The AI systems we’ve built, what they do, how long they take, and the impact they’ve had.
Built for a North Vancouver rental company. A full year of orders audited in a single day, with thousands of dollars in unbilled revenue surfaced.
The company suspected revenue was being lost across a high volume of orders, but manually comparing a full year of CRM records, billing notes, customer correspondence, and invoices was not practical.
We deployed 15 AI agents to review the entire year in parallel. Each agent reconstructed what happened on an order, compared the agreed work with the final invoice, and flagged missed billings, incorrect amounts, and orders that had not been properly closed out.
Every finding came with an explanation and the supporting records, so the team had a prioritized list it could verify and act on with confidence. The same system can run continuously, checking new orders each day so billing problems are caught before revenue slips through the cracks.
A Python script pulled the order data from the company's SQL database and fed it to AI agents running in parallel. Each agent audited its own batch of orders separately and reported to a lead agent, which compiled the findings into a single report.
One day. The data was pulled, a full year of orders audited, and the findings compiled in a single day.
60+ issues and thousands of dollars in recoverable revenue from a review that would otherwise never have happened.
Built for a Vancouver communications company. Reports pulled in seconds by anyone, instead of waiting for IT.
The company stored its operational and financial data in enterprise SQL databases, but every report had to go through a handful of technical staff members. Requests piled up, and meetings waited on numbers.
We built an AI application with a chat interface where authorized employee can ask questions like “what is the total number of hours each technician worked this month?” A locally hosted model translates the question into SQL using the database schema as context, runs it, and returns the result as a clean table. Queries can be saved and rerun each month.
It runs on a physical server in the company's own server room. No traffic leaves the network and no schemas or data are shared with third parties.
Read the natural-language SQL case study or learn more about keeping AI deployments in-house.
“VanteriQ helped us begin with AI at our company. We genuinely save several hours a week with the systems they implemented.”Ryan, B.C. Communications Inc.
Locally hosted LLM on an on-premise server, connected to the company's SQL Server database through an internal chat interface.
A few weeks to a working version, including a short training session for staff.
Around 5+ hours a week saved for the IT department, and faster decision making across teams that now pull their own data.
Built for a company with years of records locked in a legacy system. A new CRM over the same data, with access control, faster retrieval, and tools shaped to how the team works.
The business ran on a legacy system that held years of data. Its backend database could not be refactored without disrupting daily operations, so staff kept working around slow navigation, weak access control, and an application that had to be installed on every machine.
Rather than replace the database, we built a new CRM that works directly with the existing data. It adds role-based access control and authorization, real security over the records, faster navigation and retrieval, and smoother day to day operation. It runs in a free web browser with nothing else to install.
We built this CRM with the help of AI development tools, which is why it came together quickly and keeps evolving. When the business finds a gap in its workflow, a custom tool is added to close it.
See how we implement custom systems, or read what custom development costs.
A custom web application over the existing legacy database, with a role-based access and authorization layer and no schema changes. Built with AI tooling.
Built in stages. A working version went live within 3 months. No migration, and new tools are added as the workflow calls for them.
The database remained secure. No migration, no disruption, and a faster, more secure system staff open in a browser.
Built for teams buried in PDFs, reports, and shared drives. Source-backed answers in seconds instead of wasted time digging through folders.
Employees spend valuable time digging through folders, PDFs, and reports just to locate a single piece of information. Keyword search fails the moment the document says “fee” and you searched for “price.”
This system ingests your entire internal knowledge base and lets employees ask questions in natural language, like ChatGPT but grounded entirely in your company's documents. Answers come with clickable source references that take you to the exact section of the file.
It can be sized for thousands of files and deployed locally when the project needs tighter control over data.
Learn how AI document search works, or see where it helps engineering firms.
Retrieval-augmented generation over your documents, with vector search and source citations. Runs on a local model or a third-party provider depending on your data requirements.
4 to 12+ weeks depending on document volume and whether it runs on-premise.
Staff save valuable time on every search for information. Across the team and the week, that adds up to hours of reclaimed time..
Built for teams answering the same questions from the same documents. It finds the relevant information and drafts replies in seconds, any time of day.
Teams spend a significant amount of time searching through specifications, contracts, reports, and internal documentation to respond to questions from clients, partners, and stakeholders.
This system ingests your documentation and drafts a professional, context-aware reply the moment a query arrives. It retrieves the most relevant information, writes a structured response grounded in your internal knowledge, and hands it to your team.
Your team reviews, edits if needed, and sends. Nobody starts from a blank page or a folder search.
An email intake, retrieval-augmented generation over your documentation, a language model for drafting, and a human review step before anything is sent.
2 to 4 weeks to ingest documents, tune the reply style, and connect your inbox.
Staff save time replying to emails, freeing them up to focus on other tasks.
Built for review-heavy teams in engineering, finance, real estate, and insurance. Analyze, compare, and review documents - ask complex questions and get specific answers.
Reviewing documents for key information (like vendor submittals, tender packages, contracts, and permit applications) is slow and prone to mistakes.
This AI tool ingests your documents, becomes an expert on them, and lets you ask questions directly. Ask things like “compare the pump requirements in design spec 2 vs. design spec 3” or “list all deadlines, inspections, and compliance conditions across these permit documents.”
For teams where document review is a significant part of the day, this tool makes that work easier, faster, and more accurate.
Language model question answering over your ingested document set, with answers tied back to the source pages. Handles PDFs and other text documents.
1 to 3 weeks, most of it spent encoding your review criteria.
For teams with document-heavy workloads, cumulative time savings translate into hours saved over weeks and months.
Built for websites that get the same questions every day. Visitors answered instantly, 24/7, instead of waiting on a contact form reply.
An AI chat assistant on your website answers questions, guides visitors to the right page or service, and helps convert more of them into customers. It has become a standard feature on modern commerce websites because it provides instant support at any hour.
We teach it about your business, products, and services so the answers are yours, not generic, and it knows when to hand off to a person.
Retrieval over your website content, an embeddable chat widget, lead capture, and a handoff to a person when the conversation needs one.
About 2 weeks from content description to a widget live on your site.
Customers gain information they need fast and at any time. Leads turn into customers.
Built for businesses with predictable, high-volume calls. Calls answered around the clock making call handling more efficient.
Handling inbound and outbound calls takes significant time, especially when conversations follow predictable patterns like information collection, scheduling, or basic inquiries.
These voice agents hold natural conversations, ask questions, collect specific information, and carry out actions such as booking meetings or routing requests to the right person.
Your team offloads the repetitive call workflows and keeps the ones that need a human.
Speech-to-text, a conversational language model, and text-to-speech, connected to your calendar and CRM so the agent can act on what it hears.
2 to 4 weeks to script the call flows, connect your phone line, and test edge cases.
Saves 5 to 10 minutes per routine call, plus answers the calls that currently go to voicemail after hours.
Built as a working prototype for a mortgage brokerage. A mortgage qualification response drafted instead of manual review.
Mortgage brokers spend a large portion of their day reviewing client financial information and drafting responses about eligibility and potential rates.
This prototype takes client-submitted data such as income, debt, and down payment, calculates the mortgage products they may qualify for based on the brokerage's criteria, and generates a professional, structured response outlining the results.
The broker reviews and sends, keeping full control over client communication while skipping the repetitive work. The same approach applies to any business that qualifies clients against a rule set.
A qualification rules engine on top of a client intake form, with a language model drafting the response and a broker review step before it goes out.
1 to 2 weeks to encode your product criteria and match the response to your house style.
Saves valuable time, leading to increased capacity for clients and requests handled.
Most projects start with a short conversation about where your team's time goes. Tell us the bottleneck and we'll tell you honestly whether AI fits.