AI agents that run the work, not just talk about it
A chatbot answers questions. An agent does the job — reads the data, makes the decision, updates the systems, and tells you what it did. Built with retries, logging, and alerts, so you can trust it unattended.
API & CRM integration
Error handling built in
From $2.1k
An AI agent is software that completes a multi-step business task on its own: it gathers the data it needs from your systems, applies rules and judgement to decide what to do, writes the result back, and escalates when something falls outside its authority. Unlike a chatbot, it acts rather than answers. DAvision builds agents for Canadian businesses from $2,100 for a single-task agent to $11,550 for multi-agent orchestration with custom model fine-tuning.
Automation that breaks silently is worse than no automation
Zapier runs out of road
Simple triggers work until the logic branches. Once a decision needs context — which supplier, which priority, which exception — a rules engine cannot carry it.
Nobody notices when it stops
Most homegrown automations fail quietly. The first sign is a customer asking why nobody called back three weeks ago.
Your team became the integration layer
Copying a number from the CRM into the invoicing tool, then into the spreadsheet, is a job a machine should have taken years ago.
What an agent handles
Multi-step workflows with real branching
Conditional logic, exception paths, and escalation rules — not a linear chain that snaps at the first unexpected input.
Reads and writes your actual systems
CRM, accounting, calendar, inventory, email, internal databases. Two-way, with field mapping and validation before anything is written.
Retries, queues, and alerts
When an API is down the work queues and replays. When something genuinely fails, a human hears about it the same hour.
Decisions you can audit
Every action is logged with the reasoning and the inputs behind it. When you ask why it did something, there is an answer.
Human approval where it matters
Anything above a threshold you set stops for sign-off. The agent moves fast on the routine and waits on the consequential.
Reporting that reaches you
Daily or weekly summaries of what ran, what it changed, and what needed attention.
How it works
Four steps, no surprises. You see a working version before you pay the balance.
Map the process
We sit with the people doing the work today and document every step, exception, and judgement call.
Scope what pays
Not everything is worth automating. We show you the estimated hours saved per workflow before anything gets built.
Build and shadow-run
The agent runs alongside your team without write access until its decisions match theirs.
Go live with monitoring
Full write access, alerting, and weekly optimization reviews for the first quarter.
Packages & pricing
Fixed-price builds in Canadian dollars. Ongoing support is quoted separately from $750 per month.
- Single-task AI agent
- 1–2 workflow automations
- Basic API integration
- Email & notification automation
- Monthly performance report
- 30-day warranty
- Multi-task AI agent
- 5–10 complex workflows
- Advanced API & CRM integrations
- Multi-step conditional logic & error handling
- Real-time data sync & analytics
- Weekly optimization reviews
- Priority support (48h response)
- 90-day warranty
- Multi-agent orchestration system
- Unlimited workflows & custom integrations
- Custom LLM fine-tuning
- RPA (Robotic Process Automation)
- Enterprise security & compliance
- SLA guarantee (99.9% uptime)
- Dedicated AI architect
- 24/7 monitoring & alerts
- 12-month warranty & support
What separates an agent that works from one you stop trusting
Automation projects fail on exceptions, not on the happy path. Any tool can move a record from A to B when the data is clean. The question is what happens when the supplier code is missing, the invoice arrives in a currency nobody expected, or the API returns a 500 for eleven minutes. A rules engine hits that and stops, usually silently. An agent built properly hits it, recognises it is outside its authority, queues the work, and tells a human — with enough context that the human can resolve it in a minute rather than reconstructing what happened.
That is why we shadow-run before granting write access. The agent processes real work in parallel with your team, producing decisions nobody acts on, and we compare its output against what the humans did. Where they disagree we either fix the agent or discover that the documented process is not the real process — which happens more often than anyone expects, and is valuable on its own.
The second thing that separates a durable agent is observability. Every action is logged with the inputs that produced it and the reasoning behind it. When someone asks in three months why an order was routed to the wrong supplier, there is an answer rather than a shrug. This is also what makes improvement possible: you cannot tune what you cannot inspect.
Finally, authority limits. The agent moves fast on the routine and stops on the consequential. You define the thresholds — dollar value, customer tier, exception type — and anything above them waits for a person. This is what makes it safe to run unattended, and it is the difference between automation your team relies on and automation your team quietly works around.
What changes after launch
Concrete, measurable, and checkable against the baseline we take before we start.
Manual handoffs disappear
The steps where a person copied data between two systems stop existing, along with the transcription errors they produced.
Failures become visible
Instead of work vanishing silently, exceptions queue and alert. Problems surface in hours rather than at month end.
Throughput stops depending on headcount
Volume can double without the process breaking, because the constraint was never the people — it was the copying.
Who this is for
Energy and construction firms moving data between project systems and accounting. Clinics handling intake, insurance verification, and recall campaigns. Real estate teams processing listings and lead routing. Any operation where a person is currently the glue between two pieces of software.
We work with dental clinics, construction, energy, real estate, and commercial businesses across Calgary and Canada.
Frequently asked questions
What is the difference between an AI agent and a chatbot?
A chatbot converses. An agent acts — it reads your systems, decides, writes changes back, and escalates exceptions. A chatbot tells a customer their invoice is overdue; an agent finds the overdue invoices, sends the reminders, logs the responses, and flags the accounts that need a phone call.
How much does AI automation cost?
A single-task agent with one or two workflows starts at $2,100. Five to ten complex workflows with CRM integration and error handling run $5,250. Multi-agent orchestration with custom model fine-tuning starts at $11,550.
What if the agent makes a mistake?
Every action is logged with its inputs and reasoning, anything above a threshold you set requires human approval, and the agent shadow-runs without write access until its decisions match your team's. Mistakes are recoverable because nothing happens unobserved.
Can it work with software we already use?
Yes — if it has an API or a database we can reach, we can integrate it. HubSpot, Salesforce, QuickBooks, Google Workspace, Microsoft 365, and most industry-specific systems are routine.
How do we know it is actually saving money?
The process mapping stage produces an hours-saved estimate per workflow before you commit. After launch, the monthly report tracks what ran and what it handled, so the number is checkable rather than promised.
Do you host it, or do we?
Either. We host on managed infrastructure by default. If your data cannot leave your environment, we deploy into yours instead — that is an Enterprise-tier arrangement.
Related services
Thirty minutes, no pitch
We map out exactly what you need and what it would cost. If automation is not the answer, we will say so.
