AI Agents & Automation · The reading room
Ask your company's knowledge, get a cited answer
We build private RAG over your internal knowledge: an embedding pipeline with a chunking strategy tuned to your documents, permissions-aware retrieval so each person sees only what they're cleared for, and citation grounding so every answer points back to its source.
Request slip
filed by Priya · Support desk
“What's our refund policy for enterprise annual plans?”
Returned from the stacks
Enterprise annual plans can be refunded pro-rata within the first 30 days[FIN·04], after which unused months apply as credit toward renewal[SUP·11].
Shelves consulted
Enterprise Billing Policy.pdf
Finance
Refund SOP — Support Playbook.docx
Support
Legal Terms Addendum 2025.pdf
Legal
The brief
Retrieval-augmented answers your team can actually trust
Internal knowledge AI only works if people trust it and it respects who can see what. We build the embedding pipeline, tune the chunking strategy so retrieval returns coherent passages, enforce permissions-aware retrieval so access rules carry through to answers, and ground every response in citations — so an answer is checkable and scoped to the person asking.
A private retrieval system over your internal knowledge — permissions-aware retrieval, tuned chunking, and cited answers so teams trust what it returns and see only what they may.
Card 01
Answers scoped to the asker
Permissions-aware retrieval carries your access rules into the AI, so an answer never surfaces a document the person asking isn't cleared to read.
Card 02
Every answer is checkable
Citation grounding points each response back to the source passage, so a user can verify the answer instead of trusting it blindly.
Card 03
Retrieval that returns the right passage
A chunking strategy tuned to your document structure means retrieval pulls coherent, relevant sections rather than fragments that lose their meaning.
The stacks
What we build behind the reference desk
Embedding & chunking
An embedding pipeline with a chunking strategy tuned to your documents, so retrieval returns passages that are whole and on-topic.
- Embedding pipeline
- Chunking strategy
- Re-embedding on update
Permissions-aware retrieval
Access controls enforced at retrieval time, so the system filters candidate passages by what the asking user is allowed to see.
- Access filtering
- Per-user scoping
- Document-level rules
Citation grounding
Answers assembled from retrieved passages with citations back to the source, so nothing is asserted without a checkable origin.
- Source citations
- Passage grounding
- Traceable answers
Evaluation & freshness
Evaluation of retrieval and answer quality, plus a pipeline that re-indexes as your knowledge changes so answers don't go stale.
- Retrieval evals
- Answer quality checks
- Index refresh
The accession
How your archive gets built and cataloged
Week 1
Map what you already store knowledge in
Wikis, drives, ticketing systems, and document stores get inventoried before any ingestion pipeline is built.
Week 2
Mirror your access rules
Permissions are mapped with your IT or security stakeholders, so retrieval respects the rules you already trust.
Week 3
Tune chunking and retrieval
Chunking is tuned to your actual document structure, so retrieval returns coherent passages instead of fragments.
Week 4
Evaluate against real questions
Retrieval and answer quality are measured with evals, and tuned wherever the system falls short.
The reference desk
From scattered documents to a system your team trusts
Inventory the knowledge
Map the sources, their structure, and the access rules that must carry through to every answer.
Build the pipeline
Set up the embedding and chunking so retrieval returns coherent, relevant passages from your material.
Enforce permissions
Wire permissions-aware retrieval so each user's results are filtered to what they're cleared to see.
Ground & evaluate
Add citations, run retrieval and answer evals, and set the index to refresh as knowledge changes.
The clearance
Three procedures the archive runs on
An archive is only trustworthy if the rules are set before the first request comes in. These three are built up front, long before a document could surface in front of the wrong person.
C·01Ingestion & chunking
The ingestion pipeline connects to wherever your knowledge already lives, and chunking is tuned to your actual document structure so retrieval returns coherent passages instead of fragments.
- Wikis, drives & ticketing systems
- Document-structure-aware chunking
- Coherent retrieved passages
C·02Permissions-aware retrieval
Access rules run at the moment of retrieval, never bolted on after. A restricted document is never a candidate passage in the first place.
- Retrieval-time permission checks
- Role-based access mirrored
- Restricted docs excluded upfront
C·03Evaluation & freshness
Retrieval and answer quality are measured against real questions, and the index re-embeds affected content as documents change so it doesn't quietly go stale.
- Evals against real questions
- Ongoing quality tuning
- Automatic re-embedding on change
The reading room
What it's like once the archive answers
Answers cite their source
Every answer is assembled from retrieved passages with a citation you can check, never an assertion you have to take on faith.
Restricted shelves never surface
A document outside someone's access is never a candidate passage. It isn't filtered out afterward; it's excluded before the search even runs.
The catalog doesn't go stale
Content is re-embedded as documents change, so answers stay current with what's actually true today.
Coherent passages, not fragments
Chunking tuned to your document structure means retrieval returns something a person can actually read.
Your archive stays yours
Deployments where your content isn't used to train a provider's model, run in your own environment if needed.
Quality measured, not assumed
Retrieval and answer evals are run and shared with you, so "it works" is something you can see.
Selected work
Archives that answer with their sources
What you get
A working archive, not a demo
We handle the ingestion, the permissions model, and the evaluation, then keep it current as your knowledge changes.
Private RAG system
The retrieval system over your knowledge, with permissions-aware retrieval and cited answers.
Ingestion & chunking pipeline
The embedding and chunking setup, plus the refresh process that keeps the index current.
Evaluation report
Retrieval and answer-quality evals showing how the system performs on your real questions.
Industry expertise
Wherever knowledge is scattered and access rules matter
Legal
Precedent, matter history, and firm policy answered with cited passages and matter-level access controls.
Healthcare
Clinical protocols and internal guidance retrieved with role-based access baked into every query.
Financial services
Policy and compliance documentation answered with permissions that mirror your existing access tiers.
Professional services
Institutional knowledge across engagements, searchable without re-doing research that already happened.
Engineering & manufacturing
Specs, SOPs, and technical documentation retrieved with citations engineers can verify on the floor.
HR & people ops
Policy and benefits questions answered consistently, with sensitive records kept out of reach of the wrong audience.
Sitting on knowledge your team can't easily find?
Tell us the sources and the access rules — we'll build retrieval that answers from them and respects who sees what.
Why choose us
Built by people who take access control as seriously as retrieval quality
Permissions enforced at retrieval
Access rules run at the moment of retrieval, never bolted on after. A restricted document is never a candidate passage in the first place.
Chunking tuned to your documents
We tune the chunking strategy to your actual document structure, so retrieval returns coherent passages instead of fragments.
Citation-grounded, always
Every answer is assembled from retrieved passages with a citation back to the source. Nothing is asserted without an origin you can check.
Evaluated, not assumed
Retrieval and answer quality are measured with evals against real questions, and tuned where the system falls short.
Freshness built in
The ingestion pipeline re-embeds affected content as documents change, so the index doesn't quietly go stale.
Your data stays yours
We build on deployments where your content isn't used to train a provider's models, and can run the system in your own environment.
Why work with Flaidex
A partner that scopes the access model before it scopes the demo
We scope access rules with your team
Permissions get mapped with your IT or security stakeholders up front, so the retrieval layer mirrors rules you already trust.
We integrate with what you already store knowledge in
Wikis, drives, ticketing systems, and document stores. We build the ingestion pipeline against what you actually use.
We report on evaluation, plainly
You see the retrieval and answer-quality evals we run, instead of just a promise that it works.
We keep tuning after launch
As your knowledge base grows and changes, we tune chunking and retrieval so quality doesn't drift down over time.
We're honest about fit
If a simpler search tool solves your problem, we'll say so instead of building a system you don't need.
We move on a defined timeline
A scoped engagement with a clear inventory, build, and evaluation plan, never an open-ended research project.
Questions
What teams ask before they trust it with real questions
What does RAG mean, and why does it matter for internal knowledge?
RAG — retrieval-augmented generation — means the AI retrieves relevant passages from your own content and answers from those, rather than relying on what a general model happened to learn. For internal knowledge, that's the whole point: the assistant speaks from your documents, cites where each answer came from, and stays current as your knowledge changes, instead of confidently reciting something generic or outdated.
How does the system respect who's allowed to see what?
Through permissions-aware retrieval. Your access rules are enforced at the moment of retrieval, so the system only ever considers passages the asking user is cleared to read. A person querying the assistant gets answers built from documents they already have permission to see — the AI doesn't become a side door that leaks restricted material to someone who couldn't open the file directly.
How do we know the answers are accurate?
Every answer carries citations back to the source passages it was built from, so a user can click through and verify rather than taking the response on faith. Beyond that, we run evaluations on retrieval and answer quality against a set of real questions, so we can measure whether the system is returning the right passages and grounding its answers well — and tune the chunking or retrieval where it falls short.
Does our data train anyone else's model?
No. A private RAG system keeps your documents in your own index and retrieval layer, and we build on model deployments where your content isn't used to train the provider's models. Your knowledge is used to answer your questions and nothing else. Depending on your sensitivity requirements, the whole system can run in your own environment so the data never leaves your control.
What happens when our documents change?
The index refreshes. We build the ingestion pipeline so that when documents are added, updated, or removed, the affected content is re-embedded and the index stays in sync. That keeps answers current — a policy that changed last week is reflected in what the assistant returns, rather than the system quietly citing a superseded version because it was indexed once and forgotten.
Let's talk
Running a large platform, shaping a first MVP, or getting a product ready for a funding round? Tell us where you are. We'll shape the process around it, and stay with you after launch.
