Custom AI software for Canadian organisations that has to satisfy a privacy officer, a regulator and a board. We build LLM applications, AI agents, machine learning and computer vision into the systems you already run, keep data in Canadian cloud regions when it has to stay home, and work your hours from Halifax to Vancouver.
Custom AI software development means building AI around your own data, workflows and rules instead of renting a generic tool. That covers language models that answer from your documents, agents that complete tasks inside your systems, and predictive or vision models trained on your own history. In Canada it also means meeting PIPEDA, provincial privacy laws such as Quebec’s Law 25 and any sector regulator before the system goes live.
Appther has built software since 2018 and serves Canadian clients from a representative office in Montreal. We take one use case from a scoped pilot to a production system your own team can run, on a fixed-price contract in Canadian dollars.
Updated · Reviewed by Appther’s AI engineering team
Custom AI Software Services for Canadian Teams
Canadian organisations hold years of contracts, claims, patient notes, invoices and call recordings in English and French, and much of it is still read by hand. These six service lines take that work from a tested prototype to a model your own staff can run, with the privacy paperwork written alongside the code.
AI service lines
01 · Service line
LLM & Generative AI Apps
Answers drawn from your policies, contracts and manuals, with sources shown
Drafting and summarising inside Microsoft 365, Google Workspace or your intranet
English and French prompts tested before anyone depends on them
Token spend tracked per workload and trimmed every month
GPT, Claude, Gemini, Llama or Cohere Command, picked by benchmark
What Canadian Companies Gain From Custom AI Software
A slick demo is easy. The value shows up on an ordinary Tuesday afternoon, when the queue is shorter and the forecast is right. Here is where custom AI tends to pay back for Canadian teams.
Less manual reading and re-keying
Finance, claims and operations staff lose hours reading forms and copying data between systems. A model takes the first pass, and people handle the exceptions.
Email, PDFs and scans read, sorted and routed
First drafts ready for review, not blank pages
People approve; the model does the typing
Decisions backed by your own data
Forecasts, scores and anomaly checks turn the history already in QuickBooks, Salesforce or your data warehouse into numbers a manager can act on this week.
Cash-flow and demand forecasts from your ledgers
Lead and churn scores you can explain
Anomalies caught before quarter-end close
Built for production, not demos
A prototype on a laptop and a system serving hundreds of staff at 8am are different builds. We design for production from the first sprint.
Accuracy measured on a held-back test set
Drift monitoring and planned retraining
Cost and response time tracked per workload
Faster service in both official languages
Customers get answers in seconds, in English or French, at any hour, with suggestions based on what they actually did.
Answers at 11pm without an overnight shift
Suggestions based on real purchase history
Service levels competitors cannot match quickly
Technologies
The Techniques Behind Our Canadian AI Builds
Seven techniques decide whether an AI system merely sounds right or holds up in front of a Canadian auditor, regulator or customer. Pick one to see where it fits.
Large Language Models
We benchmark GPT, Claude, Gemini, Llama, Mistral and Toronto-built Cohere on your actual tasks, then choose on accuracy, cost and where the model can be hosted. A smaller model often matches a larger one for a fraction of the per-call price, and we tell you when it does.
Retrieval & Vector Search
Policies, contracts, tickets and wikis are indexed by meaning, in English and French, so the model receives the right passages and cites them. We use pgvector, Pinecone or OpenSearch depending on where your data already lives.
AI Governance & Guardrails
Inputs and outputs are filtered, personal information is removed before it reaches an external API, and every prompt and response is logged. Under PIPEDA and Law 25 that log is how you answer an access request or explain an automated decision.
Fine-Tuning & Distillation
Fine-tuning on your labelled examples, or distilling a large model into a small one that runs cheaply in a Canadian region, comes after cheaper options. Better retrieval and prompting usually close the gap first.
Evaluation & Observability
A held-back test set is scored automatically on every prompt or model change, so quality is a number on a dashboard instead of an opinion in a meeting.
Vision & Multimodal Models
Models that read photos, video and scanned paper as easily as text. For Canadian clients that often means claims photos, site and equipment imagery, shipping documents and bilingual forms.
Private & Self-Hosted Deployment
Open-weight models running inside your own cloud account in Montreal, Toronto, Calgary or Quebec City, so regulated data stays in Canada. The usual choice for health, financial and public-sector work, or whenever your contracts require it.
Not Sure Where AI Fits? Start With a Free Use-Case Audit.
One conversation about your data, workflows and goals shows which use case deserves funding first, what it is likely to cost in Canadian dollars, and where AI is the wrong answer.
Free · No obligation · Report within 48 hours, your time zone
Industries
Custom AI for Canadian Industries
Nine sectors where Canadian organisations are putting models to work, from federally regulated banks to provincial health networks. See our fintech and healthcare practices for more depth, or enterprise software when the AI must connect to everything else you run.
[01]Banking, Credit Unions & FinTech
Lending, fraud and compliance work where OSFI and FINTRAC expect a clear audit trail.
Why Canadian Teams Choose Appther for Custom AI Software
Plenty of firms can demo a chatbot. Fewer will test your own data in week one, tell you plainly what a model can and cannot do, and put privacy obligations in the plan from the start. One team covers the whole stack, with our AI development and cloud infrastructure specialists on the same roadmap.
01
One team from data to interface
Pipeline, retrieval, model, interface and the APIs your other systems call are built by one team on one roadmap, so accountability never gets lost between vendors.
02
Proven on your data
Nothing ships without a held-back test set and a simple baseline to beat. If a rule works as well as a model, we say so before you pay for the model.
03
Privacy designed in, not added later
Consent, purpose limits, a privacy impact assessment where Law 25 or your policies call for one, Canadian hosting and an audit log are scoped in discovery, so your privacy officer approves before the build, supported by our AI engineering practice.
04
Fixed price in CAD, full ownership
Discovery ends with a written scope and a fixed CAD quote with GST/HST invoicing. You own all code, prompts, fine-tuned weights and documentation, with an NDA signed before we see your data.
Pricing
How Much Does Custom AI Development Cost in Canada?
Most Canadian AI projects fall into one of three bands. Price depends on the number of use cases, how clean the data is and how many systems the AI must connect to. The model itself is rarely the largest cost.
Typical custom AI development costs in Canada, in Canadian dollars
Stage
What you get
Timeline
Typical budget (CAD)
AI pilot
One use case on your own data, scored against a baseline, with one live integration
4 to 8 weeks
CA$20,000 to CA$45,000
Production system
Several workflows, monitoring, retraining, a security review and enterprise integrations
3 to 6 months
CA$60,000 to CA$140,000
AI platform
Shared platform for several teams: agents, model routing, tenant isolation and MLOps
6 months and up
From CA$140,000
Model usage and cloud hosting are billed by the provider at cost and estimated against your volumes before you commit. Every quote is fixed-price in CAD with GST/HST invoicing.
Tax credits
SR&ED
AI work with real technical uncertainty, such as getting a model to perform reliably on unusual data, may qualify for Canada’s Scientific Research and Experimental Development program. We keep the records a claim relies on: hypotheses, experiments and results.
Innovation funding
NRC IRAP
Canadian small and medium-sized firms can apply to the National Research Council’s Industrial Research Assistance Program for innovation projects. We provide the technical plan, milestones and budget an IRAP advisor asks to see.
We are engineers, not tax advisers. Eligibility is decided by the CRA and the NRC, so confirm with your accountant or IRAP advisor.
Custom AI software or an off-the-shelf AI tool?
Off-the-shelf AI tools compared with custom AI software
Off-the-shelf AI tool
Custom AI software
Fit to your workflow
You adapt to the tool
Built around how your team works
Where data lives
Vendor’s regions, often outside Canada
Canadian regions or your own tenancy
Connection to your systems
Limited to the vendor’s connectors
Any system with an API, including in-house ones
Cost as usage grows
Per-seat fees that rise with headcount
Higher up front, lower per use at scale
Ownership
Licence only
You own the code, prompts and models
Off-the-shelf tools are the right call for generic tasks such as meeting notes. Custom software pays off when the AI has to use your data, follow your rules or run inside your own systems.
Security & Compliance
How We Keep Your Data Safe in a Canadian AI Build
Prompts and training sets move your data somewhere new. In Canada that brings duties under federal and provincial privacy law and, often, to a sector regulator. Here is how we handle each area, limited to what we can honestly claim.
Personal information appears in prompts and training data more often than teams expect. We start by collecting less, so privacy compliance comes from how the system is built rather than a checklist before launch.
PIPEDA & provincial PIPAPurposes and consent mapped for each feature, retention rules enforced in code, and breach reporting to the Privacy Commissioner rehearsed, not just written down.
Quebec Law 25Privacy impact assessments before Quebec data leaves the province, privacy-protective defaults, and the notice Law 25 requires when a decision is made solely by automated processing.
AI rules and guidanceWith AIDA lapsed when Bill C-27 died in January 2025, Canada has no AI-specific law in force at the time of writing. We follow the federal voluntary code on generative AI, the Privacy Commissioner’s generative AI principles and your sector’s guidance.
Data minimisationModels receive only the data they need, and personal information is masked before any external API call, which also lowers token cost.
Attackers use whichever route into a model is easiest. Identity, encryption, secrets and prompt handling are designed together rather than as separate tickets.
Tenant isolationEach customer or business unit gets its own keys and vector namespace, so one tenant’s search cannot return another’s documents.
Encrypted transportModel calls travel over TLS, secrets sit in a managed vault, and prompts and responses are never logged outside your account, or outside Canada when residency applies.
Prompt-injection defenceUploaded files and web content are treated as data, never as instructions, and every tool call is validated so a malicious document cannot act on someone’s behalf.
Least-privilege toolsAgents get only the permissions a task needs. Payments, outbound email and deletions wait for a named person to approve them.
These engineering practices apply to every Canadian project. Appther holds ISO/IEC 27001 and ISO 9001:2015 certification.
OWASP Top 10 for LLMsEach release is reviewed against the OWASP Top 10 for LLM applications, from prompt injection to excessive agency.
ISO/IEC 27001 certifiedAccess control, change management and incident response run under Appther’s certified ISO/IEC 27001 management system, whether or not your organisation is certified.
Secure coding & reviewEvery change gets a second engineer’s review, static analysis and dependency scans run in CI, and no secrets live in the repository.
Penetration testingRegulated workloads get an independent penetration test before launch, with every finding fixed and retested.
Our ISO certifications cover how Appther works; they do not certify your product. Product-level attestations, such as SOC 2 or a health privacy review, sit with your compliance team and assessor. We build and document so that review is quick.
Regulated sectors change the architecture, not just the paperwork. We map these constraints in discovery so they never surprise you mid-project.
Health informationFor clinical workloads: PHIPA, Alberta’s HIA or Quebec’s health information rules mapped up front, with patient data kept in Canadian regions and never sent to endpoints outside your agreements.
OSFI model risk (E-23)For federally regulated lenders and insurers, models are inventoried, validated and monitored in line with OSFI’s Guideline E-23 on model risk, with inputs and outputs logged for review.
PCI DSS scope reductionCard numbers are masked before they reach any model, keeping the AI layer out of PCI scope and your assessment smaller.
Canadian data residencyAWS Montreal or Calgary, Azure Toronto or Quebec City, or your own tenancy for prompts, embeddings and logs, documented for your privacy officer and auditors.
AI features lose trust and fail public-sector procurement for predictable reasons. We design against those failure points from the start.
Disclosure of AI usePeople are told when they are dealing with AI and can reach a person easily, in line with Law 25’s transparency duties and the federal Directive on Automated Decision-Making for public bodies.
Human escalation pathsEvery assistant has a documented route to a person and takes it when confidence drops instead of guessing.
WCAG 2.2 AAInterfaces are tested for contrast, focus order and screen readers against WCAG 2.2 AA, supporting the Accessible Canada Act and Ontario’s AODA.
Failure statesWhen unsure, the system says so and shows its sources instead of answering with false confidence.
Grouped by layer, from the model that answers to the pipeline that checks it. Each can run in a Canadian region when your data must stay in the country.
OpenAI
Anthropic Claude
Google Gemini
Meta Llama
Mistral
DeepSeek
Qwen
Hugging Face
LangChain
LangGraph
CrewAI
PyTorch
TensorFlow
FastAPI
Python
Node.js
pgvector
Milvus
Qdrant
Redis
MongoDB
TimescaleDB
InfluxDB
Grafana
AWS Bedrock
Azure OpenAI
Google Vertex AI
Docker
Kubernetes
Terraform
React
Twilio
Feature Set
What Ships in Our AI Applications
Building blocks that appear in most AI platforms we deliver for Canadian clients, from the first search query to production monitoring.
Cited Answers From Your Documents
Task Agents With Approval Steps
Planned Retraining Cycles
Live Transcripts & Streaming Replies
Demand & Churn Forecasts
Quality & Spend Dashboards
Encryption in Transit & at Rest
Tenant Isolation by Design
Routing Across GPT, Claude, Gemini & Cohere
CRM, ERP & Helpdesk Connectors
Role-Based Permissions
Hand-Off to a Person
Version Control for Prompts & Models
Bilingual OCR & Image Analysis
Drift & Accuracy Tracking
Token Cost Optimisation
Our Process
How We Build Custom AI in Canada
Eight stages take an idea to a system your team runs. The risky parts are tested on your data early, the privacy file grows alongside the code, and each stage ends with something you can measure.
Week 1
Discovery & Use Case
The problem, the constraints and the metric that defines success.
1
What we doWe meet the people doing the work today, find where the hours go and check whether your data can support the use case. Existing tools, contracts and privacy commitments are reviewed now, not discovered later.
What you getA problem statement, one success metric and a ranked shortlist of use cases.
Week 1–2
Business Case & Budget
What the AI must be worth, in dollars, before it is built.
2
What we doWe estimate the hours saved or revenue gained, size model and hosting costs at launch and at scale, and agree the target we will be measured against.
What you getA business case with the arithmetic shown, a CAD budget and a scope you can hold us to.
Week 2–3
Data & Privacy Review
Whether your data can carry the use case, and on what terms.
3
What we doWe audit coverage, labels, freshness and access rights in every source, confirm consent and purpose under PIPEDA or Law 25, and decide where the data will be hosted.
What you getA data assessment, a cleaning plan, a residency decision and a privacy impact assessment where needed.
Week 3–6
Prototype & Evaluation
A working prototype scored against a baseline.
4
What we doWe build the simplest approach that could work, often retrieval plus a strong prompt, and score it on held-back examples from your data against a rules-based baseline. If the baseline wins, you hear it from us.
What you getA prototype on your data, a measured accuracy figure and a clear go or no-go.
Week 5–12
Build & Integration
Models, retrieval and agents wired into your systems.
5
What we doWe select the model by benchmark, build the retrieval layer, define which tools an agent may use and which actions need approval, then connect it to Salesforce, Dynamics, Odoo, QuickBooks or your own product.
What you getWorking features inside the tools your team already uses, with an audit log of every action.
Week 10–16
Guardrails & Hardening
Proof it is safe before anyone relies on it.
6
What we doInput and output filters, personal-information masking and rate limits are mapped to your obligations. Regression tests, red-team prompts and load tests run, plus a penetration test for regulated workloads.
What you getA documented guardrail set, a quality baseline and a system that holds up under load and attack.
Week 14–20
Staged Launch
Live, but rolled out in steps you control.
7
What we doWe release behind feature flags in your own cloud account, in a Canadian region where required, with dashboards for cost, latency and quality and a tested rollback.
What you getA live system, runbooks, dashboards and a team that knows how to run it.
Ongoing
Monitoring & Improvement
Keeping quality high long after launch.
8
What we doWe watch for drift, retrain on a schedule or trigger, and re-run the evaluation set whenever a new model version appears.
What you getStable quality, predictable costs and a monthly report in plain language, in English or French.
Client Voices
What Clients Say After Go-Live
“Appther built our student competition app exactly the way we envisioned it: easy registration, smooth judging, and real-time results that keep participants engaged.”
Dionna MilemOwner · My BOB Team LLC
“Building a mental health app means every detail has to be handled with care, and Appther got that from day one. The product feels warm, simple, and genuinely helpful.”
Bob SinghalProfessor of Joy · Joyscore Inc
“Appther exceeded every expectation, on time and on budget, and the final product has been instrumental in scaling our business. A rare engineering partner.”
Johnathan PefferCEO · Bull's Eye Technologies Inc
“Implementing an ERP across our operations felt daunting at first, but Appther made the transition seamless. Every department now works off a single source of truth.”
Pushpender KumarGeneral Manager · A.B. Sugars Ltd
Latest Insights
Guides for Canadian AI Buyers
Architecture, cost and integration guides drawn from real projects, for the questions Canadian teams ask before they commit a budget.
Where models earn their place inside Odoo, Dynamics, NetSuite and QuickBooks, and where they do not.
Free 30-Minute AI Strategy Call, in Your Time Zone
Turn One Call Into an AI Roadmap
In thirty minutes you get a prioritised use case, a realistic budget in Canadian dollars and a delivery timeline. No slide deck, no obligation, and an honest answer if AI is the wrong tool.
Prioritised use case Budget in CAD Delivery timeline
Questions Canadian Teams Ask Before Investing in AI
It designs, builds and runs AI systems around one organisation’s data and processes: document assistants, AI agents, forecasting models, computer vision and the integrations that connect them to existing software. Appther does this for Canadian businesses, from the first scoping call to production support.
Most pilots cost CA$20,000 to CA$45,000 and take 4 to 8 weeks. Production systems with several workflows, monitoring and enterprise integrations usually run CA$60,000 to CA$140,000, and larger AI platforms start around CA$140,000. You get a fixed-price CAD quote after a free scoping call, and model usage and hosting are estimated separately before you commit.
A pilot on your own data takes 4 to 8 weeks. A production system with integrations and monitoring takes 3 to 6 months. Data quality, not model choice, usually decides the timeline, which is why data review is the third stage of our process.
Yes. We deploy in AWS Montreal or Calgary, Azure Toronto or Quebec City, Google Cloud Montreal or Toronto, or your own tenancy, and can self-host open-weight models so prompts and documents never leave the country. Where residency is not required, hosted APIs are often cheaper and faster, and we will say so.
Purposes, consent and retention are mapped for each feature during discovery. For Quebec data we support the privacy impact assessment Law 25 requires before information leaves the province and build in the notice for decisions made solely by automated processing. Canada has no AI-specific law in force at the time of writing, so we also follow the federal voluntary code on generative AI and sector guidance such as OSFI Guideline E-23 on model risk.
Often, when the work involves genuine technological uncertainty, such as getting a model to perform reliably on unusual data. We keep the technical records an SR&ED claim relies on, including hypotheses, experiments and results. Eligibility is decided by the CRA, so confirm with your tax adviser.
Yes. Retrieval, prompts, test sets and interfaces are built and tested in English and Canadian French, which matters for Quebec customers who have the right to be served in French.
GPT, Claude, Gemini, Llama, Mistral and Cohere’s Command models, hosted through Azure OpenAI, Amazon Bedrock, Google Vertex AI or your own infrastructure. We choose per task by benchmarking accuracy, cost and response time on your data.
Yes. You own all code, prompts, evaluation sets, fine-tuned weights and documentation, with an NDA signed at the start and IP assigned in the contract. Everything runs in your own cloud accounts, so nothing depends on us if you take it in-house.
Appther has a representative office in Montreal. Engineering is delivered by our team headquartered in Noida, India, with project hours aligned to Canadian time zones from Atlantic to Pacific and a named senior engineer as your main contact.
An AI Engineer Replies Within One Business Day, Your Time Zone
Let's Scope Your AI Project
Tell us what you want to automate, predict or answer, and where the data lives. No sales pitch, just a clear view of approach, scope, timeline and cost in Canadian dollars. Prefer email? Use our contact page.
Free 30-min strategy callBooked in your time zone, with plain advice on what to fund first.
Fixed-price quote in CADWritten scope and price before work begins, with GST/HST invoicing.
NDA on requestYour data, prompts and roadmap stay yours.
