Frequently asked
Fascia Labs is an AI Operating Intelligence & Transformation Studio. These are the questions founders, operators, and executives ask us most often.
AI Operating Intelligence is the use of AI, workflow analysis, data mapping, and decision-system design to improve how a business actually runs. Instead of treating AI as a chatbot or productivity add-on, it embeds intelligence into workflows, customer experience, employee experience, revenue loops, retention, and operational decision-making.
Automation agencies usually focus on automating specific tasks or building tools — chatbots, forms, workflow automations. Fascia Labs looks at the broader business system: workflows, handoffs, customer experience, employee experience, data flows, retention, revenue leakage, and decision bottlenecks. We diagnose first, then design AI-enabled changes that fit the actual operating system.
B2B service businesses, SaaS and subscription teams, agencies, recruiters and staffing firms, consultants, and professional-service operators — usually 10 to 500 people — where growth is being capped by how the business runs rather than by demand.
A free 20-minute working call where we surface the most visible workflow friction, revenue leakage, data gaps, or decision bottlenecks in your business, and tell you whether the AI Operating Intelligence Diagnostic is the right next step.
Both. We provide diagnostic thinking, strategy, and roadmaps, and we guide implementation — including AI-enabled workflow design, decision systems, and vendor selection. Build work happens with internal teams or selected partners. We stay accountable for the operating outcome rather than billing for tool delivery.
The AI Operating Intelligence Diagnostic typically runs 7–14 days end to end. The Workflow & Data Friction Audit runs 5–10 days. The Experience Transformation Sprint runs 10–21 days. Timeline depends on organization size and access to operators.
Operator and stakeholder interviews, end-to-end workflow mapping, data flow review, decision-intelligence review, revenue and retention friction analysis, an AI opportunity backlog scored by impact and effort, a prioritized transformation roadmap, and an implementation plan with sequencing and ownership.
Diagnostics are CAD $1,500–$2,500, with CAD $500–$750 beta pricing for selected early clients. The Workflow & Data Friction Audit is CAD $1,000–$2,000. The Experience Transformation Sprint is CAD $3,000–$5,000. Fractional AI/Product/Ops partnerships are CAD $1,500–$5,000 per month depending on scope.
No. Messy data is usually one of the things we surface and address. Part of the Diagnostic is mapping where data is scattered, duplicated, or unreliable, and identifying what needs to be cleaned, consolidated, or instrumented before specific AI opportunities are practical.
Yes. We are vendor-neutral. Tool selection happens after diagnosis, scoped to the workflows, decisions, and data flows that will actually use the tool. We help evaluate options against your operating context rather than chasing the most-hyped platform.
Yes — through a Transformation Partnership or fractional AI / product / ops leadership. We guide redesign, sequencing, vendor selection, and rollout while developing internal capacity, so the operating improvements continue after the engagement ends.
No. No consulting diagnostic can ethically guarantee revenue outcomes. Fascia Labs focuses on identifying practical opportunities, reducing operating friction, improving decision quality, and designing implementation-ready AI-enabled systems that can support better business performance.
Yes. Fascia Labs is remote-first, based in Canada, working with clients across Canada, the United States, and globally. Engagements run on video with shared documents and workflow maps; in-person visits are available for larger Transformation Partnerships.
B2B service businesses, SaaS and subscription teams, agencies (creative, marketing, consulting), recruiting and staffing firms, professional services (legal-adjacent, financial-adjacent, healthcare-adjacent), and operator-heavy SMBs. We work less well with very early-stage startups still searching for product-market fit.