Find the busywork.Automate it.Keep it running.

We build AI automation for the manual workflows eating your team's week, then measure it and maintain it. Booking audits across Canada & the US.

Good morning

Automation console

Overview
tender-robot · daily runrunning
  1. 08:30:00run started · 12 portals

Worth bidding

0

of 3,900 postings

Job costing

sample view
JobPlannedUsed%
Job A14,940 h26,431 h177%
Job B3,200 h2,890 h90%
Job C6,500 h4,120 h63%
Job Dn/a1,040 hno budget

Forecast at recent pace: Job A needs a conversation on Friday, not at close-out.

Real numbers from systems running today.

3,900

tender postings read every morning by one robot

177%

of budgeted hours: an overrun caught automatically

81.5%

eval pass rate on a private AI support assistant

Most teams lose hours every week copying, checking, searching and re-typing. We build the systems that do it for them, measure that they work, and keep them running.

  1. 01

    Audit

    A free 30-minute call. We pick one manual workflow, map it, and show you the hours it costs.

  2. 02

    Automate

    We build it on your real data and test it properly, so you see it working before you rely on it.

  3. 03

    Maintain

    We watch it every day, fix it when a website or system changes, and improve it as you grow.

Your work arrives messy.

Emails, PDFs, portals and spreadsheets, in every format, every day.

AI reads, checks and sorts it.

Rules handle the obvious. An AI model handles the judgment calls and gives a reason for each.

Your team gets clean answers.

Reports, dashboards and short lists, ready when the day starts.

EmailsPDF invoicesTimesheetsTender portalsSpreadsheetsAIreads · checks · sortsWeekly reportLive dashboardShort list

From a simple email parser to ML models in production. One team builds all of it, and keeps it running.

SimpleComplex

01

Quick wins

Small, repetitive jobs that quietly eat hours every week.

  • Email parsers
  • Forms → CRM or spreadsheet
  • Invoice & receipt data entry
  • Scheduled reports
  • Alerts & reminders
  • File naming & filing

02

Operations & IT

The glue between your systems and the chores that keep them running.

  • Staff onboarding & offboarding
  • Ticket triage & routing
  • Backups & health checks
  • App-to-app integrations (APIs)
  • Data syncs between systems
  • Access reviews

03

Complex systems

Where judgment, scale or risk is involved. Built with tests and monitoring from day one.

  • AI agents that judge & decide
  • Private AI assistants on your documents
  • ML model deployment pipelines
  • Automated model checks & drift alarms
  • Reconciliation across systems
  • Monitoring robots across many websites

01 / 04

One team.
Four kinds of automation.

Most businesses need one of these first. The audit finds which.

01 / 04Monitoring & research robots
tender-robot · example run3,900 → 12
  • Road resurfacing, Phase 2closes in 6 days
  • Streetlight LED retrofitmatches profile
  • Office interior lightingnot our work
  • Substation civil worksmatches profile

Stop checking a dozen websites every morning

A robot logs into your portals, collects everything new, filters out the noise, and hands your team a short list with the reasons attached.

  • Tender & bid monitoring
  • Competitor pricing
  • Regulatory updates
02 / 04Reporting that builds itself
job costing · weeklysample
  • Job A177% of hours
  • Job B90% of hours
  • Job C63% of hours

No more Friday spreadsheet by hand

Numbers pulled straight from your accounting, payroll and field systems into one report, with forecasts and red / amber / green flags.

  • Job costing vs budget
  • Labour & overtime
  • Cash-flow forecasts
03 / 04Document & data entry
invoice.pdf → accountingsample
  • SupplierNorthline Supply
  • Invoice #INV-20418
  • Amount$4,812.50
  • Job codeJ-1042

✓ checked · entered · flagged for review: none

Nobody re-types a form again

Invoices, intake forms, emails and PDFs read, checked and entered into your systems, with a human review only where it matters.

  • Invoice → accounting
  • Forms → CRM
  • Email triage → tickets
04 / 04Private AI assistants
private assistantsample

What's our policy on overtime for weekend call-outs?

Weekend call-outs are paid at 1.5× for the first 4 hours, then 2×, and need a supervisor's sign-off.Source: Field Staff Handbook, §4.2

Answers from your own documents, not the internet

Assistants that answer from your manuals, policies and history, with citations, guardrails, and models that can run entirely inside your environment.

  • Support reply drafting
  • SOP & policy Q&A
  • Onboarding

Real automations, measured rather than promised. Client names stay private; the results don't.

Canadian infrastructure contractor

↗

A robot that reads every tender so estimators don't have to

Every morning it checks 13 tender portals, drops what can't be won, has an AI judge decide what's genuinely the company's kind of work and which team it belongs to, and hands each team a short list.

100%

of held-out past bids kept by the AI judge, while ~94% of postings the teams had passed on were filtered out

How it works+

Problem

Someone checked a dozen tender portals every morning, each with its own login, layout and search, digging for a handful of relevant jobs among thousands of irrelevant ones. Keyword filters flagged office lighting as street lighting; one team said none of what it was sent was theirs.

Built

  • 13 portal connectors: public APIs, guest routes and scripted sign-ins, each failing on its own without stopping the run
  • One normalised record per posting with a fingerprint, so a tender seen on several portals is stored once
  • A single scope gate: closed, closing within 10 days, out of province, or already kept → dropped
  • An AI judge reads each posting against a written profile of the company's work and returns yes / maybe / no, the team it belongs to, and a one-line reason
  • Falls back to keyword rules if the AI is unavailable, and says so in the email subject
  • Outputs: a colour-coded workbook, a web dashboard with a calendar, map and history, and per-team plus executive emails
  • Bid / Pass / Reviewing answers on the dashboard are stored as labelled data to keep improving the judge

Results

  • ~100 new postings a day screened; the judge keeps the 3–10 that matter
  • The 'unclear' pile dropped from ~80 postings a day to under 10
  • AI screening costs about $0.20 a day
  • Validated before switch-over against historical tenders, then made the main classifier
  • Backed by 758 automated tests

How it's kept safe

  • ✓The AI sees only public tender text plus a scrubbed business profile. A test fails the build if names, values or win rates ever reappear
  • ✓Personal dashboard links that become secure cookies; who decided what comes from the link, never the request
  • ✓Same-origin writes only, credentials blanked from network captures, and no email to staff without the owner's sign-off
  • ✓A circuit breaker caps automatic notices so a bug can't flood anyone's inbox
PythonPlaywrightClaudeSQLiteExcelLeaflet

Canadian infrastructure contractor

↗

Job-cost tracking that flags trouble before the job ends

Replaced a weekly hand-built spreadsheet with an app that joins the accounting system and field timesheets by job, and every Friday shows which jobs are drifting over budget, with a dashboard managers actually use.

177%

of budgeted hours on one job, caught automatically rather than at close-out

How it works+

Problem

Each week someone combined numbers from the field timesheet app and the accounting system by hand, guessing the latest weeks because payroll posts about a week late. Overruns surfaced only when it was too late to act.

Built

  • Read-only pulls from the accounting system's database and the timesheet app's API, joined by job number
  • Fills the payroll lag with the pending payroll batch and field hours not yet posted, shown as 'in progress'
  • Per job: % of budget used, recent pace, crew size per day, overtime and double time, and missing timesheets
  • Projected final cost at the recent pace, shown as 'at least' when a job is already over budget
  • A part-week projection mid-week, backtested to about 12% error by Thursday
  • Built automatically every Friday at 8 am: workbook, single-file dashboard and summary email; anyone authorised can pull any date range up to a year
  • Pages for overview, job detail with a burn-up chart, crew per day, staff, data checks and history

Results

  • Traffic light per job: 🔴 over budget · 🟡 close · 🟢 fine · ⚪ no budget, with the reason in words
  • Managers pick which supervisors to talk to on Friday from one list
  • No more estimated weeks filled in by hand
  • Every rule lives in one place and generates its own plain-English definitions page
  • Backed by 139 automated tests

How it's kept safe

  • ✓Read-only access to every source system
  • ✓Credentials kept in the operating system's credential store, never in code or files
  • ✓Office-network allowlist and access-code sign-in with secure cookies
  • ✓No company data passes through AI tools; demos and screenshots use made-up data
PythonSQL ServerREST APIChart.jsExcel

Canadian infrastructure contractor

↗

One live view of a utility client's capital program

Tracks contract value, change requests, billing and schedule for a major client's program across dozens of projects. It replaced a weekly report copied by hand, and flagging where the systems disagree.

30 / 30

projects matched by an independent recheck script

How it works+

Problem

A weekly Excel report was copied by hand from the project tracker and was full of errors. Contract values, change requests, invoices and schedules lived in three places that didn't agree, and nobody could say which number was right.

Built

  • Read-only pulls from the project tracker's API, the accounting system's billing lines and the weekly crew sheet
  • Contract = original order + approved change requests; billed = posted billing + invoices after the accounting cut-off
  • Change requests sorted into approved, submitted and excluded, and matched to billing lines by amount to show invoiced vs processing
  • Per-project reconciliation between the tracker and accounting: agrees, timing difference, investigate, or nothing billed
  • Projected finish from crew bookings, a health label per project, and a billing forecast to any cut-off date
  • A discrepancy report covering ten checks, filterable and downloadable, so teams can fix the source data
  • A client view and an internal view from the same data

Results

  • Checks for changes in about a second; a full refresh takes about 80 seconds and only runs when something changed
  • The page updates itself without losing the reader's place, and never reads a half-written file
  • Exposed gaps in the hand-kept invoice log, so accounting became the billing source of truth
  • Every figure has a formula hover that explains how it was built

How it's kept safe

  • ✓Queries only: a guard blocks any write to the tracker, and boards holding passwords are blocked outright
  • ✓Internal costs, rates and margins are never requested from any system
  • ✓Client data files are kept out of version control
  • ✓If a source column is renamed, the refresh stops and keeps the last good data instead of silently dropping figures
PythonGraphQL APISQLJavaScriptExcel

Product · live at aria-bot.com

↗

ARIA: a private AI support assistant

Answers support questions from a company's own knowledge base, with every model call able to run inside the customer's environment.

74 → 81.5%

eval pass rate after retrieval improvements

How it works+

Problem

Support teams spend hours searching help articles and rewriting the same replies. Off-the-shelf chatbots make things up and send company data to outside vendors.

Built

  • Pipeline: PII redaction → policy check → search → grounded answer → approval gate
  • Guardrails with risk flags and a human approval queue
  • Evaluation suite with rubric grading and drift alerts
  • Local models, so no AI vendor ever sees customer data

Results

  • Eval pass rate raised from 74.1% to 81.5%
  • Answer groundedness improved from 0.63 to 0.70
  • Every answer cites its source articles
FastAPIpgvectorReactClaudeOllamaDockeraria-bot.com →

Project · AWS SageMaker

↗

Fraud detection that won't ship a bad model

Trains, gates, deploys and monitors a fraud model with no manual hand-offs.

AUPRC ≥ 0.80

quality gate before any deployment

How it works+

Problem

Fraud is 0.17% of transactions, so models are easy to get wrong and silently degrade as behaviour changes.

Built

  • SageMaker Pipelines for training and evaluation
  • Automatic quality gate and model registry
  • Real-time inference endpoint
  • Drift alarm when live data shifts

Results

  • Trained on 284,807 real transactions
  • Bad models blocked automatically
  • Fully reproducible infrastructure as code
AWS SageMakerAWS CDKPythonCloudWatchVideo walkthrough →

We connect the tools you already have instead of replacing them.

Claude
Python
Microsoft 365
Azure
AWS
PostgreSQL
FastAPI
Playwright
Docker
GitHub
n8n
Ollama

And maintained long after launch. Every automation is watched daily and fixed when a website or system it depends on changes.

  • ✓Daily health checks on every automation
  • ✓Alerts the moment something changes
  • ✓Fixes and improvements included in maintenance
Automation health · sample viewRunning
  1. 08:30Daily run started
  2. 08:3413 of 13 sources collected
  3. 08:35Accuracy check passed against labelled answers
  4. 08:36One portal changed its layout, collection failed
  5. 08:41Connector updated and re-run, all clear
  6. 08:52Short lists delivered to every team

Runs completed, last 14 days

98%
on time
1
issue caught
11 min
to fix

Then keep it running: monitored, measured and maintained.

  1. 01

    Free workflow audit

    30 minutes. We pick one manual workflow, map it, and we estimate the hours it costs you and what automating it takes.

  2. 02

    Build a pilot

    We build against your real data and measure it from day one, so you see it working before you rely on it.

  3. 03

    Measure it

    You get real numbers (hours saved, accuracy, cost to run) and a straight answer if a workflow isn't worth automating.

  4. 04

    Run & maintain

    We monitor accuracy, fix breakages when a website or system changes, and improve it as your business changes.

Move the sliders for one repetitive task in your business.

Assumes 48 working weeks and that 70% of the task can be automated.

Time back per year

336 h

Worth roughly

$15,120

That's 8 working weeks your team spends on something else.

Do I need to replace my current software?+

No. We work with what you already use (accounting, payroll, CRM, spreadsheets, websites) and connect them. Nothing is ripped out.

Does my data get sent to an AI company?+

Only if you're comfortable with it. Where data is sensitive we use models that run inside your own environment, so it never leaves.

What does it cost?+

It depends on the workflow. After the free audit you get a fixed quote for the pilot, and an optional monthly plan for monitoring and maintenance.

What happens when a website or system changes?+

That's what maintenance is for. We monitor every automation, and when something breaks or drifts we fix it, usually before you notice.

What if automation isn't worth it for us?+

Then we'll tell you. The audit is free precisely so you can find out before spending anything.

Free · 30 minutes · No obligation

Tell us which task eats your team's week. You leave with a map of that workflow and an estimate of the hours it costs, whether or not you work with us.

  • ✓ No sales script
  • ✓ Fixed quote for the pilot
  • ✓ Honest answer if it isn't worth automating

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