Solution
Make generative AI useful inside the organization.
Fuchsius designs RAG systems, enterprise assistants, copilots and LLM integrations that connect models to governed data and business tools while maintaining permission boundaries and evaluation.
The challenge
The challenge
- Public AI tools do not have the organization's current or private knowledge.
- Teams need answers grounded in internal documents and systems.
- LLM outputs need evaluation, traceability and access controls.
- Generative AI must fit existing workflows rather than become another isolated application.
What this solution is designed to improve
Faster access to organizational knowledge
Reduced repetitive drafting and search work
Consistent access controls
Measured answer quality
Integration with business tools and workflows
Capabilities
Capabilities this solution combines
Use cases
Common use cases
Our approach
From assessment to evolution
- 01
Assess
Understand the current business process, users, technology estate, data, constraints, risks and desired outcomes.
- 02
Design
Define the target experience, solution architecture, integration model, security approach and delivery roadmap.
- 03
Build
Engineer the solution iteratively with testing, automation, observability and security built into delivery.
- 04
Launch
Prepare migration, production deployment, training, monitoring, support and operational handover.
- 05
Evolve
Use real operational data and business feedback to optimize, extend and modernize the solution.
Implementation
Implementation phases
Discover
Establish current state, target outcome, constraints and measurable baseline.
Prove
Test the highest-risk product, architecture, data or integration assumptions.
Deliver
Build production capability in reviewable increments.
Transition
Prepare data, users, operations and support for production change.
Optimize
Use real usage and operational evidence to improve the solution.
Architecture
Architecture considerations
- Separate model/provider choice from application orchestration so models can be evaluated or changed.
- Define permission-aware access to enterprise data, tools and actions.
- Create deterministic controls around high-impact agent actions.
- Build evaluation, tracing, cost and safety monitoring into the AI application.
- Keep a clear human escalation path for ambiguous, sensitive or high-impact cases.
Risks
Risks to manage
- Automating a poorly understood process.
- Using sensitive data without appropriate access or retention controls.
- Evaluating AI only through demos instead of representative tasks.
- Giving agents broader tool permissions than the workflow requires.
- Operational costs rising because token, model and tool usage are not measured.
Governance
Governance and ownership
- Define a named business and technical owner.
- Document material architecture and operating decisions.
- Track assumptions, risks and dependencies.
- Use measurable acceptance criteria for major releases.
- Review production evidence after launch.
- Maintain evaluation datasets and model/application quality thresholds.
- Define human escalation and prohibited-action rules.
Deliverables
Typical deliverables
Possible success measures
Task success rate
Evaluation quality
Human escalation rate
Cost per task/interaction
Latency
User adoption
Unsafe/incorrect output rate
Use only measures that match the actual business baseline and solution scope.
FAQ
Common questions
Can Enterprise Generative AI start with a discovery phase?
Can Fuchsius work with our current platforms and vendors?
How are technology choices made?
Can the solution be delivered in phases?
Can Fuchsius operate or support the solution after launch?
Discuss this solution
Does this match the problem you are trying to solve?
Describe the objective, current systems and constraints. We can help shape the approach and the practical next step.