Put Your Organization's Knowledge to Work, With Permissions Intact
Policies, product documentation, contracts and support history usually sit across wikis, shared drives, ticketing tools and inboxes. Kramora designs AI assistants that retrieve from those sources, respect who is allowed to see what, cite where every answer came from and route uncertain or sensitive requests to people. The result is an assistant your teams can question with confidence and your security team can reason about.
The Operating Challenge
What the Solution Includes
Knowledge access
The first decision is which sources the assistant may use and which questions it should answer. Connectors pull content from document stores, intranets, ticketing tools and databases, keeping metadata such as owner, date and access group. Narrow, well-understood sources make a better first release than connecting everything at once.
Retrieval design
Documents are split, enriched and indexed in vector databases alongside keyword search, because exact terms like product codes and clause numbers matter. Retrieval combines both signals and reranks results before the model sees them. Each answer is generated only from retrieved passages and cites them.
Access control
Permissions from source systems travel with every indexed chunk and are checked at query time against the signed-in user's identity. A finance analyst and a field technician asking the same question can receive different, correctly scoped answers. Sensitive collections can be excluded entirely or placed behind additional approval.
Human review and escalation
Assistants are told to decline when sources do not support an answer, and low-confidence or policy-sensitive requests route to a named owner. User feedback on each answer feeds a review queue for content owners. That loop improves both the assistant and the underlying documentation.
Evaluation and operations
A test set built from real questions measures grounding, accuracy and refusal behavior, and it runs before every prompt, model or index change. Production monitoring tracks usage, unanswered questions and cost. Teams can see whether the assistant is getting better and where knowledge gaps remain.
Illustrative Architecture
Illustrative reference architecture, not a client implementation, showing how a question moves from connected sources to a cited, permission-checked answer.
Outcomes to Expect
Policy and procedure assistant for an operations team
A logistics company keeps operating procedures, HR policies and customer contract terms in a document management system, an intranet and a ticketing tool. Supervisors want quick answers, while contract terms must stay visible only to account teams.
Kramora would start with a narrow set of high-value supervisor questions and the sources that answer them, then build hybrid retrieval that mirrors existing access groups. An evaluation set drawn from real questions would gate the first release, with low-confidence answers routed to a policy owner for review.
Frequently Asked Questions
Model choice follows the use case, data sensitivity and hosting constraints. Kramora works with OpenAI-compatible APIs and can evaluate hosted or self-managed options within your AWS, Azure or Google Cloud environment. The architecture keeps the model replaceable so the decision can be revisited as options change.
Services Involved in This Solution
Relevant Industry Context
Start with the operating challenge.
Tell us where work slows down today. We will help you decide whether this solution fits and what a first phase could look like.