Enterprise AIProposed offering

Let your people ask a question and get a cited answer from the sources they are allowed to see.

We build retrieval and assistant systems that index your wikis, drives, tickets and databases, respect each user's permissions, cite every answer and measure retrieval quality before users do.

Explore your knowledge sources Try the knowledge hub demo

This is a service Kindlebit proposes to deliver. No named customer project is published for it on this site.

Retrieval pipeline

Connectors
Chunk and index
Hybrid retrieval
Permission filter
Cited answer
Evaluation

Reference design. Components are options, not a statement of what is deployed at any customer.

Problems this service is built to solve

These are situations we expect buyers to recognise. Each one states why it happens, what it costs and how we would approach it.

Knowledge scattered across repositories

A new engineer needs to know how to request production access. The answer is partly in a wiki page last edited two years ago, partly in a ticket template, and partly with whoever approved the last request.

Why it happens: Content grows in whichever tool a team preferred, and each tool has its own search that sees only itself.
What it costs: Time lost searching, repeated questions to senior staff and decisions made on partial information.
How we approach it: Connect each repository, normalise content and metadata, and index with hybrid retrieval so one question searches them all. Show which source each part of an answer came from.
What to measure: Search success rate, time to find an answer and share of questions answered from more than one source.

Repeated internal support questions

The HR and IT help desks answer the same fifty questions every week: leave rules, expense limits, VPN setup. Specialists spend their day on lookups.

Why it happens: Answers exist, but finding the right clause takes longer than asking a person.
What it costs: Specialist time spent on routine lookups, and inconsistent answers between agents.
How we approach it: An assistant answers common questions from approved policy content with citations and escalates the rest to a person with the conversation attached.
What to measure: Deflection rate with satisfaction, repeat question rate and escalations with context supplied.

Outdated or inconsistent answers

The assistant quotes last year's travel policy, because both versions are in the index and the old one has more text matching the question.

Why it happens: Retrieval treats all documents alike, and nobody tracks versions, owners or effective dates.
What it costs: Employees act on wrong rules, and trust in the assistant collapses after one visible error.
How we approach it: Index metadata such as owner, version and effective date, prefer current documents, retire superseded content, sync on change and flag documents past their review date.
What to measure: Share of answers citing current documents, stale document count and answer accuracy on a policy test set.

Unauthorised information retrieval

An employee asks about the reorganisation and the assistant summarises a confidential board document it was able to read at indexing time.

Why it happens: The assistant was given broad crawl access, and answers were generated before access checks were applied.
What it costs: A data breach from inside the company, and an immediate stop to the project.
How we approach it: Preserve source permissions in the index, filter retrieval by the asking user's identity before the model sees any text, and test with access boundary cases.
What to measure: Access test pass rate, leakage incidents and time to propagate a permission change to the index.

Solutions we engineer

Concrete capabilities, each with the need it serves, how it integrates, what you receive and the value to expect.

Connectors and ingestion

Connectors for wikis, drives, ticketing, email archives and databases, with incremental sync, deletion handling and metadata capture.

Customer need: Get content from where it lives.
Integration: SharePoint, Google Drive, Confluence, Notion, Jira, ServiceNow, databases and file shares.
Deliverable: Connector set with sync monitoring.
Business value: Complete and current content.

Hybrid retrieval

Keyword and vector retrieval combined with reranking, tuned per content type.

Customer need: Find exact terms and related meaning.
Integration: OpenSearch, Elasticsearch, PostgreSQL with pgvector or a managed vector store.
Deliverable: Search service with tuned ranking.
Business value: Higher retrieval quality than either method alone.

Cited answers

Answers built only from retrieved passages, with links to the exact source and a refusal when evidence is insufficient.

Customer need: Make every answer checkable.
Integration: Chat, intranet and in tool surfaces.
Deliverable: Answer service and citation UI.
Business value: Trust and verification.

Permission aware retrieval

Document and group permissions stored with the index and applied as filters at query time using the user's identity.

Customer need: Never show what the user may not see.
Integration: SSO, group directories and source ACLs.
Deliverable: Access model and boundary tests.
Business value: Secure, defensible deployment.

Knowledge freshness and governance

Change based sync, version precedence, review date alerts and owner notifications.

Customer need: Keep answers current.
Integration: Content owners and workflow tools.
Deliverable: Freshness dashboard.
Business value: Fewer outdated answers.

Retrieval and answer evaluation

Question sets with expected sources, scoring for recall, groundedness and answer correctness, and monitoring of production samples.

Customer need: Measure quality continuously.
Integration: CI and evaluation tooling.
Deliverable: Evaluation suite and dashboard.
Business value: Improvement driven by evidence.

Workflow assistance

Actions such as open a ticket, draft a request or start a form, using scoped tools and approval.

Customer need: Go from answer to action.
Integration: ITSM, HR and finance workflow tools.
Deliverable: Action catalogue and approvals.
Business value: Fewer steps from question to resolution.

How we solve it

A delivery sequence that includes model selection, evaluation, data governance and human oversight.

Inventory sources and users

List repositories, owners, volumes, formats and the groups who need answers, and rank by value.

Define access and quality rules

Capture permission models, content that must be excluded, data classification and answer quality criteria.

Design the retrieval architecture

Choose chunking by document type, embedding and ranking approaches, and the permission model at index and query time.

Build ingestion and index

Implement connectors, incremental sync, metadata extraction and monitoring for failures.

Build the assistant

Implement query understanding, retrieval, answer generation with citations and refusal behaviour, and select the model through measured comparison.

Evaluate retrieval and answers

Build question sets with expected sources, measure recall and groundedness, and test permission boundaries and prompt injection in documents.

Deploy to a pilot group

Release with feedback controls, usage analytics and human escalation, then expand by content area.

Operate and improve

Track unanswered questions, stale content and feedback, tune retrieval and add sources in priority order.

Solution in action: Employee policy and IT knowledge assistant

Solution Concept An illustrative scenario. It describes how we would structure the work, not a delivered customer project.

Starting problem

Employees ask HR, IT and finance the same questions repeatedly, and answers differ between staff.

Existing workflow

People search three tools or message colleagues, and HR staff paste policy excerpts into replies.

Improved workflow

An assistant in the chat tool takes the question, identifies the user and groups, retrieves permitted passages from the policy library, wiki and IT knowledge base, and returns a short answer with citations and effective dates. If evidence is weak it offers to open a ticket with the conversation attached.

An employee asks a policy question in chat.

Systems involved

Chat platform, SSO, wiki, document library, ticketing, search index, model provider.

Data movement

Document text, metadata, access groups and user identity. Queries and feedback are stored under a retention policy.

Human decisions

Policy owners review flagged answers and stale document alerts. A person handles escalations.

Automation opportunities

Incremental sync, permission propagation, citation formatting and ticket creation.

Exception handling

No sufficient evidence produces a refusal and an escalation path. Conflicting documents produce an answer naming both with dates.

Resulting user experience

A question gets a cited answer in seconds, and a confidential document never appears for someone without access.

KPIs to evaluate

Search success rate
Groundedness score on a test set
Deflection with satisfaction
Permission test pass rate

What you receive

Concrete deliverables for this service, written so you can check them against the contract.

Source inventory and access model
Connectors with sync monitoring
Search index and retrieval service
Assistant with citations and refusal behaviour
Evaluation question sets, scoring and dashboard
Permission boundary test report
Freshness and governance reports for content owners
Admin guide, runbook and user training

Technology and engineering

Options we would evaluate for this service. Unless a group is marked as publicly listed on kindlebit.com, treat each tool as a proposed implementation option. Naming a tool does not imply a vendor partnership.

Retrieval (proposed implementation options)

OpenSearch
Elasticsearch
PostgreSQL with pgvector
Pinecone
Qdrant
Weaviate
Azure AI Search
Amazon Kendra

Models

OpenAI compatible APIs
Anthropic
Azure AI
AWS Bedrock
Google Cloud AI

Frameworks

LangChain
LlamaIndex
Haystack
Custom Python services

Evaluation

LangSmith
Arize
Galileo
Ragas style metrics

Relevant Kindlebit work and evidence

We use the strongest evidence available and say which kind it is.

Proposed offering

Evidence status for this service

This is a service Kindlebit proposes to deliver. No named customer project is published for it on this site.

See case study status

Solution ConceptInteractive demo with simulated data

Enterprise Knowledge Intelligence Hub

An interactive demonstration with a synthetic corpus, role based access and a retrieval evaluation panel. The retrieval logic runs in your browser on demo data and is not connected to any real repository.

Open the demonstration

Business outcomes and success criteria

These are the measures we would agree before work starts. They are criteria for success, not results from past engagements.

Search success

Share of queries where users find or receive a correct answer.

Answer accuracy

Correctness and groundedness on a labelled question set.

Retrieval quality

Recall of expected sources in the top results.

Employee productivity

Time to answer and reduction in repeated questions to specialists.

Questions buyers ask

What is RAG and why use it?

Retrieval augmented generation retrieves relevant passages from your content and asks the model to answer using them. It keeps answers tied to current internal sources and allows citations, without retraining a model.

How do you protect confidential documents?

Permissions from the source are stored with the index, and retrieval filters by the asking user's identity before any text reaches the model. We test with boundary cases as a release requirement.

How long does it take?

A pilot on two or three sources typically takes weeks. Time depends on connector availability, content quality and permission complexity.

Do we need a vector database?

Not always. Existing search platforms and relational databases with vector support can be sufficient. We choose based on scale, filtering needs and your operational preferences.

How do you handle content that is wrong or outdated?

We surface stale and conflicting content to owners, prefer current versions in ranking and show effective dates in answers. The assistant exposes content problems, which is a benefit but needs owners.

What does it cost to run?

Costs come from indexing, storage, search infrastructure and model calls per question. We estimate them during design and add metering.

Related services

Industries where this applies

Discuss your knowledge assistant and the sources behind it.

List the repositories your people search and the questions they ask most. We will outline what an assistant would need to answer them safely.

Talk through your knowledge sources Explore a demonstration