allclouds technologies

An organisation’s knowledge is scattered across documents, attachments and databases, and searching for similar words often returns fragments that are incomplete or taken out of context. The model may then miss definitions, exceptions and dependencies, or fill in the answer with information that is not in the sources. Without continuous measurement it is also hard to detect a drop in quality after a change of model, prompt or indexing method

TWIN:DESK RAG optimises queries while preserving proper names, identifiers and the documents’ terminology. It compares results from multiple sources, ranks them by relevance and adds the neighbouring fragments needed to understand the context. It controls document versions and duplicates, and local glossaries help link abbreviations to formal terms. A separate model assesses grounding, completeness and citations, and regression question sets make it possible to measure quality and improve the whole process safely

What exactly TWIN:DESK RAG does

The value of conversations, notes and documents often disappears when a single session ends. The user has to explain their goals, preferences and history again, so each new interaction with AI starts from scratch. Scattered context also makes it harder to keep work continuous and to pass on knowledge within the organisation

Digital Twin creates a personal TWIN based on the user’s profile and persistent, multi-level memory. It combines facts, preferences, history and current context drawn from conversations, documents, notes, feedback and the calendar. As a result, answers are better grounded in the user’s reality, and work can continue without reconstructing earlier arrangements. The user keeps control over the memory and can export the data, and the solution can support safe knowledge sharing within the organisation

What exactly Digital Twin does

Ad hoc prompts lead to inconsistent results, and preparing a complete instruction requires knowledge of models, sources, answer formats and security rules. When it is unclear which data the AI used, it is hard to verify the result, repeat the process and limit answers based on uncertain information

Prompt Manager turns a business goal, the model’s role, input data, sources and quality rules into a structured prompt pack compliant with the CRISPE-CDF standard. Validation detects gaps and inconsistencies, and the SOURCE_ONLY and UNKNOWN mechanisms together with human approval reduce the risk of fabrication. Results, sources, citations and the course of execution can be logged and audited. Finished packs are versioned, shared and reused, so the organisation standardises its work with AI without programming and without depending on individual users’ skills

What exactly Prompt Manager does

An answer from a single model may present only some of the possible arguments, overlook important risks or depend too heavily on the line of reasoning it adopted. In analyses, recommendations and business decisions the user then has no easy way to compare alternatives and assess which proposal best fits the specific task

Model Council sends the same question in parallel to 3–5 selected models and presents their answers in a single view. The user can compare perspectives, arguments and courses of action, and an additional evaluator model helps assess how useful the results are. You can then choose the best proposal or create a synthesis of the key conclusions. The team gains a transparent, repeatable analysis process and a sounder basis for an informed decision

What exactly Model Council does

Complex business processes involve many stages, documents, sources and tools, so carrying them out manually is time-consuming and prone to omissions. Traditional automation requires programming, and without control over the plan, permissions and course of execution it is hard to trust autonomous AI action and to verify the final result

NL Agent Creator lets you describe in natural language the goal of the process, the input data, knowledge sources, rules and the tools available. The agent prepares a plan for approval, carries out the successive stages, analyses the materials and logs the sources used and the results. It can produce summary reports, classify information and check data completeness. Once configured, a process is reusable and can be shared in line with permissions, which standardises work, reduces manual effort and preserves auditability without writing code

What exactly NL Agent Creator does