We find the work your team does by hand every week, automate the parts that are rules and reading, and leave the judgement with people. Baselined before, reported monthly after, governed to a standard your auditor can read — with a two-week assessment that ranks candidates by payback before anyone builds anything.
Almost every engagement is one of these, or a combination. Each ships with a confidence threshold, a review queue for anything below it, and an audit trail — so the automation can be wrong without being expensive.
Invoices, purchase orders, delivery notes, claim forms, statements. Extracted, validated against your own records, and posted — with anything ambiguous routed to a person instead of guessed.
The handoffs that currently happen by email and copy-paste: routing, approvals, status sync, reconciliation, alerts. Built at the API layer so a UI change doesn't break it.
An internal assistant that can answer from your own documents and take scoped actions — draft the reply, create the record, prepare the report — with approval required before anything is committed.
We came to AI work from ISO 27001 and GRC engagements, so governance isn't a slide at the end. These controls are designed into the build and documented for your privacy register and your auditor.
Enterprise endpoints with training disabled and zero-retention terms, or self-hosted open models where residency demands it. Contracted, not assumed.
TermsZero retentionDeployed into your cloud tenant in the region you name — Canada, US or EU. Prompts, outputs and documents never transit a Noordev system.
RegionCA · US · EUA confidence threshold per field or decision, a review queue below it, and a named owner for exceptions. Nothing with financial or legal effect commits unapproved.
ApprovalRequired at gatesInput, output, model and version, confidence, who approved and when — retained in your infrastructure and queryable. Every automated action can be traced and reversed.
RetentionYour policyA labelled test set built from your real documents, with accuracy and cost thresholds that must be met to go live — and re-run whenever a model or prompt changes.
GateEval suite in CIData flow diagram, purpose, lawful basis, retention and subprocessor list handed over as records you can drop into your privacy register.
DeliverableRegister-readyMost AI projects fail at the first step — automating something that was never worth automating. Two weeks of measurement costs less than one wrong build, and the fee is credited against whatever you decide to do next.
Process inventory, volume and hours baseline, data readiness check, and a ranked backlog with estimated payback per candidate.
The top candidate run against a labelled sample of your real data. Accuracy, cost per item and failure modes measured before any integration work.
Integration, review queue, thresholds, logging and dashboards — in your tenant, in your repository, deployed through CI with the eval suite as a gate.
Automation runs alongside the manual process while your team checks its output. Thresholds tuned on live data before anything is switched off.
Monthly report against the original baseline: hours reclaimed, accuracy, exceptions, run cost. Retire or rebuild anything that misses its case.
We don't resell tokens or take a margin on inference. Your provider bills you directly, we show the run cost per item in the monthly report, and we're paid for the engineering.
A measured, ranked answer to what's worth automating. Credited in full against your first build, and useful even if you build nothing.
One process taken from manual to production, including the review queue, the logging and the parallel-run period. Where most of our AI work lands.
Multiple workflows, retrieval over your own content, scoped tool access and an approval layer — plus the governance to put it in front of regulated users.
Measured against the baseline recorded during the assessment. Two automations missed their projected payback and were retired — those are in the median too.
“The assessment told us two of our four ideas weren't worth building. We funded one of the other two and it paid for itself in five months — and my controller still approves everything over a threshold she set.”
We'll tell you when a spreadsheet formula, a report, or a process change would beat an AI build — that answer comes up more often than the market implies.
High-volume, rule-heavy work that currently moves through inboxes and spreadsheets: reading invoices and purchase orders, triaging and routing tickets, extracting data from PDFs and contracts, reconciling records between systems, drafting first-pass replies and reports. We automate the steps, not the judgement — a human still approves anything with money or legal consequence attached.
A two-week assessment scores every candidate process on volume, hours consumed, error rate, data readiness and how contained the failure mode is. You get a ranked backlog with an estimated payback per item. We start with the highest-payback process that can fail safely, not the one that demos best.
No. We deploy against enterprise API endpoints with training disabled and zero-retention terms, or self-hosted open models where residency requires it. Data stays in your cloud tenant, prompts and outputs are logged in your infrastructure, and the data flow is documented for your privacy register — including Québec Law 25 and GDPR records.
Every automation ships with a confidence threshold, a human review queue below it, and a full audit trail of input, output, model version and decision. Failure is designed to be visible and reversible: low-confidence items route to a person rather than being pushed through, and every action an agent takes can be traced and undone.
A two-week assessment is CAD $9,500 and credited against the first build. An automation sprint that puts one process into production is $18k to $45k depending on integration depth. Agent and platform builds with multiple workflows, tool access and an approval layer run $85k to $180k. All fixed-fee, with running model costs quoted separately and billed at cost.
Yes — that's usually where the value is. Odoo, Dynamics, Salesforce, HubSpot, Sage, Shopify, SharePoint and Postgres are the systems we most often connect. Where an API exists we use it; where one doesn't, we build a documented integration rather than screen-scraping a UI that will change.
No. Classic RPA drives a user interface with a script and breaks the next time a button moves. We work at the data and API layer, using language models only for the parts that genuinely need judgement — reading unstructured documents, classifying intent, drafting language. The result is cheaper to run and far less brittle.
We baseline the process before we touch it — volume, hours, cycle time, error rate — and report the same numbers monthly after go-live. If an automation doesn't reach the payback we projected, we say so in the report and either fix it or recommend retiring it. Hours reclaimed is the metric, not tokens consumed.
A 45-minute call on the work that eats your team's week. If a two-week assessment is the right next step we'll say so — and if the honest answer is a process change rather than a model, we'll say that instead.