We put AI to work on precise tasks: tooled agents, connected to your systems, supervised by your teams and evaluated on measurable results.
Request received
Client email with attachment
Understanding
Request type, urgency, linked file
Tools called
Internal search · ERP · calendar
Proposal
Drafted reply, sent for approval
Where AI actually pays off
We are not after an impressive demo, but after the repetitive task whose cost is already known.
Business agents
The agent reads the request, queries your systems, prepares the answer or action, and leaves the final call to a person when the stakes require it.
Full log of every decision and every tool called
Conversational assistants
Your teams ask a question in plain language and get an answer drawn from your own content, with sources shown and access rights respected.
Sourced answers, never invented
Document analysis
Invoices, contracts, reports, forms: extraction of useful fields, classification, summary and direct transfer into your management tools.
Confidence checks and ambiguous cases held for review
Intelligent automation
AI steps in only where it brings a real advantage. The rest of the process stays deterministic, therefore predictable, testable and auditable.
Deterministic path kept for critical steps
Anatomy of an agent
Five inseparable parts. Remove supervision or knowledge, and the demo will not survive its first month.
Étape 01
Your documents and data indexed, chunked and filtered according to each user’s rights.
Étape 02
The model chosen for cost, latency, expected quality and required hosting.
Étape 03
The permitted actions: read a record, create a task, send an email, and nothing more.
Étape 04
A human checkpoint on sensitive decisions, with a proposal ready to accept.
Étape 05
Traces, costs, acceptance rate and quality measured continuously so fixes come fast.
Autonomy levels
Every use case starts at the most cautious level and moves up only when the measurements allow it. You keep your hand on the dial.
AI proposes, the person decides and executes. No operational risk, immediate learning.
The agent prepares the complete work - reply, record, entry - and waits for a simple approval.
The agent acts alone within a defined perimeter, with thresholds, caps and a human escape hatch.
The process runs continuously; humans supervise exceptions and overall performance.
Responsible AI
Confidentiality, verifiable sources, explicit limits and traceability: four written commitments, applicable and verifiable.
Your content is never used to train a public model. Canadian hosting when required.
Every answer cites its source documents, making verification a single click away.
We document what the agent must not do, and what it answers when it does not know.
Every call, tool and decision is logged and available for an audit.
Tooling
Models move fast. The architecture is designed so they can be swapped without rewriting what surrounds them.
Models
Knowledge
Orchestration
Supervision
Frequently asked questions
With a high-volume internal process that carries low legal stakes, at the “suggestion” level. We measure quality for a few weeks, then increase autonomy only if the numbers justify it.
The agent answers from your corpus, cites its sources and has an explicit instruction to say it does not know. Low-confidence cases are routed to a person rather than guessed.
The one that fits your cost, latency and sovereignty constraints. The architecture stays provider-independent, so a model can change without rewriting the application.
Before rollout we quantify the time spent on the process. Then we track the acceptance rate of proposals, handling time and cost per request.
Our team is ready to hear your goals and frame the best next step.
