AI Agents
Agents scoped to one workflow, integrated with your systems, always reviewable by a person.
Solutions
Support that scales with volume, not with hiring.
Support teams spend most of their time on questions that have documented answers. We automate that layer properly — grounded in your own knowledge, with citations and honest escalation — so your people spend their time on the cases that genuinely need judgement.
The business problem
Response times drift upward as the customer base grows, and the only lever anyone offers is another hire. Meanwhile the knowledge needed to answer most tickets already exists — scattered across a wiki, a shared drive, an old email thread and one person's head.
- The same twenty questions account for most of the queue.
- First response time is the metric everyone is judged on and nobody controls.
- Knowledge is real but unfindable, so answers are re-derived every time.
Before and after
How it runs today
- A request arrives by email, chat or form and joins one undifferentiated queue.
- An agent reads it, works out the category, and searches for precedent.
- For anything unusual, they ask a colleague and wait.
- The answer is written from scratch, then not recorded anywhere reusable.
How it runs afterwards
- The request is classified and routed on arrival.
- Documented questions get a grounded answer with citations, immediately.
- Low confidence, policy topics and unhappy customers escalate to a person by design.
- The agent receives a draft with sources, and keeps the final say.
- Gaps in the knowledge base surface as a report instead of as repeat tickets.
How RAIS approaches it
Ground everything in your own content
The agent answers from your documented knowledge with citations, and says it does not know rather than inventing. An unsourced answer is a defect, not a feature.
Design the escalation first
We define what must always reach a human — refunds, complaints, contractual and legal topics, low confidence — before we define what the agent handles.
Assist before replacing
The usual first deployment drafts answers for agents rather than sending them. It builds trust, and the correction data is what makes full automation safe later.
Close the knowledge loop
Questions the system could not answer become a prioritised content backlog, so the knowledge base improves as a by-product of running support.
Services involved
Web Development
Corporate sites, product sites and conversion systems built as commercial infrastructure.
Related use cases
AI Customer Support
First-line answers grounded in your documentation, with citations and designed escalation.
Internal Knowledge Assistant
Let your team find the answer that already exists, with permissions respected and sources shown.
Customer Portal
Give clients their own data — documents, statuses, requests — instead of emailing it on request.
Document Processing
Extract structured records from invoices, contracts and forms, with a review threshold before anything is written.
Typical integrations
CRM
Read and write deals, contacts and activities so qualified enquiries land where sales already works.
Telegram
Bot-based notifications, approvals and conversational entry points for teams that already live there.
Microsoft 365
Identity, mail, calendars and document libraries, with permissions respected rather than bypassed.
Google Workspace
Sign-in, Drive, Sheets and Calendar as data sources and destinations for automated workflows.
REST API
Documented, versioned and authenticated endpoints — both consuming yours and exposing ours.
Frequently asked
Will customers know they are talking to an AI?
Yes. We disclose it, both because several jurisdictions require it and because pretending otherwise reliably backfires the first time the agent gets something wrong. Disclosure also makes escalation feel like a feature rather than a failure.
What if our knowledge base is a mess?
That is the normal starting position. Part of the engagement is working out what is actually authoritative and what is stale. It is genuinely useful work in its own right — the audit is often the part clients say they got the most out of.
Can it work in Russian and English?
Yes, and it is a common requirement. The practical constraint is that quality follows your source content: if your documentation is thorough in one language and thin in the other, the agent will be too. We measure per language rather than reporting one blended number.
Next solution
Process Automation
Remove the manual steps between systems that quietly consume a department's week.
