Solutions

Offers and solutions for AI-native operations.

You do not buy agents. You engage an operational capability.

Offers define how a client engages Alltera. Solution Plays define which operational problem Alltera solves.

Offer

Offers and solutions for AI-native operations.

Fig. 01Engagement journey

AI Opportunity Assessment

AI initiatives are scattered, with no economic cut, no operational owner, and no rule for priority.

Production Value Sprint

AI remains a demonstration. Integration, governance, KPI, and day-to-day operation for real work are missing.

Managed AI Operations

Without an operational owner, quality, cost, and autonomy drift. Production becomes a closed project.

AI Transformation Pod

Each function runs its own initiative. Skills are not reused. Operations never become a continuous layer.

Offer

AI Opportunity Assessment

When to engage: When the organization does not yet know which workflow to attack first, or needs a business case with a baseline.
Problem solved: AI initiatives are scattered, with no economic cut, no operational owner, and no rule for priority.
Scope: Mapping of real work, volumes, systems, exceptions, baseline, and selection of the First Success Candidate.
Duration: 7–10 business days.
Client participation: Access to operators, workflow owners, volume data, and the systems involved. A sponsor with authority to prioritize.
Commercial model: A scoped discovery engagement. It is the entry point to a Production Value Sprint — not an open-ended diagnostic product.
Deliverables: Current State Map
Economic Baseline
AI Opportunity Map
Prioritization Matrix
Target Workflow
Business Case
Transformation Roadmap
Next step: A Production Value Sprint on the selected workflow.

Offer

Production Value Sprint

When to engage: When a priority workflow is already clear and the organization needs a measurable result in production.
Problem solved: AI remains a demonstration. Integration, governance, KPI, and day-to-day operation for real work are missing.
Scope: Design and build of the workflow, agents, skills, integrations, Human-in-the-Loop, and passage through the Production Readiness Gate.
Duration: 15–30 days to a workflow in production and the first measurable outcome. Closure depends on the Production Readiness Gate, not a demo.
Client participation: Domain team, system access, a sample of live cases, and authority to approve rules and exceptions.
Commercial model: A delivery engagement for one workflow. Service is the entry point; the product is the outcome in production.
Deliverables: Workflow in production
Instrumented economic or operational KPI
AI/human responsibility matrix
Operating runbook
Quality and exception record
Next step: Managed AI Operations to run and improve the capability.

Offer

Managed AI Operations

When to engage: When the workflow is already in production and needs operations, monitoring, governance, and continuous improvement.
Problem solved: Without an operational owner, quality, cost, and autonomy drift. Production becomes a closed project.
Scope: Run, Monitor, Improve, Govern, and Expand for an AI-native workflow already in production.
Duration: Ongoing operational retainer.
Client participation: Workflow owner, an exceptions channel, and participation in autonomy and risk decisions.
Commercial model: Managed operations. Not a software subscription plan and not unbundled development hours.
Deliverables: Agreed operational coverage
Observability of quality, cost, and throughput
Improvement cycle for skills, rules, and exceptions
Governance of access, models, and audit
Next step: An AI Transformation Pod when the organization is ready to expand across workflows.

Offer

AI Transformation Pod

When to engage: When the organization needs to expand capacity across several workflows, functions, and units.
Problem solved: Each function runs its own initiative. Skills are not reused. Operations never become a continuous layer.
Scope: A mixed pod of AI Transformation Strategist and Forward-Deployed AI Engineer, shipping new workflows on the AI Operations OS.
Duration: Standing capacity, reviewed by expansion cycle.
Client participation: Executive sponsorship, owners per workflow, and alignment across operations, risk, and technology.
Commercial model: Dedicated operational transformation capacity. Not a generic development team.
Deliverables: Prioritized workflow backlog
Reuse of skills, tools, and integrations
Autonomy expansion under governance
Roadmap across units and functions
Next step: Further Production Value Sprints inside the pod cadence.

Solution Plays

The operational problem to solve

Offers define how a client engages Alltera. Solution Plays define which operational problem Alltera solves.

Collections Operations

Collections queues stall when prioritization, contact, and exception handling live in separate tools.

Alltera runs the case end to end — ranking, outreach, exception, and close — with Human-in-the-Loop on authority limits.

Higher collections throughput with a complete trail on every case.

Revenue Assurance

Revenue leaks between events, invoices, contracts, and manual reconciliations.

We match operational events, documents, and billing rules to detect, explain, and route leakage.

Shorter correction cycles and less unexplained revenue loss.

Operations Control

Incidents, queues, and exceptions spread without an owner, SLA, or shared context.

Intake, triage, routing, and follow-through sit in one workflow, with state visible to the people who run it.

Continuous operational control — not another disconnected dashboard.

Document Intelligence

Critical documents arrive by email, PDF, and portal, then wait in human queues.

Extraction, classification, and validation happen inside the workflow, with human review when confidence drops.

Cases move with structured context, not a standalone OCR silo.

Contract Operations

Contracts, amendments, and obligations sit outside the rhythm of day-to-day work.

Clauses, dates, and evidence connect to source systems and the approvals the business already uses.

Obligations stay visible, deadlines are tracked, and legal-operational rework falls.

AI Service Operations

Service treats every ticket as a conversation instead of an operational case.

We run service as a workflow: intent, data, system action, exception, and close.

Resolution with auditable quality — not merely a faster channel.

Start with the right workflow.

Map an opportunity Method