01Useful knowledge is buried in documents
Information people cannot find quickly.
RAG retrieves relevant approved information for an answer — it is not automatic model training or a guarantee of accuracy.
Discuss this situationKnowledge, documents and decision support
When the problem does not fit an off-the-shelf tool, start with your knowledge, documents and existing systems. Define the output your team needs and build a solution around that specific task.
01Starting points
Common solutions problems
When the hard part is the knowledge system
Custom Solutions own RAG, document intelligence and problem-specific AI systems. This page is not another five-service hub.
02Staff repeatedly search or summarize information
Manual review of the same document piles.
Document extraction and summarization can be scoped with evaluation examples and review paths.
Discuss this situation03Generic assistants lack business context
Tools that do not know your information.
Business-specific context, permissions and retrieval design matter more than a generic chat UI alone.
Discuss this situation04Prototype has no evaluation criteria
A demo without a definition of correct.
Agree task-specific evaluation and acceptable performance before treating a prototype as production-ready.
Discuss this situationScroll to explore
04 situations
Need the product interface too?
AI Applications own the usable product surface. A project may include both, with separate deliverables.
02AI services
Solutions scope
Supported examples may include RAG, internal copilots, document extraction/summarization and LLM integration. Prediction models, computer vision, fine-tuning, on-premise deployment and compliance certifications are omitted unless confirmed.
What the system must produce — and from what.
Assess documents, systems, permissions and whether information is complete enough for the task.
- Task definition
- Data inventory
- Permission boundaries
- Gap and risk notes
RAG and document intelligence in plain language.
Design how approved information is retrieved or processed for the specific output your team needs.
- Retrieval design
- Document workflows
- Freshness considerations
- Fallback when info is missing
LLM integration with your systems.
Connect models and tools with access controls and auditability appropriate to the task.
- LLM integration
- System connections in scope
- Access controls
- Logging as agreed
Representative examples before release.
Evaluate retrieval quality, exception handling, usefulness and operating cost for the scoped task.
- Evaluation examples
- Exception handling
- Monitoring scope
- Handover docs
Internal copilots
Internal copilots may be delivered as a Custom Solution, an Application interface, or both — depending on whether the primary deliverable is the knowledge system or the product UI.
03How we work
Solutions process
Define task and data → prototype a specific approach → evaluate and integrate → validate and hand over.
Output, sources and permissions.
Write down the job to be done and what information is allowed.
Define
- Task
- Sources
- Permissions
- Success criteria
Outcome
A problem statement ready for design.
No general accuracy guarantee
Agree task-specific evaluation instead of promising every output is correct.
04Why Buildaze
How success is assessed
Success depends on task-specific quality, relevant retrieval and useful exception handling.
RAG is not automatic truth.
When RAG is in scope, review whether answers draw on the right material — and refuse when they should not.
Ready to discuss a custom AI solution?
Share the task, document types and systems involved — not confidential credentials in the form.
TaskRetrieveEvaluateHandover
Not a second AI hub.
05Questions
Applications focus on the usable product or feature. Custom Solutions focus on the underlying knowledge, document or business-specific AI system. A project may include both, with separate deliverables.
06Related services
07Contact
Tell us what you want AI to help with
Share who will use the solution, the task they need to complete and which systems or documents are involved. Do not send confidential credentials through the form.