AI assistants & agent workflows
Build assistants that answer from approved information and agents that carry out defined steps using permitted tools. Give people review points, source references, and a way to handle exceptions.
Practical examples for your business.
01
Failed inspection → hold and notify
A failed result puts the lot or order on hold in the ERP or quality system, notifies quality, and attaches the images.
The problem: An inspection fails, and the line lead has to tell quality and update the lot status by hand.
02
Label and barcode checks → shipping
A camera or scanner reads the label and barcode, compares them with the order, and flags mismatches before the order ships.
The problem: Labels are checked by eye before orders ship.
03
Inspection data → records
Results are written to your quality or ERP records automatically, so they can be reported on later.
The problem: Inspection results live in the vision system and are copied into quality records.
A practical approach
Connect the tools your team already uses.
The right starting point depends on your people, information, and business goals. We agree the scope together.
- Common systems: Cognex, Keyence, Azure AI Vision, AWS Rekognition, Google Cloud Vision, Roboflow
- Read results from inspection systems and cloud vision services
- Create holds, alerts and quality records from results
- Attach images to the related order or lot
- Send uncertain results to a person
- Agree on data access and review steps before implementation
- Define a clear scope, price, and handoff together
Systems around the workflow
Build around your existing vision ai tools.
Connect camera and inspection results to your quality, inventory and ERP workflows. We identify the source of truth, permitted access, field mapping, and a safe way to handle failed or repeated requests.
- Cognex
- Keyence
- Azure AI Vision
- AWS Rekognition
- Google Cloud Vision
- Roboflow
The records and rules
Define the camera or image inputs, environment, eligible visual task, and reference examples. Agree what images may be stored and who can review them.
The staff decisions
Staff review uncertain detections and consequential decisions. A pilot needs representative difficult examples, including lighting changes and unexpected conditions.
What the pilot should prove
Evaluate false positives, missed detections, image quality, changing conditions, unavailable devices, review effort, and the workflow following a detection.
When a connection fails
Keep a traceable record of what was requested, what completed, and what needs review. Agree retry limits, duplicate protection, staff fallback, and the owner who resolves a failed update before launch.
Implementation, end to end
A working process your team can operate.
01
Understand the current work
Walk through a real request with the people doing the job. Identify the source records, decisions, bottlenecks, and exceptions before choosing software.
02
Build a useful pilot
Define a limited starting scope and create a working version. Test representative requests alongside difficult cases, missing data, and failed connections.
03
Launch with clear ownership
Agree access, approval steps, acceptance checks, and a release plan. Show staff how to use the system, review exceptions, and contact the responsible owner.
04
Maintain and improve
Provide the agreed documentation and handoff. Review usage, operating costs, and issues; define support coverage and future changes in the engagement.
Connect the rest of the workflow.
Data foundations & AI readiness
Organize the data and knowledge behind a priority workflow. Identify authoritative sources, clean up agreed records, document ownership, and prepare searchable information or reliable reporting inputs before adding more automation.
Before we begin
The questions that shape your project.
Bring the business problem. We can help translate it into a practical technical plan.
Can you work with our existing software?
We review the systems you use, available APIs or supported imports, subscription requirements, and access permissions. The scope identifies which connections can be implemented and which need an alternative.
What do you need from our team?
A process owner, examples of the current work, and access to the systems included in the scope. Your staff help confirm the business rules, review a pilot, and decide whether the result is ready to use.
How are cost and success agreed?
The proposal defines deliverables, assumptions, responsibilities, acceptance checks, and pricing. Third-party subscriptions and usage costs are identified separately. We choose meaningful measures from your existing process rather than promise an untested result.
What happens after launch?
The agreement defines code and account ownership, documentation, training, deployment responsibilities, and any ongoing support. Staff get a clear way to handle failures and requests for changes.
Let’s talk about what you want to improve.
A short conversation can help you decide where AI and better systems would make a useful difference.