AI Tool Implementation
Assistants, copilots, and AI features embedded into the products and tools you already run — configured, integrated, and actually adopted by the people meant to use them.
— AI automation · Montréal
Automate the work that eats your week.
AI @ N2L is an AI implementation team working with organisations across Greater Montréal and remotely. Invoice and document processing, form and email triage, and the repetitive back-office steps that quietly cost you a salary.
We do not sell hype. We wire intelligence into the tools you already run, automate the work that drains your teams, and build custom systems where off-the-shelf stops reaching.
Outcome-first
Every engagement is scoped to a measurable result — never to a buzzword.
Grounded
Retrieval, guardrails, and evals on everything that ships. No guesswork in production.
Yours to run
Models, prompts, and pipelines hand off to you — documented, monitored, owned.
Priced up front
A written scope and a price before any work starts. Pilots are fixed-price.
— The work
Intelligence, from integration to production.
Not strategy decks — working systems. We cover the full arc: embedding AI where you already work, automating what drains you, and building what the market cannot sell you off a shelf.
Workflow & Process Automation
Routing, triage, extraction, follow-ups — the repetitive load automated end-to-end with agentic pipelines, and a human approval gate exactly where it matters.
Custom Models & Fine-Tuning
Bespoke and fine-tuned models for the tasks generic ones get wrong — trained against your data, measured against your benchmarks, owned by you.
RAG & Knowledge Systems
Retrieval-augmented systems that answer from your documents and your data — grounded, cited, and guard-railed instead of improvising.
Discovery, Search & Recommendation
Ranking, semantic search, and recommendation engines that surface the right thing at the right moment — across a product catalogue, a support desk, or an internal knowledge base.
Conversational Assistants & Agents
Support agents, internal copilots, and multi-step agents that take real action in your systems — with permissions, audit trails, and escalation built in.
Document & Vision AI
Extraction, classification, and image understanding that turn unstructured files into structured, queryable data your business can finally act on.
Data Engineering & MLOps
Pipelines, vector stores, evaluation, and monitoring — the unglamorous plumbing that keeps intelligent systems accurate long after launch day.
AI Strategy, Audits & Governance
Readiness reviews, use-case discovery, and guardrails for privacy and safety — so what you deploy is defensible in front of your board and your regulator.
— The capabilities
A practical toolkit, kept deliberately current.
Six disciplines behind every engagement — from foundations to governance. Nothing exotic for its own sake: everything below has already run in production.
Foundations
LLMsMultimodal modelsEmbeddingsFine-tuning
Build
PythonRAGAgents & orchestrationVector databases
Data & MLOps
PipelinesEvaluationMonitoringFeature stores
Deploy
Private APIsServerless GPUEdge inferenceCaching
Integrate
WebhooksSDKsApp pluginsStreaming
Govern
GuardrailsPII handlingAudit logsRed-teaming
— The process
Scoped, priced, then built. No renegotiation.
Our incentives are fixed to your result rather than to billable hours, and the price is agreed in writing before anything starts. This is what an engagement looks like from your side of the table.
Scope & price, up front
You see the use-case, the milestones, and the price before anything starts. The provider sets it; you agree to it. Pilots are bounded and fixed-price by default.
Work with the people doing it
You deal directly with the senior people building your system — not a delivery layer translating between you and the model.
Settle through the Algo
Every payment flows through one immutable distribution — visible, permanent, identical on every transaction. Nothing is skimmed in the middle.
Own the outcome
The work, the assets, and the results are yours — models, prompts, pipelines, and the documentation to run them. We operate them for you only as long as you want us to.
— The pricing
Three ways to pay. All of them in writing.
Licensing fees, usage-based pricing, and direct contracts. Whichever fits, you see the number before work begins and it does not move afterwards.
Licensing fees
Systems we have already built, offered as products: a written licence, a known cost, and upgrades that arrive without a new project.
Usage-based pricing
Pay for what runs. Metered pricing tied to actual consumption — visible on the same ledger that settles every N2L transaction.
External contracts
Direct service delivery for organisations: a written scope, priced by the provider, agreed by the buyer, settled by the algorithm.
— The edges
Most AI needs a place to live. And a way to be seen.
AI @ N2L stays specialist by design. When your project also needs software built, media produced, or an audience reached, our sister teams at N2L join the same engagement — one scope, one price, no subcontractor chain.
The product it lives inside
Need the platform, API, or application the model runs in? Dev @ N2L builds it — and when a project needs both, the two Realms ship it as one engagement.
Dev @ N2L →The creative hand on top
Generative work that needs human creative direction — brand, interface, and production art — belongs to the Studio Realm.
Studio @ N2L →The audience it reaches
Putting an AI product in front of the right people is its own discipline. Social @ N2L and Ads @ N2L carry it there.
Social @ N2L →— FAQ
Asked before every pilot.
If your question is not here, bring it to the call — that is what it is for.
What does AI @ N2L actually do?
We build and run the AI systems that take repetitive work off your team: reading invoices and documents, triaging forms and email, answering from your own files, and automating the steps in between. We are the artificial-intelligence arm of N2L, a Montréal company, and we work with outside organisations as well as inside the group.
How much does it cost to automate invoice processing?
There is no single figure, and anyone quoting one before seeing your documents is guessing. It depends on how many invoices you handle, how many formats and suppliers they arrive in, how much validation they need, and which system they have to land in. What is fixed is the sequence: we read a sample of your real invoices, then put a written scope, milestones and a price in front of you before any work begins. Pilots are bounded and fixed-price by default.
We receive about 200 paper invoices a month. Can that actually be automated?
Yes, and paper is not the obstacle. Scanned or photographed invoices are read, then supplier, dates, totals, taxes and line items are extracted, checked against your purchase orders or your own rules, and written into your accounting or ERP system. Anything the system is unsure of goes to a short human review queue instead of being silently guessed. The honest test is your own paperwork — send a sample, including the messy ones, and you will hear which are straightforward, which need special handling, and whether any part is not worth automating.
What happens to our data?
Scope defines it before work starts: where data lives, what any model sees, and what — if anything — leaves your environment. Private deployment inside your own infrastructure is a standard option, not an upsell, and PII handling and audit logs are part of the governance work, not an extra.
How do you keep models from making things up?
Grounding is the default architecture: retrieval against your sources, guardrails on output, and evaluation gates that measure accuracy before and after launch. Systems that cannot pass their evals do not ship.
Do we need our own AI team?
No. A pilot runs entirely on our side; you supply the documents, the access, and someone who knows how the process works today. If you want to bring it in-house afterwards, handover — documentation, training, and runbooks — is written into the scope from the start.
What if the pilot fails its metric?
Then it does not scale, and that is the system working. You keep the pilot’s findings and pay only its fixed price — no multi-year platform contract riding on a demo.
Can AI handle long-term, large-scale engagements?
That is the model’s home ground. Licensing and usage-based pricing exist precisely for systems that run for years — and the fixed algorithmic split means the economics never drift against you as the engagement grows.
Have a problem worth automating?
Tell us what is slowing you down. You will get a clear use case, an honest scope, and a price — before any work begins, and in writing.
Book a Call →— Contact
AI @ N2L
Montréal, Québec, CanadaServing Greater Montréal and Québec.