An enterprise knowledge assistant is a conversational AI search tool that answers employee questions from your own documents using retrieval-augmented generation (RAG), cites the source for every answer and only retrieves content each user is already allowed to see.
Book discovery callPolicies sit in SharePoint, procedures in Confluence, decisions in tickets and email, and search in each tool only sees its own silo.
A model without grounding in your documents answers confidently from general knowledge, and nobody can check where the answer came from.
Your risk team asks whether an intern could surface HR or board documents through the assistant, and the prototype can’t prove otherwise.
Documents change weekly, but the pilot’s index was loaded once and nobody knows which answers now cite outdated versions.
We connect SharePoint, Confluence, Google Drive, ticketing systems, file shares and databases through scheduled and event-driven sync. Incremental updates and deletions flow through, so the index reflects what your systems hold today, not what they held at launch.
We parse PDFs, slides, spreadsheets and scanned files with layout-aware extraction, OCR and table handling. Chunking follows document structure such as headings and clauses, with metadata for owner, version and date attached to every passage.
Keyword search catches exact terms like part numbers and policy codes, while vector search catches meaning. We combine both, then re-rank results so the passages passed to the model are the ones most likely to answer the question.
Access rights from your identity provider and source systems are applied before retrieval, not filtered afterwards. The model never reads a passage the user couldn’t open directly, so restricted content can’t leak into a summary.
Every answer links to the exact documents and sections it used, and the assistant says it doesn’t know when retrieval finds nothing relevant. Users verify claims in one click instead of trusting the model’s wording.
We build a test set of real employee questions with expected sources and score retrieval accuracy, groundedness and citation correctness on every change. Feedback buttons in the interface feed new failure cases back into the set.
The assistant runs in Teams, Slack, your intranet or an existing support tool, with single sign-on. Usage analytics show which questions go unanswered, pointing your content owners to documents that are missing or out of date.
Share your main sources and we’ll assess coverage, access rules and parsing risk.
Request a readiness checkWe collect real questions from the teams who’ll use the assistant and map which systems hold the answers. We document the access model for each source and agree on quality thresholds.
Deliverables: question set, source inventory, access model, quality thresholds
We build connectors, parsing, chunking and hybrid retrieval inside your cloud tenancy. Retrieval is scored against the question set before any answer generation is tuned.
Deliverables: connectors, search index, retrieval evaluation, permission tests
A pilot group uses the assistant in Teams, Slack or your intranet while we review failed answers daily. We fix retrieval gaps and flag missing content to its owners.
Deliverables: pilot deployment, feedback loop, answer quality report, content gap list
We widen access, set up monitoring for index freshness and cost, and pair with your engineers on changes. You keep the pipeline, evaluation set and runbooks.
Deliverables: production rollout, monitoring dashboards, runbooks, trained team
One question searches every connected source, and hybrid retrieval matches both exact terms and meaning.
Every answer cites its source passages, and the assistant declines when retrieval finds nothing relevant.
Source-system permissions are enforced before retrieval, so users only get answers drawn from documents they can already open.
Unanswered-question analytics and version metadata show content owners exactly which documents to write or update.
A scored question set reruns on every change, so model or chunking updates are judged on results, not impressions.
Ready to see this in your own workflows?
Book discovery callEach route trades speed against control over retrieval, permissions and where your data is processed.
| Criterion | Built-in suite copilot | Enterprise search SaaS | tkxel FDE pod build |
|---|---|---|---|
| Source coverage | Mostly the vendor’s own suite | Prebuilt connector catalogue | Your systems, including custom ones |
| Permission handling | Native within its suite | Connector-dependent sync | Enforced before retrieval, tested |
| Retrieval tuning | Little to none | Limited configuration | Full control, scored per change |
| Data location | Vendor cloud | Vendor cloud | Your cloud tenancy |
| Ongoing cost model | Per-seat licence | Per-seat or usage licence | Your infrastructure and model usage |
| Fails when | Knowledge lives outside suite | Custom systems lack connectors | No one owns content quality |
“Placeholder quote — replace with a real client quote.”
“Placeholder quote — replace with a real client quote.”
“Placeholder quote — replace with a real client quote.”
We build in your repositories and environments from day one, so there is no separate rebuild between proof of concept and release.
Each pod is led by an engineer who has shipped AI systems before, drawn from tkxel’s team of around 150 AI professionals. The same people stay through delivery.
Autonomy matrices, audit trails, and human review are designed with your risk and security teams before release, backed by ISO 27001 controls.
Every engagement starts with agreed business metrics and evaluation sets. We report against them throughout, and the pod exits when your team can run the system.
“Placeholder testimonial — replace with the global set from tkxel.com.”
“Placeholder testimonial — replace with the global set from tkxel.com.”
An enterprise knowledge assistant is an AI tool that answers employees’ questions in plain language using your organisation’s own documents and systems. It uses retrieval-augmented generation: it first searches your connected sources for relevant passages, then has a language model write an answer from those passages with citations. A production assistant also enforces each user’s existing access rights, so it can’t reveal content from documents that person couldn’t open directly.
Retrieval-augmented generation (RAG) is a method where an AI model answers from documents retrieved at question time rather than from its training data alone. For enterprise search, that means answers reflect your current policies and procedures, and each answer can point to its source. It also means you can update knowledge by changing documents instead of retraining a model. The quality of a RAG system depends more on retrieval, parsing and chunking than on which model writes the answer.
We enforce permissions before retrieval, so the model only ever reads passages the user is already allowed to open. Access rights are synced from your identity provider and from each source system, and attached to every indexed passage. Filtering citations after the answer is generated isn’t enough, because the model has already read the restricted text and can paraphrase it. We include permission test cases in the evaluation suite and run them on every release.
Yes, the risk is reduced but not eliminated. Grounding answers in retrieved passages, requiring citations and instructing the assistant to decline when retrieval finds nothing relevant all lower the rate of invented answers. Errors still happen when source documents contradict each other, are outdated or are parsed badly, such as complex tables. That’s why we score groundedness on a test set, show citations on every answer and recommend users verify high-stakes answers against the cited source.
Most document and knowledge systems can be connected, including SharePoint, OneDrive, Confluence, Google Drive, Notion, ServiceNow, Jira, Zendesk, file shares and SQL databases. Where a system has an API, we build a connector that syncs content, metadata and permissions incrementally. Where it doesn’t, we work from exports or database access agreed with your IT team. We prioritise sources by how many real questions they answer, rather than connecting everything at once.
Buy if most of your knowledge lives inside one vendor’s suite and its built-in assistant meets your accuracy and security needs. Build when your answers depend on custom systems, you need control over retrieval quality, or data must stay in your own cloud tenancy. Many organisations do both: a suite copilot for general productivity and a custom assistant for a domain such as support, operations or compliance where accuracy is tested and owned.
A tkxel FDE pod typically takes 7 to 11 weeks from scoping to a production rollout for one domain. That includes one to two weeks to collect real questions and map sources, three to four weeks to build and score retrieval, two to three weeks of pilot with real users and one to two weeks of rollout and handover. Messy source content, such as scanned files or conflicting versions, is the most common reason timelines extend.