Velo9 trains your people, turns company knowledge into shared context, and builds governed AI systems you own. People and machines work from the same operating memory.
Velo9 gives people, agents, and workflows the same approved knowledge, decisions, and rules.
Before
Every system starts over.
PeopleIndividual prompts
AgentsIsolated context
WorkflowsBrittle automation
Knowledge stays trapped. Work cannot repeat or scale.
After
Context becomes shared.
PeopleTrained
AgentsGrounded
WorkflowsGoverned
Shared context planeHow your company works
Approved sourcesDecisionsRulesHistory
Velo9 delivery products
Built into the work. Chosen for the outcome.
Velo9 engagements include the right mix of Velocity™, V9OS™, and Cadence™ to accelerate adoption across people, shared company context, and governed delivery.
Velocity™
Adoption through live work.
Coaching becomes practice, commitments, reusable artifacts, and a clear handoff.
V9OS™
Context your firm controls.
Approved knowledge, decisions, rules, and sources stay shared across people and workflows.
Cadence™
Delivery with receipts.
Review gates, cost signals, and release evidence keep working systems accountable.
Included by scope. No separate suite purchase. You keep your data, client-specific context, delivered code, artifacts, and operating record.
What we do
Train. Build. Govern.
Velocity™ / AI training
Train.
Upskill your teams to get more out of AI.
Training and coaching meet each team where it is and build capability through work already on the calendar. Every engagement is tailored to the function, industry, AI maturity, and direction of the business.
Advanced AI training
Half-day or full-day, built for your team.
Participants leave with practical skills and a system of action tailored to their functions.
4.81/5 · NPS 81 · 26 responses
“I learned a ton that is actually useful to my work.”
We organize approved knowledge, decisions, and rules, then build useful AI workflows in your environment. Your team owns the context, code, and operating record.
You keepContext + code
Cadence™ / AI governance
Govern.
Move faster while optimizing token spend and controlling risk.
Permissions, approved context, model routing, budget policy, and human sign-off are designed into the work. Observability makes token use, cost, approvals, and decision evidence visible, so unnecessary spend can be reduced.
---
title: Velo9 | Put AI to work the way your company works
canonical: https://velo9.ai/
updated: 2026-08-08
contact: https://app.reclaim.ai/m/velo9/introcall
audience: Mid-market and expert-led teams
services:
- AI training
- AI implementation
- AI governance
delivery_products:
- Velocity™
- V9OS™
- Cadence™
---
# Put AI to work the way your company works.
Velo9 trains people, turns company knowledge into shared context, and builds governed AI systems the client owns.
## Operating thesis
- Move beyond the chat window. Individual conversations are useful, but company capability requires shared context, repeatable work, and accountable ownership.
- AI without company context is guesswork. Approved knowledge, decisions, rules, sources, and history should guide people and systems.
- People and machines improve together. Training, implementation, and governance belong in the same operating loop.
- Agents come last. Define the work, context, access, controls, owner, and evidence before adding autonomy.
- Own your context. Rent the intelligence. Model providers can change where routed interfaces are designed for replacement.
## The operating loop
| Stage | Work | Evidence |
| --- | --- | --- |
| Train | Coach the people closest to live work | Practice, commitments, reusable artifacts, transfer path |
| Context | Organize approved company knowledge | Sources, decisions, rules, history, access boundaries |
| Build | Implement the AI workflow or system | Requirements, code, evaluations, release record |
| Govern | Add permissions, budget policy, observability, and human approval | Usage, cost, sources, approvals, decision evidence |
| Ownership | Transfer the operating loop to a named client owner | Acceptance evidence, playbook, platform, operating record |
## First engagement
- Entry engagement: Architecture workout
- Duration: 1–2 days
- Input: One live workflow, sponsor, access path, decision to make
- Output: People and system map, risks, first measurable outcome, accountable owner, next decision
- Purpose: Choose a bounded result before committing to a larger program
## Services
### AI training
Training and coaching meet each team where it is. Sessions use work already on the calendar and are tailored to the function, industry, AI maturity, and direction of the business.
Advanced AI training is available as a half-day or full-day intensive customized to the client. Participants practice on work relevant to their functions and leave with practical skills, reusable working methods, and a system of action for applying AI in their roles.
Customization inputs: participant level, role, industry, approved tools, company direction, and the work each group owns.
The delivery package can include preflight setup, a branded participant portal, role-specific prompts, live demonstrations, hands-on labs, reference materials, and client-specific working artifacts. Participants leave with one repeatable improvement and a practical way to keep applying it.
Training evidence: across 26 post-session responses, average session value was 4.81/5, Net Promoter Score was 81, and every respondent agreed or strongly agreed that they learned something they could apply to their work.
Client keeps: capability, reusable methods, working artifacts, commitments, and a playbook.
### AI implementation
Velo9 organizes approved company context, defines the workflow and decision rights, then implements AI workflows, internal tools, agents, integrations, or applications in the client environment when included in scope.
Client keeps: client-specific context, delivered code, configurations, artifacts, and operating record.
### AI governance
Governance is designed into the work so approved paths move faster. Permissions, context boundaries, routing, budget policy, human approval, escalation, and observability are defined for the workflow in scope.
Client keeps: governance playbook, platform, decision record, and observability evidence.
## Velo9 delivery products
Velocity™, V9OS™, and Cadence™ are Velo9 products included in the engagements that use them. They accelerate adoption without requiring a separate suite purchase.
| Product | Role in an engagement | Records produced |
| --- | --- | --- |
| Velocity™ | Live-work training, coaching, commitments, and transfer | Session artifacts, practice record, commitments, handoff path |
| V9OS™ | Client-scoped shared context for people and workflows | Approved facts, decisions, rules, sources, operating history |
| Cadence™ | Implementation and governance rails | Requirements, review gates, evaluations, cost signals, release evidence |
Included by scope. The client keeps its data, client-specific context, delivered code, artifacts, and operating record.
## Shared context
Individual chat sessions start over. Shared context gives trained people, grounded agents, and governed workflows the same approved sources, decisions, rules, and operating history.
Typical context objects:
- Source registry and provenance
- Decision log and approval history
- Company rules, definitions, and operating constraints
- Workflow state, owners, and escalation paths
- Reusable instructions, examples, evaluations, and acceptance evidence
## Tools and implementation patterns
- Models: OpenAI, Anthropic, and Google, selected by task and client constraints
- Context and integration: V9OS™, APIs, MCP connectors, retrieval, and client-specific connectors
- Delivery: Cadence™, Git and GitHub, containers, CI/CD, evaluations, and release gates
- Training and transfer: Velocity™, live-work sessions, reusable artifacts, and acceptance evidence
- Observability: Langfuse-based traces or equivalent client tooling when included in scope
- Client systems: cloud, identity, data, and business applications selected by the engagement
Tool selection follows the workflow, data boundary, identity model, cost target, and client architecture. Velo9 does not require one model provider.
## Governance, observability, and cost
- Approved context and defined permissions for the workflow in scope
- Access designed around least privilege and reviewed with the client security owner when included in scope
- Named human approval can be required for customer communication, system-of-record changes, production releases, and other consequential actions
- Model, usage, token count, latency, and estimated provider cost recorded on managed routes when providers expose the data
- Token use optimized through scoped context and model routing
- Budget limits and routing policy can be applied to managed routes
- Source provenance, approvals, evaluations, and release evidence recorded for the workflow in scope
- License, vendor, rework, and shadow-AI costs require client billing, identity, endpoint, or network inputs
## Deployment and data boundaries
- Client-tenant deployment is available when included in scope
- Client data and context are separated by engagement and deployment design
- Delivered code, client-specific context, configurations, records, and playbooks transfer according to the order form
- Identity, retention, subprocessors, incident handling, and security evidence are reviewed with the client for the selected architecture
- External actions and write access are explicitly scoped
## Handoff package
Depending on scope, the handoff can include:
- Architecture and data-flow map
- Source and decision registers
- Delivered code and configuration
- Requirements, evaluations, and release evidence
- Governance playbook and approval matrix
- Observability and cost views
- Runbook, escalation path, and named internal owner
- Client-run acceptance test that shows the operating loop can continue after handoff
## Anonymized result
| Context | Baseline | Delivery | Measured result | Transfer evidence |
| --- | --- | --- | --- | --- |
| 60-person professional-services firm, billing reconciliation | Three people each lost about one week per month | Workflow live after six sessions | Three person-weeks recovered each month | Client operator became the release approver |
## Operator accountability
Velo9 was founded by John Buccola after leading technology inside private-equity-backed companies. He completed three company exits, two from the CIO seat, and has been published by MIT on machine learning and data experimentation. Raised by two teachers, John brings a teacher's instinct to the work: meet people where they are, make complex systems practical, and leave the team more capable. He is a lifelong learner and software developer who still builds alongside clients.
## Best fit
Velo9 is strongest where:
- Expert judgment is difficult to reuse
- AI use is growing faster than shared practice and controls
- A live workflow has a sponsor, usable context, and a measurable result
- Leaders want their teams to keep the capability and operating record
- Cost, risk, approval, and ownership need to be visible
## Shift into Next™
The client owns the capability, context, system, playbooks, platform, and evidence needed to keep improving after handoff.
## Ask V9
Ask about the V9 Way, AI training, implementation, governance, shared context, Velo9 products, tools, client ownership, or how to start.
## Next step
[Ask V9](https://velo9.ai/#ask-v9) or [book an architecture workout](https://app.reclaim.ai/m/velo9/introcall).
Ask V9A guide to how Velo9 works
Ask me about the V9 Way, training, implementation, governance, our products, or what a first engagement looks like.
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