Enterprise Solutions
From workflows to autonomous outcomes.
Redesign how work gets done. Cooma helps turn human and system workflows into autonomous AI workloads—with the right models, clear boundaries and a measurable path from tokens to impact.
Design your first workloadDesigned for autonomy
Give AI an objective.
Let it carry the work.
AI’s greatest enterprise opportunity is to take responsibility for a complete piece of work: understand the request, work across systems, adapt to new information and verify the result.
That changes how work is designed. People set the intent and standards. AI carries out the workload within agreed permissions. Exceptions return to the people who own the decision.
Automation remains useful for predictable steps. Autonomy adds the ability to choose and adapt the path. We combine both where they create the most value.
The Cooma engagement
Redesign the work.
Prove it. Then scale it.
Bring one valuable workload. We work with your business and technology teams to connect the operating model, AI capabilities and economics.
- 01
Map the outcome
Trace the decisions, handoffs and systems involved. Establish today’s quality, time and cost, then define an accepted outcome.
A workload blueprint and value baseline.
- 02
Build a bounded pilot
Connect the required context and tools. Define permissions, approval points and stopping rules. Test against real, representative work.
A working pilot with clear acceptance criteria.
- 03
Put autonomy to work
Introduce the workload in stages, with monitoring, exception handling and a named operational owner. Expand authority as performance earns it.
A rollout plan and operating controls.
- 04
Improve the economics
Measure accepted outcomes and total delivery cost. Refine models, context and execution before extending the approach to more work.
An evidence-based decision to improve, scale or stop.
Delivery scope, integrations, deployment arrangements and operational responsibilities are agreed for each engagement.
The right intelligence for the work
One workload.
The right model at each step.
A document check, a complex decision and a live conversation have different needs. Evaluate quality, response time, data requirements and cost at the task level.
Extract & classify
Efficient models for structured, repeatable tasks.
Reason & act
More capable models where ambiguity and decisions demand them.
Verify & escalate
System checks, independent evaluation and human review where required.
TokenFactory provides a path to model access. GPU Cloud provides dedicated compute options. Enterprise Solutions brings the workload design and delivery plan together around your business.
Illustrative architecture. Model selection, routing, hosting and integrations are scoped to your requirements.
The token-to-impact formula
Every token is a cost.
Make the outcome count.
Define what good looks like, count the work that meets that standard, and compare it with the resources consumed. The goal is better business performance with economics you can explain.
Tokens per accepted outcome
Total tokens consumed ÷ outcomes that meet the agreed standard
Cost per accepted outcome
Total workload delivery cost ÷ outcomes that meet the agreed standard
Count failed attempts and retries in consumption. Include model, compute, tool, integration, human review and rework costs over the same period. Compare quality and turnaround with the baseline; capacity released becomes financial value only when it is put to use.
Explore the Token Factory FlywheelWhat this could look like
A customer case.
Carried through to resolution.
Illustrative engagement, not a customer result.
A service request arrives. The AI retrieves approved account information, interprets the issue, acts in connected systems within policy, and verifies that the requested change happened.
Cases outside its authority go to a person with the context already assembled. The measure is a correctly resolved case, its time to resolution and total delivery cost.
Autonomy with accountability
People set the boundaries.
Evidence earns the scale.
Build permissions, data boundaries, spend limits and stopping conditions into the workload. Keep approval for consequential actions and make exceptions visible to a named owner.
Evaluate outcomes before rollout and after changes. Record the actions taken, monitor failures and provide a way to pause or reverse supported actions. Confirm provider retention, training-use terms and data handling before introducing sensitive information.
Explore security & controlBefore we talk
Common questions.
What is an autonomous AI workload?
A workload combines a business objective, relevant context, authorised tools and a way to verify completion. An autonomous AI system can decide and adapt the steps needed to reach that objective within agreed boundaries. People retain ownership of the objective, permissions and exceptions.
Does every workflow need full autonomy?
No. Predictable steps can remain rules-based, while AI handles work that needs interpretation or adaptation. Start with bounded autonomy and increase it only when quality, reliability and business value justify it. Keep approval for consequential actions.
How does Cooma choose the right models?
We assess models against representative tasks, required quality, latency, data handling and total delivery cost. Different stages may use different models. Model access and deployment options are confirmed for the engagement; no single model is assumed to be best for every task.
How do tokens translate into business impact?
Measure token usage against accepted outcomes, then include model, infrastructure, integration, tool, human review and rework costs. Compare the resulting cost per accepted outcome and turnaround time with a measured baseline. Token volume alone does not establish value.
Can we build around our existing systems?
Yes, existing systems are the starting point for discovery. We assess APIs, data access, permissions and integration constraints before agreeing delivery scope. System compatibility, security requirements, operating responsibilities and commercial terms are confirmed per engagement.
Start with one workload
What work should
AI carry for you?
Bring the process, its bottlenecks and the outcome that matters. Let’s define a practical path to autonomy.