Inquiries wait for the person who knows how to interpret them.
01A technically complex request can sit idle while someone works out the configuration, history, and right owner.
First point of frictionAI systems for manufacturing operations
AI systems for inquiry handling, supplier operations, field service, and engineering knowledge in manufacturing.
Supply / serviceScroll to explore
You are losing time and attention in predictable places.
The specifics differ. The same operational friction keeps appearing wherever this work crosses people, records, and decisions.
Quotes rely on scattered knowledge.
02Pricing, lead times, technical constraints, and supplier availability are rarely available in one place when a customer needs an answer.
Recurring operating costSupplier performance is visible only after something goes wrong.
03Quality, delivery, and responsiveness data exists, but not in a view that helps teams act before a shortage or defect escalates.
Recurring operating costMarket signals arrive too late.
04Teams hear about customer moves, competitor activity, and supply changes through manual research that cannot keep pace with the market.
Recurring operating costField service documentation does not return as usable knowledge.
05Technicians record valuable context, yet the next technician often cannot find it when the same issue appears.
Recurring operating costEngineering knowledge is hard to access on the job.
06Drawings, specifications, parts data, and decisions sit across systems and folders while operators need an answer now.
Recurring operating costThe operational lifecycle of manufacturing.
Before we propose a system, we map the recurring surfaces of the operation and the work that moves through them.
01
Interpret
Read an inquiry, issue, or supplier signal with the relevant technical and commercial context.
- Inquiry
- Part
- Supplier
02
Connect
Bring product, service, quality, and availability information into one reviewable work item.
- Part
- Supplier
- Service
03
Coordinate
Route tasks and exceptions to engineering, sourcing, service, or commercial teams with clear ownership.
- Supplier
- Service
04
Improve
Capture the decision and outcome so the next team can act from the evidence already earned.
- Service
AI is infrastructure, not a replacement for your team.
The operation keeps its judgment. AI takes on the repeatable, system-to-system work that makes good people spend their time on administration.
AI handles
Repeatable work that slows the team down.
- Technical inquiry preparation
- Supplier and market signal monitoring
- Service record structuring
- Inquiry preparation and routing
- Part preparation and routing
- Supplier preparation and routing
Your team handles
The judgment, relationships, and accountability.
- Engineering and safety decisions
- Supplier negotiation and approval
- Customer commitments and pricing
- Anything irreversible or high consequence
Anything irreversible passes through a human.
The system can draft, organize, and surface the work. An authorized person decides and acts.
What we actually build.
Operational systems that connect the tools you already use. Not chatbots sitting next to the work.
System / 01
Inquiry and quote engine
Read a technical inquiry, assemble the relevant product, pricing, and availability context, and prepare a reviewable response for the commercial and engineering teams.
System / 02
Supplier performance scorecard
Connect delivery, quality, cost, and responsiveness data so sourcing teams can see trends before they become an operational failure.
System / 03
Market scouting agent
Monitor agreed sources for customer, competitor, and supply-chain signals, then route the evidence to the person who can decide what to do with it.
System / 04
Field service documentation
Turn service notes, photos, and work orders into structured history that the next technician can search and verify before a visit.
System / 05
Parts and engineering knowledge assistant
Give authorized teams a source-linked way to find specifications, compatibility information, and past engineering decisions without digging through disconnected repositories.
System / 06
Voice-of-customer aggregator
Bring together customer feedback from service, sales, and support so product and operations teams can see recurring issues while they are still actionable.
In production.
One representative example of an operating system shipped around a real workflow.
Manufacturing · Case study
Technical enquiries answered from connected engineering knowledge
Sales, service, and engineering teams spent too long finding compatible parts, prior decisions, supplier information, and service history.Fits into the stack you already run.
We design around the systems of record. Integration scope comes from the real workflow, access rules, and decision boundaries - not a platform replacement plan.
Operations
- ERP
- Quality system
- Supplier data
Engineering
- PLM
- Drawings
- Technical documents
Field and commercial
- CRM
- Service platform
- Customer communications
Workflow & data
- Workflow engine
- Secure data store
- Reporting
- Automation
How we think about AI inside the operation.
The principles we use when we design, ship, and improve a system alongside the people who run it.
AI is operational infrastructure.
The work still belongs to your operation. We make the repetitive path reliable and visible.
Accuracy is the floor.
Each system needs a measurable baseline, source traceability, and a clear way to improve when it is wrong.
Operational fit beats novelty.
The best system sits inside the tools and habits your team already depends on.
People own judgment.
The system can prepare, route, and remember. Your team keeps the decisions that carry consequence.
Same approach. Different language.
How we apply the same operating discipline in other contexts.
AI for E-commerce
Support / retention
AI systems for customer support, retention, order exceptions, and revenue recovery as volume grows.
Explore →AI for Financial servicesKYC / reporting
AI systems for KYC, onboarding, fund administration, reporting, and controlled compliance operations.
Explore →AI for HealthcareClaims / patient ops
AI systems for revenue cycle, claims, documentation, and patient operations with controls that fit care delivery.
Explore →Questions,
answered.
The things teams ask before the work begins.
Where should we start?+
Start where the work is frequent, visible, and costly when it goes wrong. The first mapping session identifies the people, systems, data, and controls around that workflow.
Do we need to replace current systems?+
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
How do people retain control?+
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
How long does the first system take?+
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
How do we measure whether it works?+
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
What access does the system need?+
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
How is operational data protected?+
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
Can the system work across several teams?+
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
What happens after the first system ships?+
We define the workflow, decision boundary, and system access before building. The first production system is designed to fit the operation and give the team a measurable result.
Start with the work
Bring the workflow that needs attention.
Pick a time for a working session or send a short brief. Either way, we will come prepared to understand where the work gets stuck.
Talk through the work
Book a 20-minute consultation.
Bring the workflow that feels slow or fragile. We will determine whether it is a sensible candidate for an AI system.
Bartosz LuderaFounder, HarnessloopSend a workflow brief
Prefer to write it down?
Tell us where work waits, repeats, or falls through the cracks.