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.
AI intelligence engine
Every target found, scored and synced before the Monday meeting
Discovery, enrichment, scoring and movement alerts, written into the CRM you already run. People decide. 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
The target list is whoever someone remembered.
Coverage depends on who searched last and what they typed. The market does not wait for the next research sprint.
02
Qualification lives in one person's head.
The screen exists, but it is applied from memory. Two people rate the same company differently and nobody can say why.
03
You hear about the trigger event after your competitor did.
A role change or a filing is a reason to call. By the time it comes up in conversation, someone else has made the call.
04
The CRM tells you where deals are, not where to look next.
It records history. It does not rank the market, so every quarter's pipeline starts from a blank page.
What we build
01 · Discovery
Companies and people matching your screen are found continuously from registries, filings and news, not once a quarter when someone has time.
02 · Enrichment
Each target's contact and company record is assembled from multiple sources and kept current, so a call starts with the facts in hand.
03 · Scoring
Every target is scored against criteria you set and can read. The score shows its reasoning, so your team can argue with it and tighten it.
04 · Movement detection
Role changes, filings and other signals are watched around the clock. When a target moves, the right person hears about it that day.
05 · CRM sync
Qualified targets land in your CRM with the score, the reasoning and a short brief attached. No re-keying, no second system to check.
What's included
- Your screen turned into scoring criteria you can read and change
- Discovery, enrichment and movement detection running on a schedule
- Sync into the CRM you already run, with the reasoning attached
- Your team trained to tune the criteria without us. Cancel anytime
Where this lands hardest
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
Write the screen down first.
The build starts by turning how you qualify today into criteria the system can apply and your team can read. A scoring model nobody can inspect is a gut feeling with extra steps.
Step 02
Score a market you already know.
The first ranked list covers accounts your team can check by hand. Coverage widens once the ranking holds up against the judgment of your best people.
Step 03
Then let it watch.
Movement detection and CRM sync turn on once the scores are trusted. An alert from a system nobody believes yet is noise.
What we will not automate
Ranking is not deciding. The system never moves money, never contacts a target on its own, and never buries the reasoning behind a score. Who to pursue stays a human call.
What that means in practice
Retrieval and RAG
Connecting AI to your actual documents so it answers from your knowledge, accurately and with citations, instead of making things up.
Where we stop. A dedicated vector database is justified by scale, not by default. Most of RAG quality is won or lost in chunking and indexing strategy, not in the model choice, and we have walked clients back from RAG to plain search when that was the honest answer.
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.
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.
Questions? Straight answers.
Also built here
Three ways in
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.