From current work to AI-native operations.
Alltera starts with the workflow, the baseline, and the desired outcome. Only then do agents, integrations, and automation enter the picture.
- 01
Discover
Identify the workflow with the strongest combination of impact, feasibility, and expansion potential.
- 02
Build
Design the future workflow and the infrastructure that will sustain it.
- 03
Production
Run real cases, validate quality, and produce the first measurable operational outcome.
- 04
Operate
Run, Monitor, Improve, Govern, and Expand.
- 05
Scale
Expand what already works without starting over in every function.
Identify the workflow with the strongest combination of impact, feasibility, and expansion potential.
What we cover
- Operational interviews with the people who run the work and own the result
- Current-state process mapping of how the work actually happens
- Volume, systems, handoffs, exceptions, rules, and decision rights
- Operational and economic baseline
- Opportunity mapping
- First Success Candidate selection
Deliverables
- Current State Map
- Economic Baseline
- AI Opportunity Map
- Prioritization Matrix
- Target Workflow
- AI/Human Responsibility Matrix
- Business Case
- Transformation Roadmap
Duration: 7–10 business days.
Design the future workflow and the infrastructure that will sustain it.
What we cover
- Trigger → Intake → Context → Decision → Action → Exception → Result
- Agents, reusable skills, policies, and integrations
- Quality evaluations and Human-in-the-Loop design
- Roles for the Forward-Deployed AI Engineer and AI Transformation Strategist in the operating design
Deliverables
- Target operating workflow
- Agent, skill, and tool design
- Integration map
- Autonomy and approval matrix
- Production Readiness plan
Duration: Set by the scope of the selected workflow.
Run real cases, validate quality, and produce the first measurable operational outcome.
What we cover
- Live cases — not isolated demonstrations
- Instrumentation of an economic or operational KPI
- In-operation adjustments to rules, skills, and exceptions
Deliverables
- Workflow in production
- Baseline versus observed result
- Quality, exception, and governance record
Duration: The sprint closes when the Production Readiness Gate is met.
Production Readiness Gate
Functional
Does it work end to end?
Economic
Does it produce measurable impact against the baseline?
Quality
Is output quality acceptable?
Governance
Are autonomy and decision rights defined?
Operational
Can it run reliably every day?
Run, Monitor, Improve, Govern, and Expand.
What we cover
- Continuous execution of the workflow in production
- Observation of quality, cost, throughput, and autonomy
- Correction of drift and improvement of skills
- Governance of access, models, and exceptions
Deliverables
- Managed operation of the workflow
- Operational metrics view
- Improvement cycle with a prioritized backlog
Duration: Ongoing for as long as the capability remains in production.
Operating metrics
These are the measures we track once a capability is in production. Panel figures shown in commercial conversations are illustrative until a client baseline exists.
- Business Outcome
- Throughput
- AI Autonomy
- Human Minutes per Outcome
- Quality / Resolution
- AI Cost per Outcome
- Time to First Production
- Expansion / NRR
- Reuse Rate
Expand what already works without starting over in every function.
What we cover
- New workflows built on reusable skills and integrations
- Progressive autonomy where risk allows
- Additional connected systems and business units
Deliverables
- Expansion map
- Catalog of reusable skills and capabilities
- Autonomy plan by workflow
Duration: In cadence with the AI Transformation Pod.
Start with the right workflow.
The Assessment sets the cut. The Sprint puts the work into production.