Company knowledge assistant
Searches procedures, manuals, contracts, and catalogues, and points to the material used. A technician can find the relevant instruction faster and verify it before acting.
Connect AI to the email, documents, and systems your business already uses
AI for business — agents, automation, and secure integrations
We design assistants, agents, and automation that work with company data. The solution can run in the cloud, on your own infrastructure, or on a server with no internet connection.
AI can answer questions, but it can also do more useful work: organise documents, search company knowledge, draft offers, classify requests, complete records, and route cases to the right people. Connected to email, a CRM, an ERP, or a custom application, it becomes part of the workflow rather than another separate tool.
We design and implement AI solutions around the way a company actually works—from a focused knowledge assistant, through document and communication automation, to agents handling multi-step tasks. You do not need to choose a model first. A description of the slow, error-prone, or repetitive process is enough to start.
What can AI do in your company?
The useful starting point is not “we need AI” but a specific job worth improving. The system may assist an employee, prepare a result for approval, or carry out selected actions within defined limits.
Documents and messages
Recognises document types, extracts fields, compares content with an order, assigns priority, and drafts a reply. Unusual cases are handed to a person.
Sales and proposals
Combines an enquiry with CRM data, prices, and the service catalogue, then identifies missing information and drafts an offer. The salesperson controls prices, terms, and sending.
Customer service
Answers routine questions from approved knowledge, checks case status, and gathers details. Complaints, price exceptions, and ambiguous situations are escalated.
Reports and summaries
Summarises documents, meetings, and tickets, highlighting missing information and recurring issues. Important conclusions can remain linked to source data.
Speech, images, and files
Transcribes calls, prepares notes, and classifies images or forms. For example, it can propose a call summary and tasks for approval before saving them to the CRM.
An AI agent is more than a chatbot
A conventional assistant answers a question. An agent can read a message, retrieve CRM data, check a document, create a task, draft a reply, and ask a person to approve sending it.
It should not receive unlimited access. The implementation defines the available data and tools, actions it may take, approval points, handling of missing information, and the audit trail. A deterministic automation is often enough for a stable rule-based workflow. AI is most useful where language, documents, images, or ambiguous requests must be understood.
Examples across different businesses
Online store or wholesaler
Normalising product attributes, drafting descriptions from approved facts, intent-based search, returns triage, and comparing delivery documents with orders.
Service company
Qualifying enquiries, finding missing details before a quote, drafting proposals, creating CRM tasks from email, and searching contracts or previous arrangements.
Manufacturing and logistics
Finding manuals, classifying reports, summarising shifts, and reading transport documents—without autonomous decisions affecting people or machine safety.
Office and administration
Extracting invoice and form data, assigning documents to cases, comparing contract versions, preparing reminders, and routing material under company rules.
Tourism and hospitality
Multilingual answers based on current offers, matching a suitable option, summarising a conversation, and handing unusual requests to staff.
Technical team
Searching documentation and incidents, drafting change notes, and assisting with code while keeping intellectual property away from a public AI service.
These are directions rather than fixed packages. The same mechanism needs a different scope in a small company, a wholesaler, and an organisation with a large ERP.
Integrations with tools you already use
AI does not have to replace current software. It can connect with a CRM, ERP, email, forms, helpdesk, online store, B2B panel, document repository, calendar, task system, or custom application.
We can integrate through an API or assess a controlled import or export when no suitable interface exists. Feasibility depends on the systems involved, and some integrations require cooperation from their vendor. See also custom CRM and ERP systems and software development and integrations.
AI grounded in your company data
A public model understands general language but does not know your current prices, contracts, procedures, or way of working. A solution can retrieve approved material, respect user permissions, cite sources, and recognise when information is missing instead of inventing it.
A model may be fine-tuned or trained on properly prepared company data when the expected value justifies it. Training from scratch is rarely the first sensible step. An updatable knowledge base connected to an existing model, or a smaller model tuned for one repeatable task, is often easier to maintain.
Local AI on company infrastructure
Not every document should be sent to an external provider. We can deploy AI on a company server, in a separated environment, as a hybrid solution, or on a fully air-gapped machine.
The model, knowledge base, document search, and user application can all run inside the isolated environment. Prompts and files are not sent to a cloud model provider. This greatly reduces remote exposure through an AI service while the server remains disconnected.
An air gap is not an automatic guarantee that data can never leave. Physical access, accounts, disks, removable media, backups, logs, and the update process still require controls. Local AI also needs suitable hardware, configuration, and maintenance, so the architecture must follow the sensitivity and value of the process.
Cloud, hybrid, local, or air-gapped?
| Option | When to consider it | Main trade-off |
|---|---|---|
| Cloud service | Fast start, variable demand, and access to capable models. | Dependence on a provider, its terms, costs, and data handling. |
| Hybrid | Sensitive data remains local while selected, minimised tasks use cloud services. | More complexity and a boundary that must be controlled carefully. |
| Local server | The company needs greater control over data, models, and access. | Hardware and maintenance cost, with possible model limitations. |
| Air-gapped server | The process requires strong isolation and controlled manual updates are acceptable. | The highest control also brings greater operational cost and procedures for media, updates, and physical access. |
How does an AI implementation work?
- Choose the process and outcome. Describe the manual work, delay, or error and the useful result.
- Review data, systems, and risk. Define sources, integrations, permissions, and decisions that remain human.
- Build a focused prototype. Test one scenario, including missing data and cases where the system should stop or ask for help.
- Measure usefulness. Agree quality, time, cost, or data-transfer criteria and compare the result with the real process.
- Expand in stages. Add sources, actions, or users together with logs, limits, and a safe manual fallback.
AI helps, but it is not infallible
A model can produce a convincing but incomplete or incorrect answer. Its autonomy should match the consequence of an error. A draft message may await approval, while an unusual complaint should go to a person. Pricing, payments, employment, medical, or safety decisions need appropriate control and accountability.
The solution needs boundaries, escalation, and a way to inspect sources or actions. The article AI needs human oversight—hallucinations and verification explains this approach in more detail.
From one improvement to private AI infrastructure
Not every company needs multiple agents and large servers. A useful first deployment may simply organise enquiries or search documentation safely. When it delivers measurable value, more integrations, teams, and controlled actions can be added.
Budget influences data scope, model quality and speed, integration count, isolation, and maintenance. It is usually safer to validate one process before investing in a full platform or private server. If the current system needs preparation first, see application modernisation.
What takes too much of your team’s time?
Describe one task, the applications involved, and the point where work slows down or information gets lost. Do not send confidential documents in the first message; a description and a sanitised example are enough for an initial assessment.
You do not need to choose between an agent, automation, and a local model. We will assess whether AI can reduce manual work or whether a simpler integration is the better answer. The realistic scope follows from the available data, the expected result, and the budget.
Describe your project
We design assistants, agents, and automation that work with company data. The solution can run in the cloud, on your own infrastructure, or on a server with no internet connection.