Where should we start?
Choose the first step that fits you today.
Align leaders on a systems-first model and identify where intelligence belongs in the architecture.
Explore AI (Cohort Workshop)
Map processes and systems (data lineage, integrations, access) to select a feasible PoC.
Scope & Diagnose
What the AI Operating System Does
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Connects systems with defined interfaces and data contracts so teams and workflows act as one.
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Orchestrates agents with routing, retries, and human-in-the-loop handoffs.
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Reasons using policies, rules, and versioned prompts tied to governance.
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Improves via continuously feedback loops that retrain models and update SOPs.
Our Approach
The 3-Layer Model (How it all fits)
Together, these layers create a unified system of intelligence, where data, decisions, and action move in harmony.
Business Layer
Where people, processes, and tools create value
AI Operating System
The coordination & governance layer across all functions
AI Agents
Intelligent apps that execute tasks and decisions
AI adoption is a system: memory, conductor, brain, context, and safety, wired together with contracts and controls.
Core
The 5 Core Components of the AI OS
Your core data (CRM, Finance, HR, Projects): the single source of truth.
Platforms (e.g., MindStudio, Lindy) that route tasks and manage agents.
Models + prompts + business rules that interpret and decide.
SOPs, policies, domain docs that teach AI how your business thinks.
Oversight, versioning, compliance, and auditability by design.
AI Exploration Workshop (Cohort)
Best for:
Ecosystems, leadership teams, and later-stage companies aligning on a systems-first approach.
What’s included:
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90 -120-minute facilitated session (onsite/virtual)
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“First 90 Days with AI” worksheet for your team
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Decision & actions summary
Outcomes:
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Shared vocabulary (business → OS → agents)
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A prioritization rubric (value × feasibility × risk).
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A shortlist of 3 candidate workflows with quick notes on data access, governance needs, and owners.
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A clear next step (Company Diagnostic or OS Blueprint) based on readiness.
Timeline:
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1- 2 weeks scheduling lead
Investment:
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Starting at $4.5K (flat rate; scope-dependent)
Scoping & Diagnostic (Company-Specific)
Best for:
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“Where do we start?” or unclear data/CRM readiness.
What’s included:
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Stakeholder interviews
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Process mapping
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Tool review
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Data sampling checks
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KPI & risk alignment (HITL points).
Outcomes:
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Readiness Report: data lineage, contract quality, identity/access, governance gaps
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Use-Case Matrix: value × feasibility given current systems
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Recommendation: PoC vs OS Blueprint vs Readiness, with required system changes
Timeline:
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2 weeks
Investment:
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Starting at $6K (size-based)
AI OS Blueprint (Company-Specific)
Best for:
organizations ready to define the operating layer and rollout plan.
Outcomes:
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5-component architecture
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integration & governance diagrams
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90-day rollout roadmap.
What’s included:
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Interface specs (APIs/queues) and data contracts per integration
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Decision policies (approval paths, escalation) serialized in the KB
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Observability plan (events, metrics, SLOs, dashboards)
Timeline:
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3–4 weeks
Investment:
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Starting at $7.5K
Proof-of-Concept Build (1Agent /1 Workflow)
Best for:
Organizations that want to see one real workflow run by an AI agent before scaling.
What’s included
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One working agent for a single, agreed workflow (staging or limited production).
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Controls: required human approval for risky steps, plus a pause/rollback switch.
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Runbook & SOP so your team can operate and update the workflow.
Outcomes
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The workflow completes within the agreed time target.
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Approvals are enforced; blocked actions can’t proceed.
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Business owner provides UAT sign-off.
Timeline:
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3 - 4 weeks from kickoff (includes setup, testing, pilot runs, and handover).
Investment:
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Starting at $7.5K
Our AI Service Architecture
Design and implement the architecture, protocols, and intelligence infrastructure that define how AI operates across your organization.
Turn your AI OS Blueprint into live agents and workflows that deliver measurable results.
Maintain, monitor, and continuously improve your AI ecosystem for peak performance and scale.
Train your team to operate with AI safely, effectively, and at scale (company-specific).
Proof that systems-first wins
We measure behaviour of the whole system, not just a task. Ranges reflect typical results after data contracts and access are in place; we set baselines during Diagnostic.
Decision latency SLO:
in the target workflow (post-integration)
↓35 - 60%
Data lineage coverage: for inputs powering the agent (tracked in KB)
+100%
Integration depth :
systems per workflow with typed contracts & retries
3–7
Governance conformance: actions with audit trail + versioned prompts
≥95%
AI adoption is not teaching staff to prompt. It’s building governed, auditable AI workflows that run with minimal prompting.
Our AI Service Architecture
Build: Design the architecture & governance layer
Deploy: Activate agents & intelligent workflows
Run: Operate, observe, and improve the system
BUILD:
AI Operating System Setup
Build the coordination and governance layer that powers your entire AI ecosystem.
Design and implement the architecture, protocols, and intelligence infrastructure that define how AI operates across your organization.
Deliverables
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Phase 1: Discovery & Design
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Phase 2: Build & Integration
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Phase 3: Activation & Training
DEPLOY:
AI Implementation
Deploy the intelligence that runs on your Operating System.
Turn your AI OS Blueprint into live agents and workflows that deliver measurable results.
Deliverables
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Phase 1: Planning & Design Alignment
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Phase 2: Agent Development & Configuration
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Phase 3: Testing & Validation
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Phase 4: Handoff & Enablement
RUN:
AI Systems Management
From running your AI to making it think smarter.
Maintain, monitor, and continuously improve your AI ecosystem for performance and scale.
Deliverables
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Performance monitoring (latency, accuracy sampling, uptime, adoption)
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Agent lifecycle (training, upgrades, retirement)
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Workflow tuning & new-tool integrations
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Governance & compliance maintenance
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Quarterly innovation reviews + upgrade roadmap
Empower:
AI Training for Organizations
Company-specific training that turns your workflows, SOPs, and data into repeatable AI capabilities.
What Makes This Different
Custom, not generic. Built around your processes.
Hands-on labs. We co-design a real workflow your team can run.
Governed & safe. Guardrails, approvals, and QA
Measurable. Clear goals, before/after benchmarks, and an outcomes report.
Programs (pick any or combine)
AI Foundations for Leaders
Department Workflow Lab
AI Operations Practicum Monitoring
Format:
Workshops • working sessions • coaching • lectures Delivery: onsite, virtual, or hybrid • 4–10 weeks (flexible)
Deliverables
Training plan customized to your business
AI Playbook
Use-case spec + pilot SOP with acceptance criteria
Operations checklist
Outcomes summary for leadership
Optional Training Funding (Canada)
If your employees are in Canada, your training component may be eligible for partial reimbursement through third-party programs (e.g., Scale AI). Funding is optional and separate from our services. Eligibility and amounts are determined by the funder.
What we do:
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Prepare the course plan and budget for your application.
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Align the training scope to program rules (custom, company-specific).
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Coordinate timelines so training starts after approval if required.
Frequently asked questions FAQs
Some amazing organizations we have worked with
