01
Increase capacity
Move repetitive coordination and execution away from people without removing human judgment.
Human authority · AI execution
Put AI to work without surrendering human authority
6Control helps leaders turn artificial intelligence into measurable business advantage—better decisions, stronger operations, and scalable execution—governed end to end, with a named human accountable for every consequential call.
Two founders. Forty-nine years inside a global enterprise between them.
Business-ledTechnology follows the decision.
Human-accountableAuthority is named and retained.
Governed operationsControl lives inside operations.
Built to scaleExpansion follows demonstrated value.
Vendor agnosticWe build on the platform you already run.
Business advantage
More AI activity is not the objective
Leaders need a defensible answer about where AI belongs, what it should improve, who remains accountable, and how value will be measured. The goal is not autonomous business. It is a more capable business that remains human-accountable.
02
Improve decisions
Turn fragmented information into structured, sourced, and checkable intelligence.
03
Reduce operating drag
Redesign workflows around outcomes instead of layering AI onto broken processes.
04
Protect trust
Preserve authority, traceability, escalation, and recourse as systems become more capable.
A consequential AI decision deserves executive clarity before executive commitment.
A decision, not another deck
Leave with a position leadership can defend
ProceedThe opportunity, authority model, evidence requirements, and next decision are sufficiently clear.
SequenceThe opportunity is valid, but readiness, data, workflow, or governance dependencies must come first.
RedesignThe current approach introduces avoidable operating drag, ambiguity, or risk and requires a different architecture.
DeferThe organization is not yet positioned to act responsibly or capture durable value.
StopThe proposed use case does not justify the cost, exposure, or organizational disruption.
What the engagement includes
- Review of leadership pre-work and operating context
- Executive discovery session with both founders
- Assessment against the Executive Diagnostic Framework™
- Executive Readiness Index™ — where the organization stands today
- Executive Findings Report™ — the decision, the reasoning, and the evidence behind it
- Executive debrief with leadership
Implementation is not included. Where the decision is to proceed, the Strategic Blueprint™ is a separate engagement.
The engagement is shaped around the executive question, operating context, and level of consequence. Commercial terms follow qualification and scope; they never lead the conversation.
A limited number of inaugural engagements carry preferred commercial terms.
In exchange, we ask for permission to develop an approved, documented case study — what was decided, on what evidence, and what changed. You approve what is published before it is published, and nothing goes out that names your people or your data without your written approval.
This is a deliberate, limited trade. If you would rather not participate in a case study, say so and we will quote the engagement on standard commercial terms.
Our operating model
The Harmonic AI Model
HAM is the model everything else serves. It holds three roles in balance: the human remains accountable, analysis turns information into governed intelligence, and the machine executes at scale inside established limits.
H
Human
A named person holds authority and remains accountable for the outcome. Authority is assigned, not assumed.
A
Analysis
Information becomes governed intelligence—structured, sourced, and checkable before it reaches a decision.
M
Machine
Execution runs at scale, under governance, inside the permissions and boundaries the human set.
Reject → Improve → Accept · Work passes when an accountable human accepts it.
6Control
Governance is part of how the system operates
6Control makes HAM enforceable in operation. It embeds authority, permissions, traceability, escalation, evidence, and challengeability into governed actions rather than adding them after deployment.
Named human authority
Consequential actions have an accountable human owner with defined authority and escalation paths.
Decision evidence
The system preserves what was proposed, what changed, what was accepted, and who accepted it.
Challengeability
A person affected by an AI-mediated result has a checkable route to contest it and seek recourse.
Consequence Admission Record
A named, signed, immutable artifact: what was decided, by whom, on what evidence, and what was declined. Any consequential call can carry one — not only a machine's. We keep our own the same way.
HUMAN AUTHORITYTRACEABILITYPERMISSIONSESCALATIONEVIDENCERECOURSE
How we work
Diagnose. Design. Govern. Scale
Each stage protects the quality of the next. Scaling begins only after leadership can see the value, the controls, and the evidence in the real business.
01
Diagnose
Find the business constraint, decision bottleneck, data reality, and whether leadership is ready to act.
02
Design
Map the future workflow: agent roles, human approvals, integrations, controls, and measures of success.
03
Govern
Embed authority, permissions, traceability, escalation, challengeability, and evidence into operation.
04
Scale
Expand only after value and trust are demonstrated. Scaling an unproven system multiplies the problem.
Evidence discipline
Demonstration is not validation
Artifacts can show how a system works. They do not establish independent client outcomes. We keep the distinction visible and label claims according to the evidence that supports them.
What we can demonstrate
Decision records, governance reports, worked diagnostic outputs, workflow maps, human approval gates, and sample CAR artifacts.
What requires independent proof
Client outcomes, references, production results, and third-party assurance are represented only after the evidence exists.
Self-application
We are our own first customer. The engagements, the research, and this site are produced under the same governance we would put into your business: work prepared by an orchestrated AI team, every consequential decision named to a human, every claim traceable to evidence.
At any given time we run 30 to 100 orchestrated agent roles, scaled to the workload. The count refers to specialized roles active across our operating environment, not 30 to 100 humans or autonomous decision-makers. They prepare and coordinate. They do not decide.
Canon governs what we say; evidence governs what we claim has been proven.
How we are built
Two accountable people. An operating model that carries the work
We run our own firm on the model we sell. An orchestrated AI team — 30 to 100 specialized agent roles at any given time, scaled to the workload — prepares, coordinates, and documents the work; the two of us hold authority over every consequential call. You reach the principals directly — without a pyramid of analysts, and without the capacity ceiling that usually comes with that.
Lisa S. Castillo
Chief Executive Officer · Co-Founder
Retired from AT&T after twenty-five years, across supplier development, executive programs, events, marketing, and technology transformation — work centered on why change does or does not take hold inside large organizations. MBA in International Business, Thunderbird.
LinkedIn →
James Ketner, PhD
Chief Technology Officer · Co-Founder
Twenty-four years at AT&T in cloud and standards-led practice, now focused on secure AI systems, governed execution, and enterprise resilience. PhD in cyberterrorism, Concordia University.
LinkedIn →See your own numbers
What would this be worth in your operation?
Adjust these to match your business. Every figure below is labelled with where it came from — what you entered, what we calculated, and what is an assumption. Nothing here is a price.
Start from
$/hr
Your inputhrs/wk
Your input%
Your input$
Your inputhrs
Your input
Assumption
Coordination hours returned per year
—
Hours per week × share prepared × working weeks. Calculated
Cost of that time today
—
Hours returned × loaded hourly cost. Calculated
Additional capacity, in client-equivalents
—
Hours returned ÷ hours a client consumes. Calculated
Revenue capacity that time could carry
—
Client-equivalents × revenue per client. This is capacity, not
forecast revenue — it assumes demand exists to fill it. Assumption
What this deliberately does not show you: what 6Control costs.
That number is set against your own operation, in conversation, and it never leads. This model exists so you can decide whether the opportunity is worth a conversation at all — using your numbers, not ours.
The same mechanism, different work
The industry changes. The governance does not
Governance sits below the workflow. What differs between one operation and another is which work feeds it — not who holds authority, not what gets recorded, not what can be challenged afterwards. Three unrelated operations, one structure.
Clinical administration
A referral that expires Friday
PreparedAvailability checked, notes assembled, insurer
requirements confirmed, two options costed.
Decided by a personWhether to accept the referral, and on
which terms. Never the machine.
On the recordWho decided, when, what they were shown, and the
option they declined.
Field operations
A shipment held at the border
PreparedCarrier contacted, paperwork gap identified, two
routes priced against the delay.
Decided by a personWhich cost to absorb, and what the customer
is told. Never the machine.
On the recordWho decided, when, what they were shown, and the
option they declined.
Production & quality
A batch outside tolerance
PreparedReadings pulled, affected lots traced, rework and
scrap costs compared.
Decided by a personWhether it ships, is reworked, or is
scrapped. Never the machine.
On the recordWho decided, when, what they were shown, and the
option they declined.
Illustrative. Not client work — client outcomes are represented only once the evidence exists.
Scope
What this governs, and what it does not
6Control governs the decision — who decided, on what evidence, with what authority, all of it on the record. Three things it is not, stated plainly, because assuming otherwise is expensive.
Not security infrastructure
Kill-switches, fleet-wide drift detection, and discovery of unauthorised AI inside your systems are a different layer, built with different tooling. Necessary, and not what we do — we will point you at people who do it well.
Not agent qualification
Platforms increasingly authenticate, authorise and capability-check an agent before it runs, and that is worth having. It answers whether an agent is fit to work. It does not answer who was accountable for the decision it made on Tuesday.
Not tied to a vendor
Agents are governed regardless of origin — ours, a marketplace, or already running here. We govern what an agent may touch, not its reasoning. A regulator asks who authorised the action; that is what we answer.
6Control governs third-party consumption but does not own, resell, mark up, or derive client-specific economic benefit from your selection or consumption of third-party cloud, AI, model, integration, infrastructure, or related services.
The problem accumulates quietly
Agents are increasingly bought one at a time, from different providers, for one job each — an inbox, a ticket queue, a compliance chase. Each arrives with its own permissions and its own idea of what it may do. Individually they are cheap, useful, and easy to approve.
What no single one of them produces is a record of who authorised what, across all of them, that you could put in front of a regulator. Qualifying an agent before deployment and governing the decisions it makes afterwards are different problems. If you are running consequential AI you need both — and we will tell you plainly which one we are.
That record is the work.
Security & data
What we hold, where it lives, and who else touches it
The threat landscape changes continuously, so we do not claim a frozen posture. We claim a governed one: every change to our own agent fleet is authorised by a named person and recorded. Below is what an enterprise review needs, stated plainly.
Client data we hold
Engagements are designed so client records are anonymised before they enter our operation and so 6Control does not retain personally identifiable information unless an engagement explicitly requires it, the client authorises it, and the handling is governed in the agreed scope. What normally persists after an engagement is the decision record — what was decided, by whom, on what evidence — not the personal data the decision concerned.
Where it is stored
On your foundation, not ours. We build on the major global platform
providers and we do not dictate which one; storage location and region are inherited from
the platform you already run. Where jurisdictional restrictions apply, the approved provider,
routing, storage, and failover configuration is bounded to those requirements before work begins.
We are vendor and model agnostic: we do not require you to adopt a platform, a cloud, or a particular model provider to work with us. Our own operation currently runs on Google Cloud and Anthropic, and is not tied to either.
Retention & deletion
Retention is set by the laws and regulations your operation is already subject to. We inherit that obligation rather than imposing a different one — a clinical record and a shipping manifest are not kept for the same length of time, and it is not our place to decide which rule applies to you. Anonymised decision records are retained as our evidence base. Deletion is requested in writing to contact@ai-orchestrate.com and actioned within 30 days.
Sub-processors
Model providers are sub-processors and we name them. Where we operate on
your platform, your existing sub-processor agreements govern; where we run our own fleet, the
providers below apply.
Because we are model agnostic, the providers on your engagement are not fixed by us: they are the ones you already run, or ones we agree with you before work begins, and they are named in writing at that point. No provider is permitted to train on client inputs. Our own operation currently uses Anthropic for inference and Google Cloud for hosting.
Routing & failover
You always know who is handling your work. Where we route across more than one provider for resilience, every provider in that pool is named — including ones reached only on failover. A switch between them is approved by a person and recorded, so the answer to “who handled this?” is never a shrug.
Jurisdiction
Where your obligations bind work to a jurisdiction, the approved provider pool, routing, storage, and failover configuration are bounded to match before work begins. Resilience does not silently relocate regulated work; any approved cross-border change is a governed decision made by a named human and recorded.
Access control
Two people can reach client work, and both are named on this page. Agents
run with scoped credentials limited to the work in front of them. Where we operate inside
your platform, your access controls and logging apply — we do not ask you to weaken
them for us.
Multi-factor authentication is required on every 6Control-controlled account that can reach client work. Where we operate inside your platform, your access controls and logging remain authoritative; relevant access records are made available through the underlying platform or engagement process as agreed.
Agent deployment governance
Every agent that enters our operation is authorised by a named human
before it runs, is scoped to the minimum it needs, and is recorded in our Consequence
Admission Record with the reasoning and the date. New agents are built and deployed
continuously; none of them deploy themselves. Review cadence and rollback are set against
the platform an engagement runs on and agreed with you before work begins.
Our own fleet is reviewed weekly.
Incident response
An incident is not only a data breach. It includes an agent taking a
materially wrong action. Both are treated the same way: the agent is withdrawn, the decision
record is examined, and you are told.
Affected clients are notified in writing according to the applicable law, contract, and incident terms agreed for the engagement; where no shorter obligation applies, our operating target is notification within 72 hours of confirmation.
Independent attestation
We hold no third-party security attestation today and we will not imply
otherwise. Until we do, this page and our evidence record are what we offer for review, and
we will answer a security questionnaire directly rather than point you at a certificate.
We use ISO/IEC 42001, the AI management-system standard, as a reference framework for our governance practice. We hold no ISO/IEC 42001 certification and no SOC 2 Type II attestation today, and we do not represent otherwise. Use of a standard as a reference is not certification against it.
Why this page exists in this form
A point-in-time certificate describes a defined assurance period. It is valuable, but it does not replace ongoing governance. Until independent assurance is in place, the responsible substitute is not silence — it is disclosure plus a governed change process. The security agents we build are part of how we run our own operation; governing our own fleet is not the same as selling you security infrastructure, and we keep that line explicit.
Begin with the executive decision
Do not start with an AI product. Start with the decision
The Executive Diagnostic identifies where AI can create meaningful advantage, where it introduces unacceptable risk, and what leadership should do next.