Turn a need into an AI application you can verify.

Packaged tools do not fit a business’s own processes. A project to “do AI for the company” with no concrete success criteria leaves nobody knowing when it is finished or when it counts as achieved. Money goes into tools nobody uses, data is not ready, and nobody has defined what “effective” means.

By contrast, every real problem is different: document processing needs accurate extraction; internal lookup needs answers with sources; customer care needs responses that are appropriate and timely; quality inspection needs clear error thresholds. Each problem needs different data, a different level of verification, and a different level of supervision. That is why SITECH starts from the problem — not from the tool.

• Clarify the use case: what the specific problem is, who uses it, how the expected result is measured, and which decisions will rely on the AI’s output.
• Assess the data: whether the data is sufficient, what its quality is like, how usage rights apply, and how sensitive data must be handled.
• Prototype (PoC/MVP) on real data: a small prototype to verify — not a demo on fake data.
• Verification criteria agreed in advance: before building, both sides agree when a prototype counts as “passing” — so the decision to roll out widely or stop rests on evidence.
• Connect to existing systems: the application works alongside the business’s ERP/CRM, internal software, Web/App/IoT, as security requirements dictate.
• Common application types: document processing (OCR/NLP), internal knowledge assistants (LLM & RAG), analytics & forecasting, process automation (AI Agents) — see details at AI Agents, LLM & RAG Virtual Assistant, AI Analytics & Forecasting.

ISO/IEC 42001:2023 — AI management systems. The standard sets out governance of the AI system lifecycle: requirements, design, development, verification, validation, deployment, operation, monitoring, maintenance, change, and retirement — together with impact assessment of the AI system. SITECH organizes work along this lifecycle: each stage has clear criteria before moving to the next.

ISO/IEC 23894:2023 — Guidance on AI risk management. Guidance (not a certification standard), adapting the ISO 31000 risk management principles to AI: identifying, assessing, and treating risks — bias, privacy, reliability, explainability — throughout the system lifecycle.

ISO/IEC 23053:2022 — Framework for AI systems using machine learning. A framework describing ML-based AI systems through functional blocks: data, model, deployment/integration, monitoring/governance — giving all parties a shared language for understanding the system being built.

ISO/IEC 25059:2023 — Quality model for AI systems. Quality characteristics for evaluating AI systems — the basis for prototype verification criteria: a prototype “passes” when it meets the agreed characteristics, not when the demo looks impressive.

NIST AI RMF 1.0 — AI Risk Management Framework. Four functions: Govern – Map – Measure – Manage. SITECH follows Map (understanding context, stakeholders, and the system’s failure modes) and Measure (pre-deployment testing, post-deployment monitoring) especially closely.

EU AI Act — Regulation (EU) 2024/1689. A risk-based approach: checking whether the problem involves prohibited practices (Article 5), high-risk systems (Article 6 + Annex I/III), or transparency obligations (Article 50) — so the design is right from the start, instead of being fixed later.

With deployment on a local server, data never leaves the business’s infrastructure. The AI control layer (Guardrails) keeps AI within the business’s rules; the AI gateway (Gateway) controls who may use which AI and records usage activity. AI results are verified before being used for important decisions — no decision rests on an unverified result. See how SITECH controls AI →

• Step 1 — Clarify the problem and success criteria: the specific problem, the users, the expected results, and what counts as “passing”.
• Step 2 — Assess data and usage rights: whether the data is sufficient, what its quality is like, and how sensitive data is handled.
• Step 3 — Build a small prototype on real data: a prototype to verify, not a demo.
• Step 4 — Verify against the agreed criteria: measure the prototype’s results against exactly the criteria both sides agreed from the start.
• Step 5 — Decide on the evidence: roll out widely, adjust, or stop. Stopping when the evidence does not meet the bar is also a correct decision — better than investing heavily in the wrong direction.