AI that ships to production. Not another proof of concept.
Senior engineers in Montréal design, build, and productionize real AI and ML with you, in your timezone. You own the model and the code. Not a prompt wrapper, not an offshore science project.
- —Senior North American team, your working hours
- —A written estimate before you commit a dollar
- —You own the model, the code, and the data pipeline
Get a free estimate for your AI project.
4.9/5
Most AI projects die between the demo and production.
The demo takes a week. Production takes engineering: data, guardrails, security, and someone accountable when the model is wrong. Here is where projects stall, and what shipping actually requires.
A demo that never becomes a product
The prototype wowed the room, then stalled. Nobody scoped the path from notebook to production, so it never left the notebook.
Production is the scope from day one: deployment, access control, fallbacks, and a rollout plan written before the first prototype.
Your data in someone else's hands
Customer records pasted into third-party tools, no data agreements, no audit trail. Legal finds out after the fact.
SOC 2 and HIPAA-grade handling: data boundaries defined in writing, least-privilege access, and an architecture your compliance team can sign.
Hallucinations in front of customers
The model answers confidently and wrongly, and nothing catches it. One bad answer to the wrong customer undoes months of trust.
Grounding in your data, output validation, human-in-the-loop where stakes are high, and evaluation suites that run before every release.
Nobody watching after launch
The model ships, then drifts. Quality degrades quietly, costs creep, and no one notices until users complain.
Monitoring, logging, cost tracking, and retraining pipelines. A model in production is a system to run, not a file to forget.
Welded to one model vendor
The whole product hard-codes one provider's API. When pricing, terms, or quality change, you renegotiate from your knees.
A model-agnostic architecture: swap providers or move to your own fine-tuned model without rewriting the product.
An offshore team you can't see into
Status reports instead of working software, overnight round-trips on every question, and no one who can explain what the model actually does.
Senior engineers in your timezone, building in your repos, demoing working software weekly. You can see every decision.
From an AI feature to a custom model, built for production.
Webisoft builds with in-house senior engineers in Montréal. Whatever shape the work takes, the same rules hold: your timezone, your repos, your model, your code.
LLM applications & RAG
Assistants, copilots, and search grounded in your own documents and data, with retrieval, citations, and guardrails built in.
RAG · Chat · SearchCustom ML models
Forecasting, scoring, classification, recommendation. Trained on your data, evaluated against your baseline, owned by you.
Training · EvaluationAI features in your product
Ship AI inside the product you already have: summarization, extraction, drafting, smart defaults. Integrated, not bolted on.
Integration · UXData pipelines & MLOps
The unglamorous part that decides success: clean data in, versioned models out, monitoring and retraining on rails.
Pipelines · MonitoringComputer vision & NLP
Document extraction, image and video analysis, entity recognition, classification. Models that read what your team reads.
Vision · DocumentsAI automation & workflows
Agents and automations that take real work off your team's plate, with human checkpoints where the stakes demand them.
Agents · AutomationFour steps. Production is the goal from step one.
Scope
A short call about the job the AI has to do and the data you have. You get a written estimate and a plan, free. If AI isn't the right tool for the job, we say so.
Free · This weekPrototype
A working prototype on your real data, with evaluation criteria agreed up front. You judge it on evidence, before committing to the full build.
Your data · Real testsProductionize
Guardrails, security review, monitoring, and integration into your product and workflow. Weekly demos of working software, never a black box.
Demos every weekOwn & monitor
You launch owning the model, the code, and the pipelines, with monitoring in place. Keep us on to improve it, or run it with your own team. Your call.
No lock-inOffshore black box, DIY wrapper, or built here.
Each option is right for someone. A prompt wrapper is fine for an internal experiment. Offshore wins on hourly rate. Here is the full picture when the AI has to face customers.