Discovery
A structured conversation to understand the process, volumes, current team structure, and pain points — before any proposal is written.
- Process & volume review
- Current team & tooling
- Pain point identification
- Go / no-go decision
A structured transition designed to remove risk from handing off a live process. Every phase is documented, every output is signed off, and AI starts reducing cost and fast-tracking performance from day one.
Every engagement follows the same structured path — discovery, design, transition, and steady-state operations. The depth changes with scope; the discipline doesn't.
A structured conversation to understand the process, volumes, current team structure, and pain points — before any proposal is written.
We review the actual process — screens, systems, edge cases, exception handling and historical performance data — to build an accurate map.
We define exactly what's in and out of scope — SLAs, escalation rules, quality thresholds, and reporting cadence — all documented and signed.
Staffing model, shift structure, supervision ratios, QA plan and reporting format — designed against your actual workflow.
We review your tools, integrations and data flows — and define exactly where AI intervenes, what it screens, and what humans decide.
For regulated processes, we confirm access controls, data handling, audit trails and any required agreements before data is touched.
A documented migration plan — what moves when, who owns what, and what triggers each stage's completion. You see it before we execute.
Dedicated agents are recruited and assigned to your account — never pulled from a shared pool. Background checks and NDAs come first.
Process, systems and AI workflow training. Agents pass a certification check before touching live work.
We run alongside your existing process — same inputs, same outputs — until quality and throughput match or exceed your baseline.
Full handoff with QA running on every output from day one. Weekly reporting begins immediately, and supervisors are on-point for escalations.
Weekly reporting, monthly reviews, and AI-driven insight on quality, throughput and drift — with proposed improvements backed by data.
Most outsourcing transitions that go wrong, go wrong for the same reasons: scope wasn't defined, quality wasn't measured, and nobody owned the transition itself. We structure the engagement so every one of those risks has a named owner and a documented checkpoint.
You get a partner who treats the transition as seriously as steady-state operations, because that's what determines whether the engagement ever gets to steady-state at all.
Scope, SLAs and quality thresholds are documented and signed before any work moves.
AI workflow boundaries are designed in step 5 — before go-live, not after problems appear.
Parallel run produces QA-verified numbers. Go-live happens when the numbers say it's ready.
Engagements run month-to-month. If the transition doesn't produce results, you're not trapped.
Nothing in our process is implied. Each step produces a document, a plan or a measurable result — and you see it before the next step begins.
| Phase | Steps | Documented Outputs | Your Sign-off |
|---|---|---|---|
| Assess & Design | 01–06 · Discovery, assessment, scope, design, AI, compliance | Process map, signed scope, SLAs, staffing plan, AI workflow design, compliance plan | Scope document sign-off before transition begins |
| Transition & Launch | 07–11 · Transition plan, team, training, parallel run, go-live | Migration plan, team roster, training records, parallel-run QA results, go-live checklist | Ready-for-handoff gate before full go-live |
| Operate & Improve | 12 · Continuous improvement | Weekly KPI reports, monthly reviews, QA sampling, improvement proposals | Monthly review and expansion planning |
Every engagement inherits the same baseline security posture, then adds process-specific controls on top.
Encryption in transit and at rest, role-based access, and least-privilege workflows across every account.
NDA-bound teams, restricted environments, and auditable access logs for regulated processes.
AI output is reviewed by trained specialists before it reaches your systems — no blind automation.
Process-specific SLAs, tracking, and audit-ready reporting from day one.
Most single-function engagements go from kickoff to go-live in two to four weeks. Larger or multi-process engagements take longer — the transition plan documents the timeline before execution starts.
That's exactly what the process assessment and solution design steps are for. Custom processes are scoped in writing before any work begins — we don't pretend a template fits every case.
QA runs continuously, and every engagement has named supervisors and escalation paths. If performance dips, it's flagged through QA and reporting — before it becomes a trend.
No. Engagements run month-to-month after go-live. We'd rather keep your business because it's working than lock you in.
AI workflow configuration is designed in step 5 and configured during transition. AI screens and flags inputs; humans make final decisions. It goes live with the same QA discipline as the rest of the process.
Yes — that's the recommended approach. Most engagements start with one high-volume, rule-driven function, then expand once quality, security, and reporting are proven.
A 30-minute conversation is usually enough to scope the first process worth outsourcing.