Free · 3-5 days · No obligation
AI Opportunity Audit
We map your operations, find the highest-ROI automations, and hand you a ranked plan with payback math. Yours to keep, whoever builds it.
No obligation. No follow-up sequence.
internal operations automation
The hub where your reporting builds itself
Your numbers assembled from the tools you already run, reports written on schedule, exceptions flagged. 5x ROI in 30 days. Or we work for free.
Teams we build for
- Hoyes Michalos
- Nurse Next Door
- Fedi
- UBC Sauder
- Merchant House Capital
- Picton Investments
- Campbell Froh May & Rice LLP
- Barnakl
- Hungerford
- Breez
Teams we build for
- Hoyes Michalos
- Nurse Next Door
- Fedi
- UBC Sauder
- Merchant House Capital
- Picton Investments
- Campbell Froh May & Rice LLP
- Barnakl
- Hungerford
- Breez
Teams we build for
- Hoyes Michalos
- Nurse Next Door
- Fedi
- UBC Sauder
- Merchant House Capital
- Picton Investments
- Campbell Froh May & Rice LLP
- Barnakl
- Hungerford
- Breez
The problems this solves
01
Your numbers live in five tools.
Revenue in one place, hours in another, pipeline in a third. The truth requires four logins and an afternoon.
02
Reporting eats Fridays.
Investor updates, client briefs, board packs. Senior hours spent formatting, not thinking.
03
Approvals die in Slack.
Asked, lost, re-asked. The thread scrolled away and the invoice sat for a week.
04
The owner has no glance view.
You find out a metric slipped when somebody mentions it. A month late.
05
The process lives in one person's head.
When they're out, things stop. When they leave, things break.
What we build
01 · Connections
The hub reads from the tools you already run. Nothing gets re-keyed.
02 · Metric assembly
Numbers collected, reconciled and stored in one place on a schedule.
03 · Report generation
Investor updates, client briefs and board packs assembled automatically. The numbers come from systems. The narrative is drafted around verified figures.
04 · Approvals
Requests that track themselves: asked once, visible until answered, escalated when stuck.
05 · Owner dashboard
The glance view. Every number that matters, current, in one screen.
06 · Exception alerts
When a metric moves outside its range, Slack tells you the day it happens, not the month after.
What's included
- Connections to your existing tools and one reconciled data store
- Report templates for the documents you actually send
- Owner dashboard plus exception alerting
- Training, monitoring and quarterly iteration. Cancel anytime
Proof
An interior design firmDescribed, not named
Interior design · In deployment
An interior design firm runs five systems from one build
Five AI systems for an interior design firm: AI inbox, vendor coordination, a RAG knowledge brain, a live dashboard and a content engine.
In deploymentsystems in one build
AdvizrInternal
Agency operations · Internal system
Advizr runs the whole agency on one dashboard backed by 50+ automation scripts
Advizr runs on its own build: one dashboard for finances, leads, campaigns and workflow runs, backed by 50+ automation scripts on scheduled Python services.
50+ automation scripts in production
Weeks, not quarters.
How the full engagement works01 Discovery
WEEK 0
02 Prototype
WEEKS 1-3
03 Deploy & train
WEEKS 4-8
04 Run & improve
WEEK 9+
How we build it
Step 01
Reconcile before you report.
One store, one definition per metric, agreed in writing. Reporting on top of numbers two teams define differently produces confident nonsense.
Step 02
Automate assembly, keep the definitions human.
Collection and narrative assembly run on a schedule. The model writes around figures the systems supply and never invents one.
Step 03
Add exceptions once the baseline is trusted.
Alerting comes last, because an alert on a number nobody trusts gets muted in a week.
What we will not automate
Defining a metric for you, and writing any figure the underlying systems did not produce. The narrative is generated, the numbers are not.
How it is built
See the full stackRetrieval
Your SOPs, historical reports and the reasoning behind past decisions are indexed, so a number in a report can be traced to how it was defined rather than re-litigated every month.
- pgvector
- Full-text BM25
- Definition registry
Agents and orchestration
Collection and assembly run on scheduled workers, every run logged and re-runnable. Exceptions raise an alert with the underlying rows attached, not just a red number.
- agent-worker
- Scheduled workers
- Run history
Evaluation
Reconciliation is checked against periods your team has already closed by hand. A report that is plausible and wrong is more dangerous than one that is obviously broken.
- Eval graders
- Reconciliation checks
- quality-worker
Models
Frontier models write the narrative around figures the systems supply, never the figures themselves. Forecasting and anomaly detection are ordinary statistical models with an auditable method.
- Frontier models, one gateway, routed per task
- Statistical forecasting
- Anomaly detection
Data boundary
One reconciled store in your accounts, scoped per team. Finance data does not become a shared dataset and does not train a model.
- Supabase row-level security
- Per-team scoping
- No-training API terms
Named tools in this build: Supabase · Scheduled workers · Claude · Slack · Integration catalog
What that means in practice
Agents and orchestration
AI that does the work instead of just answering: looks things up, calls your systems, completes multi-step tasks, and knows when to hand off to a human.
Where we stop. Multi-agent swarms are oversold; most jobs need one well-guarded loop. If a cron job and a script solve it, that is what we build, because 90 percent per-step accuracy compounds to 59 percent over five chained steps and no framework changes that arithmetic.
Classic and predictive ML
Not every problem needs a language model. Predicting numbers, churn, demand, fraud risk, is usually solved better, cheaper and more explainably with proven statistical ML.
Where we stop. When the input is language, judgment or unstructured documents, classic ML underperforms and we say so. The discipline runs both ways: if your problem is a prediction problem, you will hear that it does not need an LLM from us before you pay for one.
Evaluation and observability
How we prove the AI actually works: measured, monitored and regression-tested like real software, not vibes.
Where we stop. There is no engagement where we skip this. The honest variable is depth: a document pipeline gets faithfulness and extraction suites, an outbound agent gets human review sampling, a classifier gets a held-out test set. We size the harness to the risk, never to zero.
Questions? Straight answers.
Also built here
Three ways in. No long discovery.
Paid · Fixed scope
First Build
One high-ROI system, built on your real data and deployed in your stack, with your team trained to run it. Fixed scope. Quoted after the audit. Covered by the 5x ROI guarantee.
Scope my first build
Paid up front. Cancel anytime after.
Free · 15 min
Intro Call
Fifteen minutes with James, not a sales rep. Bring your worst bottleneck, leave with a straight answer.
Book 15 minutes
No pitch deck.
Every first build is covered: 5x ROI in 30 days. Or we work for free. Read the full terms
Start free. Know your number in five days.
A 3 to 5 day audit of your operations, ending in a plan with the ROI math attached. No obligation.