Customer support deflection
The recurring questions — status, policy, how-to, eligibility — answered from your published material with the source shown.
The problem
Most deployed chatbots fail in one of two ways. Either they follow a decision tree and cannot answer anything the author did not anticipate, so people learn to type "agent" immediately; or they are a general model with no grounding, and answer confidently about a policy that does not exist. Both erode trust faster than they save cost, and the second creates a liability the first never could.
Who this is for
- Customer service and contact-centre leadsRepeat questions handled well, and the rest reaching a person with context
- Internal service desks — IT, HR, facilitiesStaff self-serving from the current policy rather than the one they remember
- Digital and product ownersSomething answerable for what it says, in Arabic and English
What people use it for
Internal help desk
HR, IT and facilities questions answered against current policy, respecting who may see what.
Guided transactions
Where the assistant needs to do something — check a record, raise a ticket, book a slot — inside defined permissions.
Voice channels
The same grounded answers over a phone line, with speech recognition tuned for Arabic and English callers.
What it needs to work
- Your source material — help centre, policies, product documentation, past tickets
- Which systems it may read from, and under whose permissions
- The handover rules: when a person takes over, and to which queue
- A named owner for the content, because the assistant is only as current as it is
How it works
-
Understand the question
In Arabic, English or a mix of both, including the way people actually write rather than the way documentation does.
-
Retrieve before answering
The relevant passages are found first and the answer is written from them, which is what makes it checkable.
-
Answer with the source
Citing the document or article behind it, so a user or a supervisor can verify without asking anyone.
-
Act where permitted
Looking up a record or raising a ticket through defined integrations, never by improvising an action.
-
Hand over cleanly
On low confidence, on request, or on a defined topic — with the transcript attached so the customer does not repeat themselves.
-
Report what it could not answer
The unanswered questions are the content backlog. A bot that hides them cannot improve.
How we deliver it
-
Content and scope review
What it should cover, what material answers it, and what is explicitly out of scope and handed straight over.
-
Build and evaluate
Grounded on your corpus and measured against a question set written by your service team, not by us.
-
Integrate channels and handover
Web, messaging or voice, wired to your service desk with the handover tested under load.
-
Pilot and widen
One audience or topic first, watched closely, then widened as the unanswered list shrinks.
Where it runs
- Private cloud on your tenancy, or fully on-premises with open-weight models
- Embedded in your website, app, WhatsApp or service-desk channel
- Voice over your telephony platform where the phone is the channel
Security and governance
- Answers are grounded and cited; ungrounded questions are declined and handed over
- Retrieval respects your permissions — it cannot surface what the asker may not see
- Conversations logged for review, with retention you set
- No commitment, price or eligibility decision issued without a defined human step
Timeline
The build is fast; getting the content right is the project. Where a help centre is current and well owned, a pilot can be live and measured quickly. Where the answers live in three places and disagree, that has to be fixed first — and it is worth fixing regardless, because your agents are already working around it.
What you receive
- A working assistant on your channels, grounded and citing sources
- An evaluation set and measured answer quality, re-runnable after any change
- Handover integrated into your service desk with transcripts
- A dashboard of what it answered, deflected and could not answer
- The content backlog it generates — which is half the value
Related work
Published projects where we did this.
What this does not do
It answers from your material and cannot know what is not written down. It should not be the thing that quotes a price, confirms eligibility or accepts a complaint that carries a legal deadline — those need a person, and we will design the handover rather than pretend otherwise. And a bot cannot compensate for a help centre nobody maintains.
Questions we are asked
How well does it handle Arabic dialects?
It is built for how people actually write, including dialect and mixed Arabic–English, and that is tested on your own transcripts rather than assumed. Where a phrasing consistently fails, it goes into the evaluation set and gets fixed.
What stops it inventing an answer?
Retrieval-first architecture, citation, and declining when nothing adequate is found. Those are design choices, not settings — a model asked to answer from nothing will always produce something.
About the figures on this page
This page describes capability and method. It does not publish accuracy figures, throughput numbers or delivery dates, because those depend on your data, your systems and your scope — and a number published here would be wrong for most readers. You get them, in writing and against your own data, at scoping.
A first call is a technical conversation, not a pitch: what you have, what you need, and whether this is the right approach at all.
Key capabilities
- Banking
- Telecom
- Retail
Built on the Unified Intelligence Layer
Every InsAI product runs on the same four-stage backbone.
- 1
Data Integration
ERP · IoT · BIM · CRM
- 2
AI Models & Predictive Engines
Forecasting, detection, optimization
- 3
Automation & AI Agents
Acting on predictions, end to end
- 4
Real-time Dashboards & Decision Systems
From the floor to the boardroom
AI-Powered Chatbot
Voice + text, 24/7.