Never leaf through folders or wikis again
In seconds, an internal chatbot searches policy documents, proposals and manuals that would otherwise take hours to read manually.
We integrate securely with SharePoint, Confluence, CRMs and even legacy systemsâwithout leaking sensitive data.
Every answer includes a source reference so colleagues immediately see where the information originates.
Each question feeds the language model, making the bot ever smarter and eliminating dead ends.
Although an internal chatbot may resemble an advanced search bar at first glance, in practice it acts as a digital colleague with a photographic memory.
Employees type a question, receive a clear answer in a flash and continue without leaving their workflow.
We often see teams experiment more quickly because misunderstandings about policy or technical details disappear overnight.
Because the chatbot quotes legislation and internal guidelines verbatim, the risk of costly violations drops.
Colleagues find what they are looking for 60% faster.
Error rates fall by an average of 35%.
Onboarding time shortens by one to two weeks.
An overview of the crucial stepsâfrom source inventory to continuous tuning.
We start with a deep-dive workshop: which intranet pages, PDF manuals and shared drives contain critical knowledge? Less is sometimes more; duplicate files are removed immediately.
OAuth, service accounts, data classificationâit might sound dull, but this layer determines whether the rest will succeed. Security therefore always comes first.
The bot learns from the selected documents. We label data, set context boundaries and test response time versus accuracy.
A small group of experienced users asks trickyâand sometimes oddly phrasedâquestions. Their feedback fine-tunes the flows and prevents teething problems.
After go-live we measure question patterns, fill gaps and release versions with improved intent detection or fresh integrations such as voice.
Ready to put knowledge to work?
Let us explore together which internal sources your teams have yet to tap. A no-strings conversationâcoffee includedâoffers immediate clarity on feasibility, security requirements and the first quick wins.
We notice that organisations sometimes expect a chatbot to fix every outdated process. That is difficult when the underlying data is messy. In fact, a bot amplifies inconsistencies when sources contradict one another. Data quality therefore deserves priority above all else.
Without clear usage guidelines the bot will guess. And guessing on compliance questions? Best avoided.
We limit the language model to internal context so nobody is confronted with public internet material out of the blue.
1. Start small; choose a single department. 2. Select high-volume, low-complexity questions (leave, expenses). 3. Document missed answers, then enrich the dataset. 4. Measure adoption through chat logs, not gut feeling. 5. Integrate into the existing chat client, for example Teams.
Within two years we expect speech-driven internal chatbots that generate presentations from raw project data. We are already seeing pilots where augmented-reality glasses project the botâs answers above factory machinesâhandy, but still pricey.
Legislation changes; the next generation of bots will automatically monitor official journals and update policy texts in real time.
Yield.inc is a new asset manager in the Netherlands that focuses on excellent customer experience, sustainability and technology. When faced with the question of whether to develop our platform in-house or with a partner, we got in touch with Spartner. We are still 100% behind the choice for Spartner because we own the source code and with Spartner, a deal really is a deal, whether it concerns the delivery date or the agreed budget!
Hans Lugtenberg Partner at Yield Inc.
Spartner is involved and contributes in our search for innovations and always delivers top-notch work. After the first Huurda.nl version got out-dated, we collaboratively launched a completely new version in 2020.
Read moreNorbert Wegter Owner of 123wonen and Expat Homes Holland
Spartner has created a customized portal for us for the accountability of large-scale collaborative projects. This enables us and our partners to execute and account for their plans in a shared environment that is secure, transparent, and manageable. The pragmatic and flexible collaboration with Spartner has ensured that we have quickly achieved a functional and user-friendly environment.
Jurjen Terpstra Managing Partner at Wecreate Consulting
In 2020, we transferred the development of Lynn to Spartner. As a result of their fast development process, the Lynn platform achieves an increasingly central role in the legal world.
Read moreMarco Caspers Software Development Manager at Lynn Legal
Innovation and change are not always welcomed with equal enthusiasm by our employees, man is a creature of habit but as a company you have to dare to move forward and the need for a contemporary drawing program for the realization of various constructions with our Masterbloc bricks was high. For us but also for Spartner, this challenge was a bit of pioneering and together we had to overcome some hurdles to arrive at a beautiful and efficient business tool. A development and learning process for both parties, in which the feedback of questions and desired adjustments by our employees to Spartner was always in good consultation and we could count on quick feedback and targeted solutions. We continue to exchange experiences with Spartner and look back and forward to a successful collaboration.
Read moreBjorn Gubbels Owner of Masterbloc
With weekly calls, using our Azure DevOps sprint system, Spartner actively works together with our innovation, communication and software development departments.
Read moreMarco Leenders Head of Automation
The projectteam of the city of Groningen is very enthusiastic about the "Roomfinder" platform.
Read moreGemeente Groningen Projectteam
We requested custom help to a performance challenge we had in our implementation of Laravel Excel. I was amazed how quickly these Laravel artisans achieved significant performance gains, which saved us a lot of development time.
Read moreJosh Mountain Co-Founder at IncentivePilot.com
Throughout the great years that we cooperate with Spartner (previously Maatwebsite, Ed.), our software has been developed continuously. Students and landlords actively work with features like allocation, payments, chat, contract generation and more.
Read moreMaurice Evers Head of Student Housing Department
The pro-active approach of Spartner was crucial in understanding the complexity of the eHerkenning network.
Read moreMaarten Bremer CTO / Founder Ensured
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As jurists with knowledge of IT, we highly value quality; within code, but also in process. The high work-level and trustworthiness of Spartner gives us the capacity to continuously create innovative features.
Read moreNiels Winters Managing Director JuriBlox
Practical answers to the questions we hear most often.
An internal chatbot is an AI-driven conversational partner that answers employeesâ questions based on internal documents such as policy papers, proposals, project reports or legislation. Instead of searching yourself, you simply ask a question in natural language.
We connect via encrypted APIs, apply strict access rights and log every interaction. In addition, the language model remains in a closed environment; no data are sent to external cloud services without explicit permission.
Although exact timing varies per organisation, most notice within a few weeks that repetitive questions are handled more quickly. The immediate saving lies mainly in fewer search hours and a reduction in human errors.
PDFs, Word files, wiki pages, spreadsheets and even structured data from CRM systems. As long as it contains machine-readable text, it can in principle be indexed.
Costs depend on licences and integration complexity, but a small-scale pilot usually fits within a limited budget. More importantly, the time saved often far outweighs the initial investment.
Human validation remains essential. We build feedback buttons so users can immediately indicate when an answer is incorrect. That feedback enters a retraining cycle so the error does not repeat itself.
Yes, multilingual models are possible. Bear in mind, however, that the source documents must also be multilingual; otherwise, a knowledge gap will still arise.
A modern identity provider (Azure AD, Okta), API access to data sources and ideally a dev environment for testing. We also review network architecture to minimise latency and security risks.
Thanks to container technology (Docker, Kubernetes) you can scale as usage grows. Adding compute nodes horizontally is usually enough to handle peaks.
We combine AI expertise with in-depth knowledge of business processes. Just as important, we keep all data within European data centres, which simplifies GDPR compliance.
Whether you have a new idea or an existing system that needs attention?
We are happy to have a conversation with you.
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