Reduce costs · AI & Automation
Put AI to work where it pays.
AI platforms are powerful but generic. The savings come from fitting them into real workflows, with real data, for real people. That takes design as much as engineering, and it’s what we’ve done for 25 years.
If this sounds familiar, we should talk.
- Skilled people spend hours re-keying data and chasing status.
- Data lives in five systems and nobody trusts the dashboard.
- You have AI pilots but no measurable return.
- Legacy software is expensive to run and painful to use.
What’s included
From first use case to running in production.
We find the work that slows your people down, then design and build the AI, automation and custom tools that remove it. Because we design around the people who use them, they actually get adopted.
AI opportunity & governance
We find and size the use cases worth doing, then help you set up the policies and AI council to do them safely.
Agents & assistants
Assistants and agents built into the tools your teams already use, grounded in your data, with people approving what matters.
Workflow automation & integration
Connect CRM, ERP, payments, forms and approvals so work moves without manual handoffs.
Custom software & portals
Internal tools, customer portals and web apps built around the way your business actually runs.
Data, dashboards & decisions
Turn scattered data into dashboards people trust and act on, from operational KPIs to predictive models.
Modernization
Replace or rebuild legacy systems without disrupting the business, then keep them secure, supported and improving.
How we keep AI accountable
Start with a baseline
We measure hours, costs, error rates and cycle times before we build, so the savings are real, not estimated.
Keep people in charge
Agents draft, route and recommend. People approve the decisions that matter.
Protect the data
Private deployments, role-based access and the SOC 2 Type II audited controls behind everything we run.
Results, not promises.
- SSM HealthSimplified how a 32-hospital system aggregates and acts on mission-critical KPIs.Read the case study
- HarrisLogicA dashboard that helps caseworkers predict recidivism and allocate resources in real time.Read the case study
- NikeA candidate evaluation app that made HR easier to manage at global scale.Read the case study
How every engagement starts
First, we get to know your business.
Before we design or build anything, we onboard: we learn how your business makes and spends money, who the work is for and what success looks like. Everything after is built on that.
- 01
Learn the business
Conversations with your stakeholders and the people who’ll use what we build, plus a look at your systems and data.
- 02
Agree on success
The outcomes that matter, the numbers we’ll measure them by and where they stand today.
- 03
Plan the work
A prioritized plan and the right team to deliver it, so the first thing we build is the thing that matters most.
Questions
Straight answers.
Where should we start with AI?
With a problem that costs real money, not with a technology. Every engagement starts with onboarding, where we learn your operations and data and find the use cases worth doing.
Do we need to replace our existing systems?
Rarely. Most of the value comes from connecting and improving what you already have. When something does need replacing, we plan the migration so the business keeps running.
Which AI models and platforms do you use?
We’re platform-agnostic. We choose models and tools, commercial or open-source, cloud or private, based on your data, your security requirements and your budget.
How do you measure ROI?
We agree on a baseline before we build: hours spent, cost per transaction, error rates, cycle times. Then we measure the same things after launch and report back.
Two more ways we create value.
Have a problem worth solving?
Tell us where money is being lost or left on the table. We’ll tell you honestly whether we can help, and what it would take.