Weaving AI for Organizations

Most AI gets bolted on. We weave it in.

Built into how your team already operates with
Excel spreadsheets.

33%

of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024, highlighting the rapid shift toward autonomous workflows.

~80%

of organizations layering AI onto existing processes see no material gains. High performers are 3× more likely to have fundamentally redesigned their workflows before selecting a single model or tool.

Phase 1: Discovery

Where are you today?

Most organisations already know the answers. Few have seen them written down.

How does your team handle their day-to-day tasks?

What happens when a recurring task lands on the team?

What does your organization rely on to make key decisions?

Roadmap Assessment

Complete the assessment above

Please answer all three questions above to generate your customized operational roadmap.

Answered 0 of 3 questions
Phase 2: Build

What we build with you

We start with the workflow you run most often, connect the systems it touches, and remove the manual steps in between.

How the day runs
Tools
Workflow
Today

No two people do it the same way.

After Build
123

Same steps. Every time. Anyone can run it.

How you find out
Automate
Capacity
Today
+

You hear about it when it is already on fire.

After Build

Flagged before it reaches you.

What only key people know
Knowledge
Intelligence
Today
+

It lives in a few people's heads.

After Build
+

Structured knowledge. Instantly queryable.

What you end up with
Work that runs without a handoff
Once the three are connected, the job doesn't stop and wait for the one person who knows how it works.
See how this works on a real engineering workflow
Manufacturing

Automating PFMEA Workflows

Discover how we built a PFMEA intelligence system that reviews supplier PFMEAs, and automatically flags potential failure modes and inconsistencies.

Read Full Context

1. Raw Data

Supplier PFMEAs, critical parameters, and past quality issues are continuously ingested.

2. AI Processing

Risk Cross-Referenced
New failure mode detected in Solutionizing process. Verifying RPN severity score.

The Vision Language Model structures data and highlights missing failure modes and controls.

3. PFMEA Reviewed

Generated_PFMEA_Review.xlsx
Process
Failure
Sev
RPN
10. Welding
Porosity
8
240
20. Assembly
Fastener drop
5
45
30. Coating
Uneven apply
3
24
Ready for Engineer Review

Engineers review the SQE report generated instead of manually verifying each document.

Operations

Managing Field Operations at Scale

Learn how we transformed distributed field operations into a unified system with real-time visibility, asset traceability, and automated compliance reporting.

Read Full Context
1. Field Inputs
Survey data
Field agent → fragmented form
No shared structure
Feasibility
Offline, delayed
Low connectivity
Installation
Hardware logged separately
No customer link
Inventory log
Panel / inverter serials
Floating loose
Survey forms, geolocation pins, and hardware barcodes recorded offline.
2. Platform Upload
Customer record
Single source of truth
Survey captured
Stored offline● syncing
Hardware linked
Subcontractor scoped
Data structure binds separate actions to a single customer record immediately.
3. Report and dashboard
Government report
Auto-generated from structured records
0 consolidation days
Live ops dashboard
All subcontractors, real-time
Scoped per subcontractor
Audit trail
Always ready. Not assembled at deadline.
Asset-level traceability
Compliance and traceability are automatic, requiring zero manual reconciliation.
Intelligence

Building Company Brain

The organizational intelligence layer that continuously learns from engineering knowledge

Read Full Context
01-02

Capture & Discover

1. Capture Knowledge
PLMERPMESDocumentsEmailsMeetingsSensors
2. Discover Patterns
Connect related information
Detect hidden relationships
Reveal recurring patterns
Preserve engineering knowledge
Organizational Memory
Connected knowledge across people, systems, and projects.
03

Understand & Recommend

Awaiting Knowledge
Ingesting and connecting engineering context from capture stage...
3. Understand & Recommend
Why did Line 4 joint fatigue occur?
Consolidating:
Email Specs MES
Results
Root causeIdentified
Similar incidentsFound
ImpactEvaluated
Recommendation

Torque specification mismatch detected between engineering change and production settings.

Org. data Verified
04-05

Act & Learn

4. Execute Actions
Approve CAD change ticket
Calibrate torque controller
Generate CAPA report
Notify engineering teams
5. Continuous Learning
Continuous Learning

Every action strengthens the Company Brain.

Feedback LoopActive loopback ←
WHAT YOU LEAVE WITH

A conversation. A document. A result.

One direct conversation about how your operation currently runs, and a written account of where it can improve.

45 minutes
The first conversation

One direct conversation about how your team currently works. You talk about your actual processes. We map what we hear.

Within 2 weeks
Your functional spec

A written document specific to your operation. Where your process gaps are. What to address first. What a realistic first result looks like.

A verifiable result
Proof of value

When we work together, the first milestone is a result you can measure yourself. Time saved per cycle. Manual steps removed. Decisions optimized.

Proven Outcomes

Results from our production deployments

System Enabled

Boiler Project Workflows

Eliminated a 4-hour BOM-to-dispatch cycle through a single connected workflow, replacing 7 manual spreadsheets.

Time to proof of value
8-10 weeks
Time to ROI
5 mins
Read Case Study
Quality Intelligence

Automotive PFMEA Reviews

Transforming PFMEA reviews by embedding historical quality data into an automated analysis pipeline, cutting review time from 6 hours to 15 minutes.

Time to proof of value
3-4 weeks
Time to ROI
15 mins
Read Case Study
Procurement Workflows

Automated Tender Bidding

Automating tender document parsing, submission prep, and side-by-side rate comparison across competing contractors.

Time to proof of value
2-3 weeks
Time to ROI
15 mins
Read Case Study

Have a direct conversation.

Just a direct engineering-led discussion about your current workflows and where AI can actually deliver returns.