Studio Radoni

Less time wasted on documents and repeat requests, AI that actually works in your business

We help you reply to customers faster, use your documents and price lists without hunting for them by hand, and automate repetitive work, with solutions already proven in production, not just demos.

For whom

  • SMEs that want fewer hours on email, paperwork and repeat requests, useful AI, not demos Mechanical · Healthcare · Logistics · Education · Manufacturing · Services
  • Professional firms that need answers from case files and regulations right away Legal · Notarial · Accounting · Labour consultants
  • Document-centric organizations whose archives and procedures should answer, not sit in folders Quality · Compliance · HR · Customer care

Results

What matters in production

  • 0x

    More productivity

  • −0%

    Lower operating costs

  • 0+

    Clients already served

What you get

Five concrete ways to make your business work better

You do not need to be technical: here is what changes in practice, and, if you care, what we call it. Capabilities already proven on real platforms, adapted to your context.

01 · Assistants (agents)

An assistant that replies for you, and calls you when needed

We design assistants on your site or internal channels, like the chatbot already in production on TuaGPT, that handle typical requests (hours, quotes, job status) and, when needed, break a goal into steps and recalibrate the plan.

What you get: an assistant that handles typical requests and hands off to you when a person is needed.

  • Web or internal assistants with context and human handoff
  • When needed: multi-step plans, clear state and controlled replanning (agents)
  • Safety rules: time limits, human escalation, a trail of what the AI did

As in TuaGPT: from the on-site chatbot to flows that work beyond chat alone.

Autonomous agent: planning cycle Plan Act Check Replan

02 · Smart documents (RAG)

Answers from your manuals, price lists and files, not guesses

It is not enough to “plug AI into your files”: we organise how your documents are read, searched and used in answers, aligned with how you work (a RAG system).

What you get: answers taken from your materials, with references to where they are written, not domain chatter.

  • Ingest and search tuned to your archive (manuals, price lists, case files)
  • Separate indexes for business lines or sites, when needed
  • Citations and quality checks: fewer made-up answers, more verifiable trails

As in TuaGPT: company knowledge you can retrieve, not just “remembered” by heart.

RAG system: documents, index and answer Index Answer + cites

03 · AI instructions (prompts)

AI that always does the same things the way you decided

We design AI instructions as clear work contracts, not free text left to chance: sections, rules, examples and typed variables (e.g. {ClientName}) resolved repeatably. It is the same discipline put into practice in TuaGPT with the .prompt format.

What you get: clear, repeatable, auditable behaviour, not handmade prompts that change every time.

  • Instruction contract: context, operations, rules, examples and output constraints
  • Typed variables (text, number, date, choice…) and flows that persist in the conversation
  • Sector catalogues and reusable system instructions, with ownership and releases
  • When needed, we clean up and standardise what you already use today

As in TuaGPT, public specification: tuagpt-prompt-format

Prompt TuaGPT: formato .prompt XML con variabili template.prompt <prompt version="1.0"> <metadata>…</metadata> <content> introduction · context operations · rules {ClientName} {ReleaseVersion} </content> Variable Wizard types · required · regex chat workflow Catalogs PromptCollection SystemPrompt · FN import / export

04 · Forecasts and classification (ML)

When chat is not enough: numbers, patterns and data-driven decisions

If the problem is forecasting, classifying or flagging anomalies, not just answering a question, we design machine learning pipelines integrated into your day-to-day work.

What you get: forecasts or classifications on your real data, checked before you use them in the business.

  • Data prepared properly so the model does not “learn at random”
  • Training, testing and monitoring: you know if it still works over time
  • Deployment linked to existing processes (API, batch or on-site)

Where GenAI chat alone is not enough.

Machine learning: data, training, eval and deploy Data features Train model Eval Deploy ML pipeline: from data to production service

05 · Connections (integration)

AI linked to ERP, mail or site, no copy-paste

Value appears when AI talks to your systems: customers, orders, tickets, channels and site. Here comes the secure bridge between AI and applications (as in TuaGate / TuaMCP).

What you get: AI connected to the software you already use, no retyping between windows.

  • Links to existing systems and modern channels
  • Assistants connected to knowledge bases, tickets and applications
  • Controlled access, usage limits and end-to-end visibility
  • From prototype to a stable service your team can use

As in TuaMCP: the bridge that connects TuaGPT to external applications.

Integration: TuaMCP between AI and external applications TuaGPT TuaMCP tools context connectors CRM ERP API / App

Concrete proof

Built in production

TuaGPT, TuaGate and the satellites are not slides: they are real systems where these capabilities already work, assistants, answers from documents, links to your software.

Illustration of the TuaGPT conversational and agentic platform

TuaGPT

Platform for assistants and answers from documents: production instructions and autonomous flows for real business scenarios.

Illustration of the TuaGate gateway and integration layer

TuaGate

The gate between AI and business systems: controlled access and the foundation to connect satellite services.

Illustration of TuaVoice, TuaAgent and TuaMCP satellites

Satellites

Practical extensions: TuaVoice (voice), TuaAgent (automation) and TuaMCP (link to external tools).

www.tuagpt.com

Method

From need to stable service

  1. 01

    Understand

    Goals, data, constraints and risks: what is worth automating and what is not.

  2. 02

    Design

    How the assistant, document answers and connections will work, and how you measure success.

  3. 03

    Build

    Step-by-step implementation with tests, checks and integration into real processes.

  4. 04

    Hand over

    Rules, operating notes and handover: AI your team can keep running.

Have a process to streamline or too many repeat requests?

One call is enough to see whether an assistant, answers from documents, forecasts or a link to your systems is the right lever.

Contact

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