Artificial intelligence inside the enterprise development process — as a pragmatic tool, delivering practical benefit. With strict data protection and control.
Generative AI is talked about everywhere, but often without much substance. InForge Labs approaches this area from two directions: on one hand, we build AI tools into our own development process to deliver more efficiently; on the other hand, we build AI-based solutions for our clients — from classic pattern recognition and computer vision through to modern LLM-based knowledge bases and automation.
Both directions share the same principle: AI is a tool in our hands — we use it when it creates real value, and we apply it in a way that keeps control, responsibility and data protection where they belong.
Retrieval-Augmented Generation systems built from the organisation's own documents. A tool tuned to the organisation's own knowledge, with controlled data handling.
Automating repetitive operational processes with AI — customer request handling, email categorisation, decision support, with Power Automate-based process automation in MS environments. Measurable efficiency gains in back-office operations.
Computer Vision applications: document OCR, visual quality control, visual anomaly detection. Typical areas: telecom infrastructure, government records, banking document processing.
Anomaly detection, fraud prevention, predictive alerts on structured data. Typical use cases: telecom fraud, banking transaction monitoring, network performance forecasting.
Processing long documents, extracting structured information, automatic summarisation. Typical use cases: proposals, contracts, audit documentation handling.
AI solutions where sensitive data never leaves the organisation's control. Local or private cloud LLM deployment, with auditable documentation.
Integrating large language models into enterprise applications — at API level, with compliance and security considerations built in. As extensions of existing business processes.
Our AI projects always start from a concrete use case: a real business problem where AI looks like a logical fit. The typical first step is a pilot: we pick a well-bounded problem and build a working, measurable solution in 4-8 weeks. If it works, the next phase grows out of it. The learning stays in every case, while the larger project risk remains contained.
On the development side, AI tools work the same way: we apply them where they bring real time savings or quality improvement — in code generation, code reviews, documentation, test case generation. Always under senior supervision: AI produces first drafts, humans decide and take responsibility.
The control mechanisms matter as much as the AI support itself. Strong review processes and requirement tracking ensure the solution aligns with the business intent. From requirement to code, every step is traceable, every decision documented.
Data protection is the first question we ask. On every AI project we clarify: where does the data live, who can see it, and what does this mean legally. The Private AI approach is particularly important in sectors (telecom, banking, government) where data stays within the organisation's control.
A 30-minute conversation usually reveals whether there's real value in it.
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