Applied generative AI for enterprise
Generative AI is easy to access.
Making it useful is the hard part.
Large language models are becoming widely available. On their own, however, they know little about your organization, processes, customers, or business context.
Real value comes from connecting AI with trusted enterprise data, knowledge, applications, and workflows.
At Fabrity, we build applied generative AI solutions that work in your real business environment—helping people find information faster, interact with data, automate knowledge-intensive work, and add intelligent capabilities to existing software.
How we help
1. Give employees faster access to enterprise knowledge
When it fits
Knowledge is scattered across documents, intranets, manuals, and other repositories, making it difficult for employees to find reliable answers quickly.What we do
- Connect AI assistants to enterprise knowledge sources.
- Build RAG and semantic search solutions.
- Ground answers in trusted content with source references.
- Integrate permissions, identity, and existing business systems.
Examples from our project portfolio
Technical knowledge assistant
Challenge: Maintenance and technical teams need to search extensive documentation before they can diagnose issues or perform complex tasks.
Solution: An AI assistant retrieves relevant information from technical documentation and allows users to ask questions in natural language.
Value: Faster access to technical knowledge, less time spent searching documentation, and better support for employees in the field.
Customer support knowledge assistant
Challenge: Customer service teams need to find accurate answers across product information, procedures, policies, and support materials.
Solution: A knowledge assistant searches approved enterprise sources and provides contextual responses with references to the underlying information.
Value: Faster response times, more consistent answers, and easier access to organizational knowledge.
Employee onboarding assistant
Challenge: New employees need to learn company policies, processes, tools, and organizational knowledge distributed across multiple systems.
Solution: A conversational assistant provides access to approved internal information through one natural-language interface.
Value: Faster onboarding and less repetitive support work for HR and internal teams.
Start with one knowledge domain
Select a focused area where employees already spend significant time searching for information.
We can connect the relevant sources, validate retrieval and answer quality using real company data, and determine whether the use case is ready to scale.
2. Make enterprise data easier to explore
Enable business users to ask questions about operational and analytical data in natural language—and get answers, insights, and visualizations.
When it fits
Business users rely on complex dashboards, spreadsheets, or analysts to get answers from enterprise data.
What we do
- Connect AI assistants to enterprise data.
- Enable natural-language querying and visualization.
- Combine structured data with relevant business context.
- Integrate access controls and existing analytics platforms.
Examples from our project portfolio
AI assistant for industrial data
Challenge: Industrial environments generate large amounts of machine, sensor, and production data, while plant managers often need to navigate complex dashboards to find relevant information.
Solution: An AI assistant combines natural-language interaction with operational data and a RAG layer containing relevant technical knowledge.
Users can ask questions about production data, request analyses or visualizations, and receive contextual information that helps them investigate operational issues.
Value: Faster access to operational insights and a simpler way for business users to interact with complex industrial data.
Conversational data exploration
Challenge: Business teams depend on analysts or predefined dashboards whenever they need to answer a new question.
Solution: A data assistant translates natural-language questions into controlled queries and presents relevant results in an accessible format.
Value: Faster exploration of data and lower dependence on manual reporting for everyday business questions.
AI needs a reliable data foundation
Generative AI cannot compensate for fragmented, inaccessible, or poorly governed data.
If your challenge starts with data integration, quality, architecture, analytics, or governance, we can address the foundation before building the AI layer.
3. Automate knowledge-intensive work with AI agents
When it fits
Employees repeatedly search for information, process documents, enter data, or move between systems to complete routine tasks.What we do
- Design AI agents for specific business workflows.
- Connect agents to APIs, CRM, ERP, and other systems.
- Combine AI with business rules and human approval.
- Monitor outputs, exceptions, and performance.
Examples from our project portfolio
AI-assisted B2B order processing
Challenge: Phone orders require sales agents to manually identify customers and products, enter information, verify details, and prepare orders for processing.
Solution: An AI-powered application combines speech-to-text, customer information, product recognition, verification workflows, and order processing.
The solution can transcribe conversations, identify relevant customer and product information, prepare an order for review, and support additional sales opportunities.
Value: Less manual processing, faster order handling, and greater capacity for sales teams.
Intelligent document workflow
Challenge: Employees spend significant time reading incoming documents or messages, extracting information, classifying requests, and entering data into business systems.
Solution: AI interprets incoming content, extracts the relevant information, and prepares or triggers the next step in the workflow, with human review where required.
Value: Reduced repetitive work, faster processing, and better consistency.
AI often becomes part of a larger software solution
An assistant or agent rarely operates in isolation.
It may need its own user interface, business logic, workflow engine, integrations, security model, or custom application around it.
That is where our software engineering capabilities become part of the solution.
4. Build enterprise RAG and AI search
Ground generative AI in the information your organization already trusts.
When it fits
Relevant knowledge exists across documents and systems, but general-purpose AI lacks the context needed to provide reliable, company-specific answers.
What we do
- Build RAG and enterprise search solutions.
- Connect and index internal knowledge sources.
- Apply permissions and provide source references.
- Evaluate and optimize retrieval and answer quality.
Examples from our project portfolio
One conversational interface across multiple knowledge sources
Challenge: Employees need to search several systems and repositories to find the information required to complete a task.
Solution: A shared retrieval layer searches approved enterprise sources and supplies relevant context to an AI assistant.
Value: One access point to distributed knowledge and less time spent switching between systems.
Source-backed answers from complex documentation
Challenge: Large volumes of technical information are difficult to navigate using traditional keyword search.
Solution: Semantic retrieval identifies relevant document fragments and provides them as context for generated answers, together with source references.
Value: Faster knowledge discovery while allowing users to verify the underlying information.
Start with one high-value knowledge set
We can validate a RAG use case using a representative set of your own documents before committing to a wider implementation.
The goal is to measure retrieval quality, answer relevance, security requirements, and business value early.
How we deliver
1. Start with the business use case
- Identify the workflow and users.
- Assess data and integration needs.
- Define scope and success metrics.
Use case first. Technology second.
2.Validate with real data
- Evaluate response and retrieval quality.
- Test different approaches where needed.
- Measure performance, cost, and user feedback.
Evidence before production.
3. Engineer for production
- Integrate with enterprise systems and identity.
- Add monitoring, evaluation, and human oversight.
- Optimize security, scalability, and performance.
Production systems—not isolated AI demos.
Engagement models
Work closely with the team
- Transparent backlog and progress tracking
- Hybrid teams with your specialists
- Shared tools, repository, and communication
- Ongoing knowledge transfer
Receive a ready-to-run solution
Let us take end-to-end responsibility for delivery.
- Regular demos and sprint reviews
- Working, tested, production-ready software
- Full handover of code and documentation
- Support and further development if needed
Commercial models
Fixed fee
A defined scope, budget, and delivery plan.
Outcome-based
Fees linked to agreed and measurable business outcomes.
Development subscription
Why Fabrity
A long-term technology partner for enterprises
Specialized in custom software, data, and AI
Engineering expertise since 2007
300+ experts across five offices in Poland
Listed on the Warsaw Stock Exchange: FAB
Fabrity operates as a publicly listed technology company.
ISO 9001, ISO/IEC 27001 and Cyber Essentials
Who we work for
Need support with your data?
Get in touch to see how we can help.
Ready to put generative AI to work?
Identify the right use case, validate it with your data, and move from PoC to production.
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