Internal policy and procedure assistant
Scope, integrations, controls and acceptance criteria are defined for the actual workflow before scaling.
Service
Data & Knowledge Intelligence
PlanckCyber builds data and knowledge systems that make business information usable by people and AI applications. Projects can include retrieval-augmented generation (RAG), enterprise knowledge assistants, semantic search, document intelligence, ingestion pipelines, knowledge bases and controlled access to internal information. The objective is not simply to connect a model to a folder of documents. Reliable knowledge systems require deliberate source selection, metadata, permissions, retrieval quality, citation behavior, evaluation and update processes. PlanckCyber can modernize an existing knowledge workflow, build a new retrieval layer, or connect knowledge infrastructure to agents and applications. Engagements begin with the business questions the system must answer and the information it is allowed to use, then proceed through data readiness, architecture, proof, evaluation and production deployment.
Problems We Solve
When this service fits.
- Employees cannot find reliable internal information
- Critical knowledge is scattered across documents and systems
- AI answers are not grounded in approved sources
- Document-heavy processes require excessive manual review
- Data pipelines are not ready to support AI applications
What We Can Deliver
Built around the requirement.
- RAG and enterprise search architecture
- Document ingestion and enrichment pipelines
- Knowledge assistant applications
- Retrieval and answer-quality evaluation
- Permission-aware source access
- Monitoring and content-update workflows
Use Cases
Concrete examples.
Enterprise search across approved repositories
Scope, integrations, controls and acceptance criteria are defined for the actual workflow before scaling.
Document extraction and classification pipeline
Scope, integrations, controls and acceptance criteria are defined for the actual workflow before scaling.
Technical knowledge assistant with citations
Scope, integrations, controls and acceptance criteria are defined for the actual workflow before scaling.
Knowledge layer used by AI agents
Scope, integrations, controls and acceptance criteria are defined for the actual workflow before scaling.
How an Engagement Can Start
Use the smallest responsible starting point.
Defined knowledge build
When sources, users and target questions are known.
Retrieval proof
To measure whether the available data can support useful answers.
Data readiness review
When sources, quality or access constraints are unclear.
Process
From definition to evidence.
- Define questions and sources
- Assess data and permissions
- Build ingestion and retrieval proof
- Evaluate relevance and grounded answers
- Deploy with monitoring and update controls
Data, Integration & Security
Constraints are design inputs.
- Source authority and freshness
- Document permissions
- PII or regulated data
- Retrieval evaluation
- Citation and provenance requirements
Related Solutions
See the service applied to a business problem.
AI Customer Service
Support teams face repetitive questions, fragmented knowledge and inconsistent routing while customers expect fast answers.
Explore solutionDocument Intelligence
Document-heavy processes require people to extract, compare, classify and review information that software can often assist with.
Explore solutionEnterprise Knowledge Assistant
Employees waste time searching across documents and systems, while generic AI tools may answer without reliable access to approved company knowledge.
Explore solutionFAQ
Questions buyers ask.
What is RAG?
Retrieval-augmented generation retrieves relevant information from approved sources and provides it to a language model when generating an answer. It can improve grounding, but retrieval and answer quality still need evaluation.
Can access permissions be respected?
They should be. A production knowledge system can be designed so users or agents retrieve only information they are authorized to access.
Do we need to fine-tune a model for our company data?
Often not. RAG or structured retrieval is frequently a better first approach for changing factual knowledge. Fine-tuning serves different purposes and should be chosen only when the requirement supports it.
Start with the problem
Have a problem AI might solve?
You do not need a specification. Tell us what you are trying to improve.