01
Priority workflow
Start with the project already on your mind: estimating, reporting, billing, CRM cleanup, or another workflow costing time.
Buildout path
Trusted AI that compounds.
Fabric starts with the AI initiative already worth solving, then expands into the data, context, and systems that make your business more intelligent over time.
02
Intelligence audit
Assess the organization as it exists today: what’s captured, what’s tribal, and what’s missing.
03
Fabric Signal OS™
Turn scattered data, context, and permissions into a trusted intelligence layer the business can query.
04
Ongoing buildout
Every new signal becomes a launchpad for higher-ROI workflows, smarter agents, and better decisions.
Phase 1
Make the AI idea real.
Your team already has an AI initiative being discussed — a prototype, hunch, or workflow leaders believe should change how work gets done. Fabric turns the highest-potential idea into a trusted system that saves time, proves ROI, and reallocates capacity.
Turn a manual, repeatable workflow into a reliable AI-assisted process with human review, source references, and clear operating rules.
Example Estimate Agent
Estimating
Scanning for opportunities...
Daily scan
Found a warehouse retrofit bid due Friday with a strong prior-job match and fresh vendor pricing. Starting a priority estimating run.
Opportunity detectedWarehouse retrofit estimateStrong prior-job match · pricing coverage ready · medium win likelihood
Assembling bid packet
Assemble packet
I found the priority estimating workflow and packed the source material into one working context.
Priority workflowWarehouse retrofitKnown estimating workflow
Bid inviteDue FridayOwner requests base + alternates
Drawings42 sheetsArchitectural, structural, electrical
Scope notesDivision 07Roofing, insulation, flashing
Prior estimate2024 jobSimilar warehouse retrofit
Vendor pricing3 updatesMembrane, fasteners, labor
Reading drawings and scope notes
Extract context
I converted the attachments into estimate-ready facts and linked them to prior work.
Due dateFriday 5PM
Project typeWarehouse retrofit
Primary tradeRoofing membrane
Pricing baseline2024 retrofit job
Known riskElectrical allowance
Running takeoff and pricing checks
Human review
I paused on judgment calls instead of hiding uncertainty in the estimate.
Needs reviewConfirm membrane specUse 60 mil TPO baseline?
Needs reviewElectrical allowanceOwner-provided or contractor-held?
Needs reviewOptional roof drainsExclude from base bid?
Pricing line items and finalizing draft
Draft estimate
Draft estimate$428K
DIV 07Roofing membrane$186K
DIV 05Structural misc.$74K
DIV 26Electrical allowance$91K
ALT 01Roof drain option$28K
Confidence: 82%Open questions: 4Time saved: 6.5 hrs
Ready for human reviewsource references attached
Finding best team members for review
Route for review
I’m contacting Marcus Reed in Estimating and Elena Park in Operations on Slack to get their input and finalize the estimate. I’ll follow up with them by email if I don’t hear back.
Slack reviewMarcus Reed · EstimatingMembrane spec and exclusions
Slack reviewElena Park · OperationsElectrical allowance and site constraints
Pausing until I hear back from or follow up with Marcus and Elena
Phase 2
Map your intelligence gaps.
We assess the systems, workflows, documents, decisions, and tribal knowledge behind your business to identify what signal is captured, what gets lost, and where better capture, structure, and access would create the highest ROI.
See Fabric’s approach to contextSignal Audit Console
CRMFront-of-mind pipeline
messyWarehouseStructured analytics data
capturedERPFinance ledger
capturedMeetingsRecorded decisions
missingDocsSOPs and scopes
partialPeopleTribal rules
tribalIntelligence auditfind gaps+ ROI
42%captured
18messy
31tribal
9missing
82
Fabric readinessImplementable now with 4 priority fixes
Clean CRM hygieneHigh ROI
Capture estimator rulesFast unlock
Connect finance ledgerTrust layer
Build context layerQueryable warehouse
"We implemented an AI chatbot with Fabric that queries our internal data warehouse via SQL, and the results exceeded expectations. It bridges complex data infrastructure with a simple, user-friendly experience and consistently delivers actionable insights."
Phase 3
Deploy Fabric Signal OS™
Stand up a private, governed context layer around your warehouse, documents, definitions and permissions. Signal OS gives teams on-demand answers they can trust while every approved workflow, correction, and usage pattern makes the system safer and smarter.
See Fabric Signal OS™Demo Warehouse Agent
Phase 4
Launch more reliable agents.
Once the trust layer is in place, deploy agents across more workflows with confidence. Each agent builds on governed data, relevant access, and reusable business context, making automation safer, faster, and more reliable.
Book a CallAgent command network
Signal
OS
Estimator Agent
ReadyBid coverage
Project Controls
RiskSchedule risk
Cashflow Agent
WatchPay app health
Change Order Agent
OpenCO exposure
🎛️ FAQ
What teams usually ask first
What does Fabric actually build?
Do we need a perfect data warehouse before starting?
How does your intelligence assessment relate to Signal OS?
Is Fabric only for analytics questions?
How do you keep AI outputs trustworthy?
Who is Fabric built for?
