AI Solutions / Company Knowledge
Company knowledge. Ready when work needs it.
Ask a work question without first working out where the answer lives. Hashfox connects your company knowledge to an AI assistant that searches approved material and returns answers your team can check against the source.
Finding the person who knows has become part of the process.
“Which version is current?” “How did we solve this last time?” “Where did we record that agreement?” A customer is waiting while your team checks folders, messages and closed tickets. Eventually, someone remembers the right file. That works until they are away or the next colleague has to start again. Your knowledge needs to be available at the point of work, with enough context to use it confidently.
Give company knowledge a route back to the evidence.
Retrieval-Augmented Generation, or RAG, brings relevant passages from your documents into the answering process. The assistant works from that material and presents the references alongside its response. A colleague can open the underlying document, check the wording and decide what to do next. The useful part is being able to follow an answer back to something your business actually recorded.
- Connect manuals, contracts, internal documents and ticket histories.
- Find relevant passages even when the question uses different terminology.
- Keep document versions and locations visible, and make gaps or conflicting information obvious to the person asking.
Access rules still apply when the interface is a question.
A convenient search box must respect the boundaries already present in your organisation. We connect identity and document permissions to retrieval, then test what different users can actually see. We also define how revised files, deleted content and revoked access reach the search system. Choose your own infrastructure or a private cloud. Hashfox operates customer systems in Germany and the EU.
- Map users and groups to the permissions supported by each source.
- Establish clear rules for updates, document versions and removal, from the ERP system to the ticket queue.
- Bring answers into the applications your staff already use, with connections built in Python, PHP or TypeScript, including interfaces to legacy industrial systems.
Before opening another ticket, find out what the business already knows.
A customer reports a fault. Your support colleague asks whether the same issue has been resolved before and which return terms apply to this account. The assistant brings together relevant ticket history, product guidance and accessible contract passages, and each finding points back to its source. In wholesale, a system like this is already part of daily operations: Hashfox introduced a company knowledge assistant, a support system and risk management on company-owned hardware.
- Bring approved tickets, manuals and contract passages together into one evidenced answer.
- An unresolved question remains a question instead of disappearing into a smooth sentence.
- Returns and replacements stay with the person responsible, who now has the evidence to hand.
Start with the questions your team asks every week.
- 1
Choose a useful starting point
Bring recurring questions and examples of how people answer them today. We agree on a manageable scope and identify the owners of the source material.
- 2
Test the evidence
A focused trial checks retrieval, references and behaviour when information is missing. Different user roles are part of that test.
- 3
Connect the working environment
We implement the agreed integrations and access rules. Your subject specialists review the results before wider use.
- 4
Keep the knowledge usable
Feedback reveals missing material and weak answers. We improve the existing scope before adding more sources.
Before you connect your documents
Does RAG mean training a model on our files?
Usually, no. RAG retrieves relevant material when a question is asked and gives that material to the model as context. Whether you run the language model yourself is a separate decision.
Does a cited answer mean a correct answer?
A reference gives you something to check. The assistant may still miss context or draw the wrong conclusion. We evaluate representative questions and keep the original material within reach of the person reviewing the answer.
How do you protect restricted information?
Access controls must apply to retrieval, before restricted content reaches the answering model. We examine the permissions available in your source systems and test the implementation with different users. Prompt instructions are not an access-control system.
How does the assistant stay current?
Each connected source needs an update process. We define how revisions, removals and permission changes are reflected in the index, based on the source interfaces and your operational requirements.
Can everything run on our own infrastructure?
Yes, with a suitable architecture, and private cloud deployment is equally possible. The choice covers the complete processing chain, including document storage, search components and the model serving the answers.
Bring the question everyone keeps asking.
In a free initial consultation, we will look at where the answer lives and what makes it difficult to find. You will get a straight assessment of whether an assistant is a sensible next step.
- Free and without obligation
- 30 minutes, by video call or phone
- An honest assessment, even when it argues against a project