Work.

We become the AI team for companies that can see the opportunity but do not have the people or time to build it themselves. Here’s what that looks like in practice.

These are illustrative engagement patterns drawn from real-world workflows. The details change by industry, but the underlying work often doesn’t. Named case studies will be added as clients approve disclosure.

Annika Lewis and Matthew Hall leading an AI workshop

Prospect Research and Qualification

The problem

A team knows the profile it wants, but the evidence lives across old contacts, paid databases, spreadsheets, web research, and what people remember. Researchers rebuild the same context candidate by candidate, then hand a long list to someone senior to decide what is actually worth pursuing.

What we build

A research workspace that starts with the mandate and known examples. It finds and enriches candidates, shows the evidence behind each suggestion, and records accept, reject, and correct decisions so the next pass starts smarter. The model narrows the field. A person makes the call.

A Northbound presenter demonstrating a practical AI workflow

Financial Model Automation

The problem

Much of finance work repeats monthly or quarterly. The inputs do not. Accounting data, pipeline exports, revenue schedules, payroll, and team-maintained spreadsheets arrive on different timelines and in different shapes. A finance lead spends hours collecting, reconciling, and remembering exceptions before the work requiring financial judgment can begin.

What we build

A monthly update workflow that brings the standard inputs together, refreshes known schedules, traces each number back to its source, and flags missing data or exceptions. It prepares the model for review. The finance lead owns the assumptions, material changes, and final sign-off.

Attendees in conversation at a Northbound event

Purchase Order and Shipment Tracking

The problem

A team has placed the order and the job is moving, but answering three basic questions—what has actually shipped, where is it going, and when will it arrive?—still means digging through an inbox. Vendors send confirmations as emails and PDFs in whatever format they use. Purchase order, job, carrier, tracking number, and ship date sit across an inbox, accounting software, and a project board.

What we build

A workflow that reads shipment confirmations, extracts the useful facts, anchors each update to the purchase order, and matches it against the active job list. High-confidence matches can move forward. Uncertain ones stay visible for a person to resolve before anything changes downstream.

How we work

We do not start with a workshop and end with a roadmap. We find the first thing worth moving, build it, and use what we learn to propose what comes next.

01
Find the first needle-moving thing.

Clients do not need a tidy brief before we begin. They may have one painful workflow, a crowded automation backlog, or a product idea that still needs technical shape. Discovery turns that starting point into a responsible first move.
We get close enough to understand the inputs, decisions, tools, exceptions, and review points. We rank possible starts on business value × operational readiness: whether the outcome matters, the data is accessible, the workflow is clear enough to build against, and someone can evaluate the output.
The first move might be a build, a small skill, a prototype, training, or no build at all. We choose the intervention that fits instead of forcing every problem into the same engagement.
02
Ship the smallest useful loop.

We make something concrete early enough to react to: a working loop, a clickable interface, or a first run against client inputs. Using it shows where the workflow or design needs more work before anyone invests in a larger build.
We work with the tools, files, naming rules, and edge cases already in place. The business does not have to clean up everything or replace its operating system before useful work can begin.
Human review stays wherever judgment matters, and automation stops when confidence is low. We keep the larger vision in view without making the first useful version wait for it.
03
Turn one build into capacity.

The first build leaves behind more than software. It documents where the data lives, how the team names things, what counts as correct, and which exceptions matter. That context makes the system more reliable and the next workflow easier to choose.
What comes next depends on what the first build teaches us. The evidence may support deeper automation, improving an adjacent handoff, or stopping. We keep a shared backlog, and every item has to earn its place.
Over time, the client gets business judgment, product thinking, and engineering capacity without hiring a full-time AI team before the work justifies one.

Start with the problem.

Bring us the bottleneck your team already feels. We’ll find the first workflow worth changing, build it around the way the work actually runs, and keep moving if there’s more worth doing.

Bring us the problem

Prospect Research and Qualification

Financial Model Automation

Purchase Order and Shipment Tracking

How we work

Find the first needle-moving thing.

Ship the smallest useful loop.

Turn one build into capacity.

Start with the problem.