AI assistants that answer your customers from your own data
Trained on your docs, policies, and catalog — live in days, every answer shows its source, and tricky questions hand off to your team.
- Live in days, not months
- Every answer shows its source
- Hands tricky chats to your team
Sample conversation
Source: Shipping policy
Sample — not a live assistant
What a conversation looks like
A scripted example for a fictional online store — showing how answers cite a source, pull live data, and know when to hand off.
Customer
What's your return policy on electronics?
Assistant
Electronics can be returned within 14 days if unused and in original packaging.
Source: Returns policy
Customer
Where's my order #48213?
Assistant
Order #48213 shipped yesterday and is expected to arrive within 3 business days.
Source: Order lookup (live system)
Customer
Can you give me a discount if I buy 50 units for my office?
Assistant
I'm not able to approve bulk pricing — I've forwarded this to the sales team, and someone will follow up within one business day.
Source: Handed off to your team
Approaches
RAG types I build
Basic RAG
Answers questions by searching your documents and generating a reply grounded in what it finds.
Best for: Straightforward FAQs, policies, and product info.
Not for: When you need the assistant to take actions, not just answer questions.
Hybrid RAG
Combines keyword search with semantic search, so exact terms (SKUs, order numbers) and fuzzy meaning both work.
Best for: Large catalogs or document sets where precision matters.
Not for: Small FAQ sets where basic search is already accurate enough.
Agentic RAG
Can call tools — look up a real order, capture a lead, or hand off to a human — not just answer from text.
Best for: Order status, lead capture, anything needing a live action.
Not for: Simple static FAQ pages with no systems to connect to.
Advanced options
Graph RAG
Maps relationships between entities in your data for multi-hop, connected questions.
Best for: Complex internal knowledge bases with many linked records.
Not for: Most small businesses — this is usually overkill.
Multimodal RAG
Understands images and diagrams alongside text, not just written documents.
Best for: Product photos, manuals with diagrams, visual catalogs.
Not for: Purely text-based support content.
Pricing
Packages
Pilot
TODO
one-time setup
- ✓ Basic RAG on your existing docs
- ✓ 1 integration (e.g. website chat)
- ✓ 2 weeks of support
Setup + Launch
TODO
one-time setup fee
- ✓ Hybrid or Agentic RAG
- ✓ Order lookup / lead capture tools
- ✓ Human handoff built in
Monthly Care
TODO
per month
- ✓ Ongoing monitoring
- ✓ Content updates as your docs change
- ✓ Monthly accuracy report
Quick check
Which one fits me?
What do you mainly need?
Roughly how many conversations per month?
Do you want ongoing monitoring and care?
Your estimate
Rough savings calculator
This is only an estimate based on the numbers you enter below — not a guarantee.
Estimated hours saved / week
5.0
Estimated savings / month
$217
Case studies
Recent work
Bankly AI Assistant
Customers get instant answers on loan eligibility, repayment calculations, and banking FAQs no waiting on a human agent, any time of day.
AI E-Commerce Shopping Assistant
Shoppers describe what they want in plain language and get personalized product matches instantly instead of scrolling through filters and categories.
Hot Freight International
This RAG-based assistant answers questions for Hot Freight International, a Canadian freight and warehouse partner. It covers the company's freight options (over-the-road, air, rail and ocean), distribution and warehousing, tradeshow delivery, and shipping across Canada, the USA and the border. It answers from the company's knowledge base, so visitors get quick, consistent replies, and it points them to the team when they need a quote or a booking.
GlobalDigitalNexus
GlobalDigitalNexus Chat is a RAG-based assistant that answers visitor questions from the company's own project knowledge. It gives quick, consistent answers about what the business does and its services, and can point visitors to the right next step, such as getting in touch. It is one example of how the same assistant architecture can be adapted to a different brand.
MFSYS (Microfinance Systems)
MFSYS Chat is a retrieval-augmented (RAG) assistant built on project knowledge from MFSYS. Instead of guessing, it finds the relevant project information first and answers from it, so replies stay grounded in the source material. It explains what the project covers, how its parts fit together, and what it's meant to do, in plain language.
Who it's for
Industries
FAQ
Common questions
How long does setup take?
A basic assistant can be live in days once I have access to your documents or policies.
What happens when the assistant doesn't know the answer?
It says so honestly and hands the conversation to your team, rather than guessing.
Can it look up real order data?
Yes, with the Agentic RAG approach — it can connect to your order system to pull live status.
Book a free call
Tell me what your customers keep asking — I'll reply with how this could work for you.