Measure outcomes. Not tokens.
specialised agents in a typical end-to-end workflow: plan, execute, validate, with approval gates where your people decide.
The first wave of AI adoption was staff pasting text into browser windows to write emails slightly faster. Marginal returns, fragmented data. The real gain is orchestration: networks of specialised agents that collaborate, check each other's output and run a workflow from intake to verified completion, with a person at the gates that matter.
The lifecycle.
An orchestration engine breaks a workflow into loops, managing execution from first ingestion to verified completion. Step five feeds step one; the loop compounds.
- 01
Ingest
The trigger arrives: an order, an invoice, a referral, an alert. Read, classified and matched to the right workflow.
- 02
Plan
A planning agent breaks the job into steps against your rules and the context in the Digital Brain.
- 03
Execute
Specialised agents do the steps, using the cheapest model that can do each one well.
- 04
Validate and gate
A validating agent checks the output against the rules; anything consequential is queued for a person's approval.
- 05
Record and learn
The result and the decision go on the record, and the measured outcome feeds the next plan.
Open-source where it can be. Frontier where it must.
Businesses of 10 to 500 people should not pay recurring platform fees for routine computing. The architecture keeps the choice of model open, so cost is a routing decision you control.
Open-source and local models
Deployed inside your own infrastructure for data parsing, email classification and high-volume internal document search. No per-token licence fee.
Commercial frontier models
Used only when a task demands deep analysis, cross-referencing or complex calculation. You pay for what the task consumes, when it needs it, and you see the cost.
Value created, value kept.
If a vendor frames your AI spend around tokens consumed, they are tracking an infrastructure metric. Tokens are text fragments. They do not equal profit. We measure against operational numbers: tickets resolved without a person, exceptions detected and handled automatically, cost per automated invoice against manual hours.
Two forces to keep separate. Value creation is the total leverage a workflow introduces: speed, error reduction, hours returned. Value capture is the share of that upside that stays in your bank account rather than leaking out as recurring platform fees. Decoupled orchestration on open foundations is how you keep more of what you create. You are building an asset you own, not renting a subscription.
Guardrails that apply from day one: human in control at the approval gates, open supply chain on the models underneath, on the record for every action.
Questions.
What is multi-agent orchestration?
A network of specialised AI agents that collaborate, peer-review each other’s output, and execute complex end-to-end business workflows without human bottlenecks, instead of one isolated chatbot doing one task at a time.
Can orchestration work alongside our human approval steps?
Yes. Approval gates are part of the lifecycle design. Agents complete and validate the bulk of the work, and defined checkpoints route decisions to your people.
What should we measure instead of tokens?
Task resolution rate: how many tickets fully resolved without human intervention, how many anomalies detected and bypassed automatically, and the cost per automated task versus manual staffing hours.
Why mix open-source and commercial AI models?
Routine work (data parsing, email classification, document search) runs on open-source models inside your own infrastructure at zero per-token cost. Expensive frontier models are reserved for tasks that genuinely need deep reasoning. You pay only for what the task demands.
Is this a subscription?
No. Quantrim builds you a permanent proprietary asset on a performance-based model: a free trial, then payment on proven results. You are investing in infrastructure you own, not renting a platform.
What is the difference between value creation and value capture?
Value creation is the total operational leverage AI introduces. Value capture is the share of that upside that stays in your bank account instead of leaking out as recurring platform fees. Decoupled, open-source-heavy architecture maximises what you keep.
How many agents does a typical orchestration involve?
As many as the workflow needs and no more, often three to six specialised agents per end-to-end process (planning, execution, validation), scaling with the complexity of the work.