Software that evolves
with AI from day zero.
We don't sell GPT-powered chatbots bolted onto existing systems. We build architectures where AI is structural — fine-tuning on the client's proprietary data, RAG on real knowledge bases, autonomous agents integrated into operational processes.
- Use Copilot to write code faster
- Offer "an AI module" as an add-on service
- Add GPT-powered chatbots to existing projects
- Have an AI team separate from core engineering
- Every engineer has a dedicated AI agent for the entire project lifecycle
- Every system architecture is designed to integrate AI from day zero
- Fine-tuning and RAG on the client's proprietary data — not generic prompts
- AI embedded in analysis, development, testing, and documentation processes
- Higher-quality software with more complete documentation
- Architectures ready to evolve with new AI models
- More predictable time-to-market on complex projects
- AI knowledge that stays in the product, not just in the team
Six areas of AI excellence
We don't offer generic "AI services." We have deep, specific expertise across six areas covering the full spectrum of enterprise AI applications.
Fine-tuning on the client's proprietary data, cloud or on-premise. Supervised pipeline with performance evaluation before and after training.
Autonomous agents that orchestrate multi-step processes, call external APIs, and maintain context memory. ReAct architectures with state management and business rules.
Enterprise knowledge queryable in natural language: documents, contracts, regulations, historical data. Verifiable citations and automatic corpus updates.
Automatic extraction from documents, visual quality control, object detection and classification. Custom models trained on client data for high accuracy.
Document classification and routing, intelligent approvals, automatic report generation — AI-driven operational flows without manual intervention.
Robust integration layer with cloud, on-premise, or hybrid models: control over costs, latency, automatic fallbacks, and data protection — vendor-agnostic.
How we integrate AI into every project
This isn't a checklist: it's a way of working integrated into every phase of the project, from the first conversation to production monitoring.
We analyse business processes, data assets, and existing systems to identify intelligent automation opportunities with measurable ROI. Output: a prioritised AI roadmap.
We design the system architecture with AI components planned from the outset: vector store, inference pipeline, context management, model monitoring. No retrofitting required.
Every engineer works with a dedicated AI agent supporting requirements analysis, supervised code generation, test writing, and documentation. Higher quality, more predictable timelines.
AI model performance dashboards active in production, updates with new client data, periodic fine-tuning to maintain accuracy as the business context evolves.
AI in production, not in the lab
A22 Autobrennero: automated detection of structural defects from drone analysis
Inspecting hundreds of kilometres of infrastructure required weeks of work with specialist teams on site. We trained an AI model on proprietary datasets to detect cracks and exposed reinforcement from drone imagery — with fully on-premise GPU processing: zero data exposed to third-party cloud.
Automated financial statement analysis for due diligence platforms
Analysing acquisition targets required days of manual work and reliance on specialist providers for data reclassification. We developed an AI pipeline that automatically acquires public financial data, reclassifies it according to the 4 Italian company profiles, and generates a structured report ready for use — with no manual intervention.
AI in production systems:
what we guarantee
AI in production introduces specific risks that must be managed contractually. These are the commitments included in every AI-Native project.
Proprietary data kept secure
Client data used for training or RAG is never shared with AI providers for training their models. Architectures with on-premise or private cloud models available for sensitive data. Explicit contractual clauses on data non-reuse included in every project.
Measured performance baseline
Before go-live, we define model performance metrics (accuracy, precision, recall, latency) and include them in the acceptance criteria. No AI system goes into production without a verified benchmark.
Integrated human oversight
Every AI system includes human escalation mechanisms for low-confidence cases. AI does not make critical decisions autonomously without a supervision layer designed with the client.
Production model monitoring
Model performance monitoring dashboard active from go-live. If accuracy degrades over time (model drift), the system detects it automatically and triggers the fine-tuning process.
IP ownership of custom models
Models fine-tuned on client data and training datasets belong to the client. No provider lock-in: models can be exported and managed independently of TC Consulting.
AI behaviour documentation
Explicit documentation of how the AI system makes decisions, its limitations, unsupported use cases, and operational procedures for error handling. Essential for audits and compliance.
From a single AI module
to the complete AI-Native architecture
Every organisation starts from a different point. Together we define the collaboration model best suited to your AI maturity and objectives.
We always start with an AI Readiness Assessment to identify the highest-ROI opportunity. The project has a defined objective, a success metric, and a measured baseline before go-live — this isn't a qualitative promise, it's a contractual acceptance criterion.
AI-Native from scratch
The model that delivers the highest results: architecture designed with structural AI from the outset, every engineer with a dedicated AI agent, fine-tuned model IP owned by the client. The architecture is model-agnostic: when a better model is released, we replace it without rewriting the system.
Not generic AI consultants: engineers with models already in production across LLM, RAG, and Computer Vision. They integrate into your team, bring the specific expertise you're missing, and leave internal knowledge behind — not dependency on us.
What we're asked before getting started
Where do we start? We don't know which processes to automate yet.
This is the most common starting point. That’s why our first step is always an AI Readiness Assessment: a 60–90 minute session where we analyse your operational processes, data assets, and existing systems. The output is a prioritised AI roadmap based on expected ROI, with effort and risk estimates for each opportunity. The session is completely free and carries no commitment.
Can our company data be used to train models for others?
No. The data we use for fine-tuning or RAG is never shared with AI providers to train their foundation models. We use APIs in configurations that strictly exclude this usage (such as Azure OpenAI, AWS Bedrock, or on-premise deployments). The contract includes explicit clauses regarding data management and non-reuse. For highly sensitive data, we evaluate architectures utilizing fully on-premise models.
How do you measure the ROI of an AI project?
Every AI project begins by defining the specific business metrics to be improved: time saved per process, error reduction, increased operational speed, or cost reduction. We measure the baseline before go-live, and then measure the delta after 30, 60, and 90 days. The ROI is fully calculable and verifiable — it is not a vague, qualitative promise. If metrics are not met, we trigger an optimization process already included in the contract.
What happens if the AI model makes a mistake? Who is responsible?
No AI system has 100% accuracy, and we are completely transparent about this. That’s why every system we build includes human oversight mechanisms: low-confidence cases are escalated to a human operator, never handled autonomously by the AI. The contract specifies the expected accuracy baseline, error-handling procedures, and use cases excluded from the AI system's scope. Operational responsibility is clearly defined contractually.
Do you build chatbots? Is it different from AI-Native development?
We build AI systems that can include a conversational interface — but we do not sell standalone "GPT-powered chatbots." The difference is fundamental: a chatbot merely adds a conversational UI on top of a generic LLM, whereas an AI-Native system integrates models trained on the client's data, built with specific business logic, integrated into company systems, with production monitoring and continuous fine-tuning. The result is a system that improves over time, rather than one that just gives generic answers.
Do the AI models you develop become obsolete with new foundation models?
The architectures we design are built to be model-agnostic: the application layer is decoupled from the underlying foundation model. When a higher-performing or more cost-effective model is released, we can swap it out without rewriting the entire architecture. Fine-tuning on your proprietary data retains its value regardless of the base model — domain knowledge is what creates the competitive advantage, not the model itself.
Ready to build software
with AI embedded by design?
We begin with an AI Readiness Assessment: we analyse your processes and identify intelligent automation opportunities with measurable ROI.
Or call us on +39 0461 1975740 · Response guaranteed within 24 working hours