Enterprise knowledge management AI that cites its sources and respects your permissions
We build retrieval-grounded assistants over your policies, manuals, tickets, contracts and databases. Every answer quotes the passage it came from, every retrieval checks what the asking user is allowed to see, and an evaluation set tells you how often the answer was right before anyone outside the pilot team sees it. Deployed in your region, on your tenancy, with your access model intact.
Why the answer exists somewhere and nobody can find it
Every large organization has already written down most of what its people need to know. The procedure is on SharePoint, the exception is in a ticket from 2024, the contract clause is in a PDF on a shared drive, the number is in a database only two people can query. New joiners ask colleagues. Experienced staff answer the same question weekly. Compliance teams discover that three versions of a policy are in circulation.
Early generative AI pilots made this worse in a specific way. They answered confidently from the wrong document, or from a document the asking user should never have seen. One leaked salary band or draft board paper ends the program.
Our approach to enterprise RAG solutions
We build retrieval-augmented generation the way a security team would specify it. Content is ingested with its original permissions attached. At query time, retrieval runs as the asking user, so a document they cannot open cannot inform their answer. The model is instructed to answer only from retrieved passages and to cite them, and when the evidence is thin it says so rather than filling the gap.
Quality is measured, not assumed. Before go-live we assemble a question set with your subject-matter experts, score retrieval recall and answer groundedness against it, and repeat that scoring on every content or model change. The same retrieval layer serves our conversational agents and agentic workflows when they need to look something up.
What the solution includes
Six components. Permission handling and evaluation are built in from the first ingestion, not added before launch.
Connectors and content ingestion
SharePoint, OneDrive, Google Drive, Confluence, ServiceNow, email archives, databases and file shares ingested incrementally, with chunking tuned to document type and tables, images and Arabic text handled correctly.
Permission-aware metadata and access control
Source permissions, sensitivity labels and ownership captured at ingestion and enforced at query time through your identity provider, so retrieval runs as the user, never as a service account.
Hybrid retrieval and ranking
Keyword and semantic search combined, then re-ranked, so a part number and a paraphrased question both find the right passage. Metadata filters narrow by department, date, product or jurisdiction.
Grounded answer generation
The model answers from retrieved passages only, with a confidence signal and an explicit "not found in your sources" response when the evidence is insufficient.
Citations and feedback interface
Every answer links to its source passages with the exact text highlighted. Users rate answers and flag wrong ones; flags open a review queue for content owners.
Evaluation, freshness and index monitoring
A curated question set scored on every change, plus monitoring of stale documents, failed ingestions and permission drift between the source and the index.
High-value use cases for an AI knowledge base
The strongest cases pair a well-defined audience with a document set that already exists and changes slowly enough to govern. These six are typical first deployments.
HR and policy assistant for employees
Leave, travel, expense and conduct policies answered with the clause cited, filtered by the employee's entity, grade and country, so a UAE employee sees UAE rules.
Technical support knowledge search
Resolved tickets, runbooks and product documentation surfaced to support engineers mid-case, with the fix that worked last time and who applied it.
Engineering and operations documentation
P&IDs, maintenance procedures, equipment manuals and safety rules searchable by asset and plant, for field teams who need the answer on a tablet.
Sales enablement and proposal reuse
Past proposals, pricing rationale and compliance responses retrieved by requirement, so bid teams start from what was already approved rather than a blank page.
Regulatory and contract navigation
SAMA circulars, NCA controls, PDPL texts and your own contracts cross-referenced, with obligations traced to the clause and the date it took effect.
Onboarding and internal learning
New joiners ask the assistant instead of a colleague, and the questions they ask reveal which documents are missing or unclear.
How does a permission-aware RAG pipeline answer a question?
Six steps, each logged. The permission filter at step three is applied before retrieval, not after, so restricted content never reaches the model.
- 01
Receive the question
From Teams, Slack, a web portal or an API call, with the user's identity token attached.
- 02
Resolve identity and scope
Group memberships and entitlements are resolved from the identity provider; department, country and sensitivity filters are derived.
- 03
Retrieve within permissions
Hybrid search runs only over content the user may open. Results are re-ranked and the top passages selected.
- 04
Generate from passages
The model composes an answer from those passages alone, or states that the sources do not cover the question.
- 05
Cite and deliver
The answer is returned with linked, highlighted source passages and a confidence indicator.
- 06
Capture feedback and score
Ratings and flags are stored, sampled answers are scored against the evaluation set, and content owners see what needs fixing.
Deployment, data residency and integration
The assistant runs in your Azure, AWS or Google Cloud tenancy in the region you choose, or on-premises with locally hosted embedding and language models where content may not leave the building. Connectors use the source systems' native APIs and honor their permissions. Delivery channels include Microsoft Teams, Slack, an embedded web widget and an API for other applications. Our data engineering team governs the ingestion pipelines alongside your existing lakehouse.
Where we deliver: in Saudi Arabia, SDAIA PDPL and NCA ECC-2:2024 favor in-Kingdom hosting and strict access control, which permission-aware retrieval provides; in UAE, UAE PDPL and government data classification rules decide which content may be indexed and where; in India, the DPDP Act 2023 and RBI or SEBI guidance govern employee and customer data in regulated firms; in United States, enterprises want the assistant inside their existing Microsoft 365 or Google Workspace security boundary. Our offices support each region.
Benefits and success criteria
Measures are agreed with your knowledge and security owners before the pilot and scored against a question set your experts built. We publish the pilot results to you in full.
Retrieval recall
How often the passage that holds the answer is in the retrieved set, scored on the expert question set per content area.
Groundedness and citation accuracy
Share of answers fully supported by their cited passages, judged by reviewers and automated checks.
Permission compliance
Zero answers drawing on content the user could not open, verified by red-team queries against restricted documents.
Adoption and deflection
Weekly active users, repeat use and questions answered that would otherwise have gone to a colleague or a help desk.
Implementation approach
A first deployment covers one audience and one or two content sources. The index, permission model and evaluation suite are then extended source by source.
- 01
Choose the audience and sources
One user group, one or two repositories, and the owners who will keep the content current.
- 02
Build the expert question set
Your subject-matter experts write 100 to 200 real questions with the correct answer and source. This is the yardstick for everything after.
- 03
Map permissions and sensitivity
Source permissions, labels and exceptions are reviewed with security. Content that cannot be governed is excluded until it can.
- 04
Ingest, retrieve and evaluate
Connectors and chunking are tuned until recall and groundedness on the question set meet the agreed bar.
- 05
Pilot with a closed group
The audience uses the assistant in Teams or the portal for several weeks. Flags and ratings are reviewed daily with content owners.
- 06
Extend and keep scoring
New sources and audiences are added; every content or model change is re-scored before release.
Why Constient for enterprise knowledge intelligence
We treat permission enforcement and evaluation as the product, not as features to add before launch. Our retrieval layer is the same one our agents and voice assistants depend on in production, so it has been tested by systems that act on its answers, not just display them.
The same engineers run our generative AI service line and the data platforms beneath it, and for regulated clients our BFSI team brings experience with SAMA and NCA compliance content. If your documents are still scanned paper, our document processing solution gets them into a searchable form first.
Frequently asked questions
How does an enterprise knowledge engagement with Constient start?
With an audience and a question set. You pick one user group and one or two repositories, and your experts write 100 to 200 real questions with correct answers and sources. We review permissions with your security team, build a first index and report recall and groundedness against that set. If the content is not governable yet, we tell you what to fix before indexing it.
How do you stop the assistant revealing documents a user should not see?
Permissions are captured from the source at ingestion and enforced at query time by running retrieval as the asking user through your identity provider. Restricted passages never reach the model for that user. Before go-live we run red-team queries against known restricted documents and report zero leakage as a pass condition. Permission drift between source and index is monitored continuously.
How accurate are the answers, and how will we know?
We measure rather than promise. Your expert question set is scored for retrieval recall and answer groundedness before launch and on every content or model change, and you see the scores. The assistant is instructed to say when the sources do not cover a question instead of guessing, and every answer carries citations your users can check in one click.
Can the data and models stay in our region or on-premises?
Yes. The assistant runs in your cloud tenancy in-region or on-premises, with embedding and language models hosted locally where content may not leave the building. We document the design against SDAIA PDPL, UAE PDPL or the DPDP Act 2023 and your classification rules before indexing anything. Nothing is sent to a public model endpoint without your written approval.
Which content sources and channels do you support?
SharePoint, OneDrive, Google Drive, Confluence, ServiceNow, email archives, file shares and SQL databases through their native APIs, with Arabic and English content handled together. Users reach the assistant in Microsoft Teams, Slack, an embedded web widget or through an API that other applications and agents can call.
How long does a pilot take and how is it priced?
One audience with one or two sources typically reaches a closed-group pilot in 8 to 12 weeks, most of it spent on the question set and permission mapping. Discovery and pilot are a fixed fee; production is priced per source and audience as a fixed scope, or as a dedicated team, with model and hosting costs passed through.
Bring us 100 questions your people keep asking
We will index the documents that should answer them, enforce your permissions, and show you the recall and groundedness scores before anyone outside the pilot group asks a thing.