analysis of a single high-impact process, to understand whether and how artificial intelligence can create real value before any development commitment. It's not a technology demo or a pre-packaged sales pitch: if the process isn't ready for AI, or a deterministic rule would create more value with less risk, we say so just as clearly as we'd present an architecture. The Assessment takes about two weeks and focuses on a single process chosen together with the client, rather than a general survey of the organization: that narrow scope is what makes it possible to reach verifiable conclusions instead of impressions. At the end, the organization has what it needs to decide whether to move forward, at what priority and with what estimated impact, without having committed any development resources. It's the same entry point we recommend to teams that already tried an AI project internally and got stuck at the demo stage.
What does an AI Assessment deliver?
A summary document with a mapping of the current process, a feasibility assessment — whether AI is really the right answer — a high-level architecture with the systems involved, and an indicative estimate of impact and complexity, with no development commitment. The process mapping highlights workflows, the roles involved and the points where time, errors or costs pile up; the feasibility assessment weighs data quality, existing systems, regulatory constraints and security requirements; the high-level architecture indicates which components would be needed — Document Intelligence, Enterprise Search, integration with business systems — without going into implementation detail yet. The impact and complexity estimate gives the organization a realistic order of magnitude, useful for setting priorities before formalizing any investment. The document stays useful even if the organization decides not to proceed right away: it's a snapshot of the process at a precise point in time.
When does it make sense to request an AI Assessment?
When a specific process takes up too much time relative to the value it creates, you manage large volumes of documents that are hard to query, you don't yet have a clear use case to start from, or you need to bring leadership an independent assessment before proposing an investment. It's also useful when an AI project has already been attempted internally and got stuck at the demo stage: the Assessment helps identify whether the block comes from the quality of the document base, missing integration with existing systems, the lack of an internal project owner, or the absence of indicators agreed from the start. It makes less sense to request one when the process to evaluate hasn't yet been identified with enough precision: in that case it's better to narrow the scope internally first, perhaps by comparing two or three candidate processes before choosing one to analyze in detail.
How does an AI Assessment actually work?
In three phases: an initial conversation to understand context and constraints, a focused analysis of the chosen process — available data, systems involved, critical points, security requirements — and a final review with mapping, feasibility, a high-level architecture and a recommendation. The initial conversation is used to jointly pick the process to focus on and clarify non-negotiable constraints, for example around data residency or deployment. The analysis itself runs on documentation, systems and, when needed, conversations with the people who run the process daily, not only with those who oversee it. The final review isn't a generic sales pitch: it's specific to the process analyzed, states clearly whether and where AI creates measurable value, and leaves the organization to decide whether and how to proceed, with the Assessment itself carrying no further development commitment.