Agent · company knowledge
Company knowledge is an asset, not «ask Masha».
You have regulations, instructions, contracts, policies, templates. They exist. They were written. But in real work employees don't search them for answers — they ask colleagues. Because asking is faster. The RAG agent turns corporate documents into a live interface: ask — get an answer with a source link, respecting access rights. This isn't «a corporate wiki nobody reads». This is a working interface to company knowledge.
Knowledge doesn't leak with employees«Masha quit» stops being the team's problem — her knowledge in the documents is already accessible via the agent.
Onboarding a new hire — in daysBefore — weeks of «getting used to» the processes, constant questions to colleagues. Now — ask the agent, get an answer from the regulation with a link.
Audit of «what's outdated»The agent sees which documents contradict each other, which are stale, what questions have no answer in the base. This is a map of the company's knowledge gaps.
The base updates itselfNo need for a team manually keeping regulations up to date. Enrich the base once — then it lives through integrations and through links with other AI agents (Sales, Back-office, Support).
Why we built this — and why corporate wikis don't work.
Over the last 10 years almost every company we worked with went through the same: «let's build a corporate knowledge base». Confluence, Notion, corporate portal, a drive with folders. The base is created, populated, and three months later people stop using it. Not because it's useless. But because finding an answer in it is slower than asking a colleague.
Meanwhile «ask a colleague» is the worst way to distribute knowledge in a company. The colleague gets distracted, makes mistakes, quits, and doesn't scale. When «Masha» leaves — knowledge leaves with her. The team finds out that half of what she knew isn't written down anywhere.
The RAG agent solves both: makes searching the base faster than asking a colleague (seconds, with a source link), and stops depending on who's in the office today. Knowledge becomes a company asset, not a «consumable» that disappears at every resignation.
And why now
A knowledge base isn't «a folder of regulations», it's capital that compounds.
Each year of the agent's work is a year when documents get ordered, contradictions surface, gaps close, answers to typical questions improve. After 2 years you have not «a knowledge base», but a real working intelligence layer of the company. Latecomers won't have it — models can be bought, an accumulated knowledge base with question history — cannot.
Six effects — at the company level.
The RAG agent doesn't change one employee's work. It changes how knowledge lives in the company: how accessible it is, how tied to people, how it turns into a shared resource instead of «Masha's head».
Knowledge doesn't leak with people changing
«Masha quit, now we don't know how to handle objection N» — no longer a story about your company. Knowledge lives in documents and is available via the agent regardless of who's on the team today.
Team speed grows
Before: question → work interrupted → find Masha → wait → get an answer. Now: question → answer → keep working. This small delta over a month — tens of hours saved across a team of 20.
Onboarding becomes cheaper
Before, a new hire went through 2-3 months of «getting used to it», constantly distracting colleagues. Now — they ask the agent, get answers from regulations with source links. Colleagues aren't interrupted. Time-to-productivity — weeks, not months.
Knowledge base gaps become visible
The agent logs questions it couldn't confidently answer. That's the «gap map»: which topics are poorly documented, which docs are stale, which contradict each other. Without it you didn't even know your base lacks an answer to a typical question.
Compliance and audits — without pain
«Show me which regulation the employee followed when making this decision» — now this is a query, not an archaeological dig through chats. Every answer is tied to a source and recorded in the log.
The knowledge base becomes a living asset
After a year, your documents aren't just «there» — they're structured, verified, cleared of contradictions, indexed by real employee questions. You can't buy this — it accumulates through the agent's and team's work.
Five changes — for the employee at work.
The company's most expensive resource is employee time. The RAG agent returns it through five daily changes.
Stops wasting time on «where does this live»
Before: 15 minutes searching for the regulation across 5 Confluence folders. Now: question in chat, link to the specific section of the document.
Stops being afraid to bother colleagues with basic questions
A junior's typical questions — «how to file expenses», «where to request leave», «which contract template for an individual» — close in the chat with the agent without social cost.
Gets an answer with a link — can verify
Not «the agent said so», but «here in section 4.2 of the regulation». You can open, read, discuss with your manager, cite in a client email.
Sees when there's a contradiction in the base
The agent honestly says: «there are two documents with different answers — need to check with the process owner». Better than «Masha says one thing, Pete says another».
Doesn't lose access rights — the agent respects them
If an employee doesn't have access to financial regulations — the agent won't pull answers from those docs. No «accidentally saw someone else's contract» — document permissions apply in the chat too.
Ten actions — from search to auto-updating the base.
So the effects don't sound abstract — ten specific actions: search, source control, keeping the base healthy, role-specific scenarios and — most importantly — auto-updating through other modules, with no manual maintenance.
· Search and answers
Searches documents and the knowledge base
Semantic (RAG) search, not «exact match». Asked «how to take a day off» — found it even if the doc calls it «one-day leave of absence».
Answers in natural language, not «document excerpts»
Not «item 4.2.1 of chapter 7» — but «you need to submit via the corporate portal at least 3 days in advance, weekends don't count». Concise, with a citation.
· Source control
Shows the source for every answer
Under each answer — a link to the specific doc and section. You can open, verify, share with a colleague. No «agent said» without backup.
Respects the employee's access rights
If you don't have access to financial regulations — the agent won't pull from them. Permissions from the corporate drive and DMS apply in the agent chat the same way.
· Knowledge base health
Finds contradictions and stale documents
If two regulations give different answers — the agent honestly says so and escalates to the process owner. If a doc is from 2019 and contradicts a fresh policy — it's flagged.
Logs unanswered questions
Every question the agent couldn't confidently answer — gets logged. This is the «knowledge base gap map»: what needs to be written, what the process owner should address.
· Scenarios and roles
Helps onboard new hires
Ready-made «first week at the company», «how to onboard paperwork», «what stack we use» scenarios — no need for the newcomer to remember or search.
Different interfaces for different roles
Employee asks in Telegram, support — in helpdesk, HR — on the portal, developer — in an IDE plugin. One agent, different entry points adapted to work habits.
· A living base — updates itself
Enriched once — keeps itself up-to-date
The key difference from a classical corporate wiki: you don't need a team manually updating regulations, instructions and the base. After the initial load, the base lives through integrations — documents from Drive, changes in Confluence, new contract versions are pulled in automatically. Stale items get flagged, contradictions escalate to the owner.
Plugs into other AI agents and pulls data from them
If Sales, Back-office or Support agents are already running — the Knowledge agent takes live data from them: new deals, processed acts, typical customer requests. The more modules connected — the more current and broad the base becomes, without your involvement. One shared knowledge layer for the entire stack.
What we plug in — and what you get
The Knowledge agent collects company knowledge into one window. Left — what we plug in at start, and which modules then update the base automatically. Right — how employees get answers.
A living knowledge base
Enriched once — then updates itself through integration with other modules. No manual maintenance needed.
- Indexes knowledge once — then keeps it up-to-date itself
- Pulls fresh data from Sales, Back-office, Support and other modules
- Answers questions with a link to the up-to-date source
- Searches by meaning, not exact match
- Highlights contradictions and outdated regulations
The key difference from a typical «corporate wiki»: you don't need a team manually updating regulations and instructions. The more other modules connected — the more up-to-date the knowledge gets without your involvement.
Where we plug in — six layers of knowledge.
The knowledge agent works with documents where they live now — nothing needs to be moved to a new system. You pick the specific sources, interfaces and controls — we help with architecture and order of connection.
Document sources
Where the agent draws knowledge from. We connect what you already have — not «let's migrate everything to a new system».
Knowledge layer
The technical layer between documents and the answer. This turns «we have documents» into «ask and get an answer with a link».
Storage
Where vector indexes, question logs, answer history live. Usually in our perimeter, in yours on request.
Interfaces for employees
Where the employee asks the question. Different entry points for different roles.
Control and audit
So knowledge doesn't «leak» and the agent's quality is visible. Permissions from your systems are respected, logs are available to the DPO.
AI models
For understanding natural-language questions. External models — fast. Russian/local — if regulation requires.
For the pilot a limited base (one department or one document type) and one entry point are needed — usually Telegram or an internal portal.
Three levels — not three pricing tiers.
The RAG agent costs more than Sales and Personal because under it sits a whole knowledge engineering layer: parsing documents of various formats, embeddings, vector search, reranking, access permission control from your systems, context handling. Not «set up a chat bot in a week».
Minimum working contour
Limited document base (one department / one regulation type). Knowledge structure preparation. Basic RAG search. Answers with source links. 2–3 typical-question scenarios. Tested on real employee queries.
Pilot goal — in 2-3 weeks you understand whether employees actually start using the agent instead of colleagues. If they don't — we'll say so.
Knowledge base for the whole company
- ▪Extended base — all main document categories
- ▪Regular updates when documents change
- ▪Full access permissions from corporate systems
- ▪Question logs and answer quality scoring
- ▪Improved search with reranking if needed
- ▪Integration with multiple corporate storages
- ▪Interfaces for different roles (Telegram, portal, helpdesk)
Sensitive documents, contracts, PII
If the base contains commercial secrets, counterparty contracts, employee PII, internal regulations under NDA — that's enterprise. Local model in a closed perimeter, private deployment, audit logs, strict access segregation, fine-tuning for your terminology.
Footnote · maintenance
From 40K RUB/month
Documents get added, changed, become stale. Employees ask new questions. The base sometimes needs a «cleaner». Maintenance covers: reindexing when new documents appear, handling contradiction/staleness flags, answer quality adjustments based on feedback, adapting to new model versions, the local model within the limit.
When the RAG agent isn't your fit.
If you don't have documents or they're only in people's heads — you need to write them first. The RAG agent works with what exists, not «pulls knowledge» from where there isn't any. If regulations aren't written — first business analysis and process description, then the agent.
If there are 5 people and everyone knows everything — the agent is overkill. The RAG agent delivers value at scale: dozens of people, regular churn, new hires every month. For 5 people it's cheaper and faster to write a one-page FAQ.
If you expect «an infallible expert with no hallucinations» — no such thing exists. The RAG agent gives an answer with a source link — but if the document is wrong, the answer will be wrong too. Responsibility for base quality stays with process owners in your company. We can highlight gaps, but we won't fix a document for you.
If you want to relax access controls for «convenience» — this isn't our format. Permissions from your corporate systems apply in the agent chat too. If someone shouldn't see financial regulations — they won't see them in the agent. No «let's give everyone access to all documents in the chat».
If any of the above describes you, mention it on the first call. We'll either propose a different configuration or honestly say other steps are needed first.
We don't just deploy — we teach people to use AI properly.
In 2–3 years AI tools will be the standard in engineering and business teams. Whoever starts learning now will gain a competitive advantage that latecomers won't have. That's why we have a separate direction: training client teams, so that AI tools and agents don't become «a toy for 3 months», but turn into part of the working process.
Corporate programs for adopting AI in development
For company engineering teams that want to switch to the AI-native approach. A 2–4 week program: review of the team's stack, selecting AI tools for the tasks, training on using them, ramp-up to regular production use. Not a «theoretical course», but a real shift in how the team works.
CTO, tech leads, developers · teams 5–50+
Client team onboarding for working with AI agents
Once we've deployed Sales, Personal, Support, RAG or another agent — we train your team to work with it well. How to adjust scenarios, write prompts for typical tasks, handle complex cases, give the agent feedback for improvement. Without this even a well-deployed agent gets «abandoned» in 3 months.
Client operational teams · typically after agent deployment
«Where to start with AI» mentor sessions for executives
Short individual sessions with CEOs, CTOs, business owners: which AI tools actually work, where to start in your company, how to link AI to business goals, how not to burn budget on «let's try something trendy». Sessions — 90 minutes, with a concrete roadmap as the output.
CEO, CTO, owners · 90-minute sessions
Open learning materials in the Telegram channel
A free column in our Telegram channel @dxaiblog. AI tool reviews, practices from real projects, mistakes and how to avoid them, reviews of new models and platforms. Anyone can subscribe — engineer, owner, product manager. This is the public part of our education, no commitment.
Anyone exploring AI · public channel · free
Training cost is discussed individually — depends on team size, format (online/hybrid/corporate visit), program depth, and whether you need a process built from scratch or accompaniment of an existing one.
If you want to start small — subscribe to the Telegram channel @dxaiblog. All materials there are free. That's our «step zero» of training.
Describe your knowledge base — we'll respond with an analysis within 2 hours
We reply within 2 hours during business hours. In the pilot call we'll show where the agent pays off and where it shouldn't be tried.