AI assistants with access to procedures, policies, contract documents and the organisation’s decision history. They help find answers faster, point to the source of information, and support the work of legal, sales, finance and IT teams.
An Enterprise AI assistant shortens the time needed for research, document analysis and preparing responses. A lawyer finds the relevant clause in a contract faster, a salesperson draws on client history and offer materials, and the CFO receives a synthesised summary of reports without manually searching through multiple sources.
The assistant points to the document, clause, report, decision date or file version it used to prepare its answer. This lets the user verify the basis of the answer and reduces the risk of decisions made on incomplete context.
New and distributed employees can find procedures, policies, documentation, decision history and operational materials more quickly. Enterprise AI helps shorten onboarding, reduce repetitive questions to experts, and make it easier to work with knowledge that was previously scattered across many systems.
Enterprise AI respects the user’s permissions to data sources. The assistant should only see the documents and information the user is authorised to access. Implementation includes access control aligned with data sources, usage logging, response history, and mechanisms supporting GDPR and AI Act compliance.
We design assistants for teams that work with large volumes of documents, decisions and data: legal, sales, finance, IT, HR, compliance and operations. Example scenarios include a legal assistant for contracts, policies and regulations, a sales assistant for client history and offer materials, a finance assistant for reports, analyses and forecasts, and an IT assistant for procedures, documentation and incidents.
We connect AI assistants to the sources where the company’s knowledge lives: SharePoint, Confluence, Notion, Google Drive, document repositories, knowledge bases, ERP, CRM and enterprise systems. In practice this can mean integration with SAP, Microsoft Dynamics, Salesforce, HubSpot, Microsoft 365, ServiceNow or other systems used by the organisation.
We design access control at the level of sources, documents, users and roles. The assistant should only use data that a given person is authorised to access - both in direct answers and in answers generated from source documents. We integrate the solution with Entra ID, Active Directory, SAP IDM and custom IAM systems.
The assistant can point to the document, clause, section, decision date or file version it used for its answer. We do not promise to eliminate hallucinations entirely. We design the solution to reduce their risk: by working on controlled sources, citing the basis for answers, refusal mechanisms when data is missing, and flagging answers that require verification.
Before production launch we analyse risks associated with AI use: prompt injection, data exfiltration, leakage through logs, unauthorised access to sources, vulnerabilities in integrations, uncontrolled tool use, and lack of data separation. For critical data we recommend additional red-team testing and a review aligned with the OWASP LLM Top 10.
We support classification of the AI system, preparation of compliance documentation, an AI register, oversight rules, ownership roles, usage logging and an audit trail. If the organisation needs a quick start, we can prepare a basic governance framework for Enterprise AI within 4 weeks - with further extension to specific systems, data sources and security requirements.
We start with workshops with the target department. We establish which questions arise most often, which documents are key, which sources can be considered reliable, who should have access to them, and what constraints follow from compliance.
On this basis we prepare an MVP for one department, typically within a 4-8 week horizon. During the pilot we measure answer quality, citation accuracy, user adoption, access security, and the solution’s real usefulness in day-to-day work.
Once value is confirmed we scale the solution: adding further sources, user groups and integrations. In parallel we run quality monitoring of answers, knowledge-base updates and security reviews.
Technology stack
The team’s experience in AI, process automation and enterprise systems confirms SNOK’s readiness to deliver Enterprise AI projects.
Law firm
Legal assistant with access to around 50,000 documents. The solution supports research, source citation and faster retrieval of relevant clauses within a large body of contracts, opinions and legal documents.
FMCG manufacturer
Finance assistant for the CFO and controlling team, supporting the synthesis of multi-entity reports, data comparison and preparation of management summaries.
SaaS sector company
Assistant for the sales team, supporting offer recommendations based on client history, product documentation, prior agreements and benchmarks.
Enterprise AI works on the organisation’s own knowledge: contracts, procedures, policies, reports, decision history and business data. Unlike a general-purpose chatbot, it has access control, source citation, an audit trail, integrations with company systems, and mechanisms supporting compliance.
It depends on the architecture. Several deployment models are possible: Azure OpenAI or Anthropic Claude via controlled API, private cloud, the client’s own environment, or local language models such as Llama or Mistral for the most sensitive data. In each case we analyse the requirements for security, compliance, running cost and data control.
We apply an AI Security Review covering source validation, source-aware access control, prompt-injection testing, sandboxing, limiting the tools available to the model, usage logging, and escalation scenarios to a human. For critical data we recommend additional red-team testing and a review aligned with the OWASP LLM Top 10.
An MVP for one department, for example a legal or finance assistant, usually fits within a 4-8 week horizon. A full organisational rollout, covering multiple data sources, integrations, compliance and broader user groups, typically takes 4-6 months.
