Services, named after the problem
Each engagement starts with data you already collect and ends with a measured result. Technology is chosen after the assessment, not before.
Operations intelligence
Named after the problem on the floor or in the office. Forecasts, alerts and reports built on the data you already collect.
- Predictive maintenanceKnow which machine will fail, days before it does.2-week assessment
- Waste and qualityFind what actually causes scrap and rework, and reduce it.2-week assessment
- Resource planningRight people and machines at the right time, from your own demand history.2-week assessment
- Growth and demand intelligenceRead the signals in your sales and customer data before your competitors do.2-week assessment
AI and ML engineering
When the problem needs a model, an agent or an automation built, we build it, test it against your own cases and hand it over.
- Custom model buildingA model trained on your own data, for one decision your business makes every day.2-week assessment
- AI agents and evaluation harnessesAgents that do multi-step work in your systems, with a test harness that proves they do it right.2-week assessment
- Workflow automationTake the repetitive back-office work off your team, one flow at a time, with a person where it matters.2-week assessment
- Document intelligenceTurn invoices, forms, registers and reports into clean data you can use.2-week assessment
- Visual inspection with camerasA camera and a model watching the line for defects, counts and safety, where a person cannot look all day.2-week assessment
- AI assistants for operationsAnswer the questions your staff answer all day, from your own documents, with guardrails.6 to 8 weeks to a supervised pilot
Data and platform
The layer underneath. Most engagements start here, and every system above depends on it staying honest.
If you are not sure which of these applies, start with the readiness check. It is also the honest answer for most growing businesses: data foundations first.
Which one do you need
Problems rarely arrive with a service name attached. Find the line that sounds most like yours.
| If your problem sounds like | Start with |
|---|---|
| A machine stops and the whole order waits | Predictive maintenance |
| Scrap is eating margin and nobody agrees why | Waste and quality |
| Overtime one week, idle hands the next | Resource planning |
| Customers drift away and we notice too late | Growth and demand intelligence |
| One decision, made all day, by whoever is on shift | Custom model building |
| Someone showed us an AI demo and we are not sure it works | AI agents and evaluation harnesses |
| Three people spend the day retyping and forwarding | Workflow automation |
| Our data arrives as paper, PDF and scans | Document intelligence |
| An inspector watches the line all shift | Visual inspection with cameras |
| Our team answers the same fifty questions every day | AI assistants for operations |
| Every report takes a week and two numbers disagree | Data foundations |
| We have models already and nobody knows if they still work | ML operations and monitoring |
How every engagement runs
The shape is the same across all twelve services. The vocabulary, the data and the model change.
- Phase 1
Understand
On the floor or in the office, with the people who do the work. One to two weeks.
- Phase 2
Assess
Samples pulled, quality checked, a go, fix-first or no-go verdict. Two weeks, fixed fee.
- Phase 3
Pilot
One line, one flow, one document type. A measured before and after. Six to twelve weeks.
- Phase 4
Deploy and support
In your environment, with your people trained. Monthly reviews, retainer optional.
Questions about the services
Which service should we start with?
Most businesses start with the readiness check, which is free and takes twenty minutes, or with data foundations. If one line in the table above sounds like you, start there and the assessment will tell us whether the data supports it.
What is the difference between an assistant, an automation and an agent?
An assistant answers questions from your documents. An automation runs fixed steps on predictable inputs. An agent takes several steps with judgement in between and uses your systems to finish a task. They cost more in that order, and most problems need the cheaper one.
Do you build with large language models?
Where the input is text, documents or conversation, yes, as one component with a test set around it. For predictions on tabular business data a smaller model trained on your records is usually more accurate, cheaper and easier to explain. We choose after the assessment, and we show you why.
Can we combine services?
Usually one at a time. Each service page lists what it is often combined with, and the typical order is foundations, then one operations problem, then the engineering that problem needs. A pilot proves the first before the second starts.
Where do these systems run?
On your servers or your cloud account. Source code, models and documentation are handed over, and nothing stops working if we part ways.
12 services across 3 groups.
Not sure if your data is ready?
Take the free 20-minute readiness check. You get a short scorecard and a plain answer about what, if anything, is worth doing next.