Why not just buy an off-the-shelf AI tool?

A product encodes one opinion about how the work should be done. Where your process differs, the gap becomes permanent manual effort. We start from your process instead.

How long until something is in production?

Two to three weeks to a documented architecture, then four to six to a first increment running in shadow mode against real data.

Where does our data go?

Wherever you decide — your cloud account or ours, with region and retention as explicit decisions. We do not train models on your data, and every system ships with an audit log.

Yes. Source lands in your repository from the first commit, with the architecture docs, evaluation suite and runbooks. Taking it in-house later requires nothing from us.

Discovery and architecture are a fixed fee. Build work is quoted as fixed-scope phases, so you approve cost per phase rather than signing an open-ended commitment.

Do we have to reorganise our documents first?

No, and we would advise against making that a prerequisite — it is the step that kills most of these projects. We index what exists in place. Poorly structured sources do lower retrieval quality, but the evaluation set tells us precisely which corpora are weak, so cleanup becomes a targeted, evidence-led task rather than a boil-the-ocean one.

How do you stop someone seeing what they should not?

Permissions are resolved at query time against your existing identity provider and the source system's own access rules, then applied as a filter before retrieval. The assistant physically cannot return a passage the asker could not already open. Every access is logged.

What stops it making things up?

It answers only from retrieved passages, and every claim carries a citation — an uncited answer is treated as a defect, not a stylistic choice. Below the confidence threshold it declines and routes to a person. We measure this explicitly: the evaluation set includes questions your documents genuinely do not answer, and refusing them correctly is a pass.

How current are the answers?

Connectors watch sources for changes and re-index on write, so a policy updated this morning is answerable this morning. Answers surface the source document's date, which matters more than people expect — it lets the reader judge whether they are looking at the current version.

Can it handle scanned documents and spreadsheets?

Yes. Scanned PDFs go through layout-aware OCR, and spreadsheets and tables are parsed with structure preserved rather than flattened into a wall of text — which is why table lookups work at all. Quality varies with scan quality, and the evaluation set will tell you where it does.

How is this different from the AI search built into our tools?

Built-in search only sees its own product. The value appears when an answer requires a policy in SharePoint, a resolved ticket in Zendesk and a Slack thread from last year — none of which can see each other. A single permission-aware layer across all of them is the thing no individual vendor will build for you.

What we buildKnowledge & Retrieval

The answer already exists. This is how people finally find it.

Your policies, contracts, runbooks and threads hold the answer to almost every question your team asks. A retrieval system makes them answerable in a sentence — with the source attached, and only for the people allowed to see it.

Test it on your own documents How it works

Answer with citations — screen recording

public/knowledge/answer.mp4

What we commit to

Cited: 100%; Every answer links the passage it came from. Unsourced claims are a bug.
Permissions: Mirrored; If you cannot open the file, the assistant cannot quote it to you.
When unsure: Refuses; Below the confidence threshold it says so rather than filling the gap.
Training on your data: 0; Your documents are retrieved from, never used to train a third-party model.

01Built around where your knowledge actually lives

No migration. No 'first, consolidate everything into one wiki.'

Every failed knowledge project starts with a tidying exercise nobody finishes. We index what you have, where it is, in whatever state it is in.

Retrieval Layer

Permission-aware · Always cited

Where it reads from

Connectors pull from the systems your documents already live in, and keep up as those documents change.

Document stores: Google Drive, SharePoint, OneDrive, Dropbox, Box — folders and permissions intact.
Wikis and tickets: Confluence, Notion, Jira, Zendesk, ServiceNow — including resolved-ticket history.
Files nobody has cleaned up: Scanned PDFs, spreadsheets, slide decks and contracts, parsed with layout preserved.
Conversations: Slack and Teams channels, where a surprising share of institutional knowledge only exists.

Where people ask

Answers arrive in the tool someone already has open, not in a new tab they must remember to visit.

Slack and Teams: Ask in the channel where the question would have been asked anyway.
Inside your helpdesk: Suggested answers with sources, drafted for an agent to approve rather than write.
Your own product: An API returning grounded answers with citations, for the interface you already ship.
A search page: When a plain, fast, permission-aware search over everything is what is actually wanted.

02How an answer is produced

Retrieval first, generation last

01
Ingested
Documents pulled from source with their permissions, version and last-modified date carried along.
02
Parsed
Layout-aware extraction keeps tables as tables and headings as structure, rather than flattening to soup.
03
Indexed
Chunking strategy chosen by evaluation per corpus — a contract and a chat thread do not split the same way.
04
Retrieved
Hybrid keyword and semantic search, then reranked, and filtered to what this specific person may see.
05
Grounded
The model answers only from the retrieved passages. Nothing outside them is treated as knowledge.
06
Cited
Every claim links back to its source passage, so the reader can verify in one click.

03The hard parts

Why a weekend RAG prototype does not survive real documents

Retrieval is easy to stand up and hard to make trustworthy. These six decide whether people keep using it after the novelty passes.

01
Permissions
The fastest way to kill a knowledge project is one person seeing a salary review. Access is resolved per query against your existing identity provider, not approximated at index time.
02
Staleness
A superseded policy quoted with total confidence is worse than no answer. Sources are re-indexed on change, and answers carry the date of the document they came from.
03
Chunking
Split a contract mid-clause and retrieval returns half an obligation. Strategy is chosen per corpus by measurement, not by copying a default from a tutorial.
04
Retrieval quality
Pure vector search misses exact terms — part numbers, policy codes, names. Hybrid search plus reranking is what makes the right passage actually surface.
05
Refusing to answer
The system is tuned to say 'this is not covered in what I can see' and point to a person. A confident invention costs more than an admission.
06
Proving accuracy
An evaluation set built from your real questions with known-correct answers, regression-tested every release, so quality is a number rather than an impression.

04Where it earns its place

Anywhere the same question is answered by hand more than twice a day

The strongest first corpus is the one whose questions already flood a shared inbox or channel.

Support deflection
Repeat questions answered from documentation, with the source shown so agents trust it.
HR and policy
Leave, expenses and benefits answered per employee, with regional variants respected.
Sales enablement
Pricing, security posture and competitor answers pulled from approved material, not invented on a call.
Engineering runbooks
Incident procedures surfaced under pressure, when nobody has time to search Confluence.
Contract and policy lookup
Obligations, renewal dates and clauses found across thousands of documents in seconds.
Onboarding
New starters asking freely without spending their first month interrupting colleagues.

Screenshot — in context

public/knowledge-retrieval/screen-1.png

Screenshot — result

public/knowledge-retrieval/screen-2.png

05FAQ

What teams ask before they commit

No, and we would advise against making that a prerequisite — it is the step that kills most of these projects. We index what exists in place. Poorly structured sources do lower retrieval quality, but the evaluation set tells us precisely which corpora are weak, so cleanup becomes a targeted, evidence-led task rather than a boil-the-ocean one.

06Also built here

The rest of what we do

Next Step

Let's build your next system.

Bring us the process costing your team the most hours. We'll tell you honestly which parts are worth automating.

Book a Strategy Call contact@araxys.dev

Prefer to talk now?+91 89391 53390+91 63806 69805

Fixed-scope phases
You own the source
NDA on request
No obligation

The answer already exists. This is how people finally find it.

What we commit to

No migration. No 'first, consolidate everything into one wiki.'

Where it reads from

Where people ask

Retrieval first, generation last

Ingested

Parsed

Indexed

Retrieved

Grounded

Cited

Why a weekend RAG prototype does not survive real documents

Permissions

Staleness

Chunking

Retrieval quality

Refusing to answer

Proving accuracy

Anywhere the same question is answered by hand more than twice a day

Support deflection

HR and policy

Sales enablement

Engineering runbooks

Contract and policy lookup

Onboarding

What teams ask before they commit

The rest of what we do

AI Agents

Voice Agents

Process Automation

Integrations

Custom Software

Let's build your next system.