AI-Native Service Builds

Most AI work bolts a copilot onto a team that still does the job. The bigger prize is the job itself. Cravings builds AI-native service operations — the agent fleet, the human-in-the-loop layer, and the controls — that deliver the service rather than assist someone delivering it. You buy an outcome and an SLA, not seats and a prompt.

The thesis is simple and not ours alone: spend on services dwarfs spend on software, and a large share of those services is already outsourced — which means it is already a process with inputs, outputs, and a quality bar someone signs off on. That is exactly the shape an agent can take over. We build for the verticals where the work is high-volume, rules-heavy, and painful to staff.

Where this works

• Insurance broking & servicing — quote intake, submission packaging, renewal chasing, endorsement processing, mid-term adjustments.
• Accounting, tax & audit — bookkeeping, reconciliations, month-end close, working-paper preparation, first-pass review.
• Compliance — KYC/AML onboarding review, sanctions and PEP screening adjudication, transaction-monitoring alert triage, regulatory filing prep.
• Healthcare administration — prior authorisation, eligibility checks, claims coding and scrubbing, denials management, patient intake.

The common thread: a defined deliverable, a regulator or auditor who can ask “why did you do that,” and a back office that grows headcount linearly with volume. An AI-native service breaks that line.

What we build

• The service, not a tool. An end-to-end pipeline that ingests the work, does it, and produces the signed-off output — with humans on the exceptions, not the queue.
• An eval suite that is the quality bar. Graded against historical work your domain experts re-scored. The agent ships when it clears the bar your auditor would.
• A defensible audit trail. Every decision replayable, every source cited, every write reversible — built for the examiner’s question before it is asked.
• A human-in-the-loop layer. Confidence-gated escalation, four-eyes checks on anything money- or regulation-sensitive, and a review console your specialists actually want to use.
• The operating model around it. SLAs, throughput dashboards, drift monitoring, and the on-call rotation that keeps a service a service at 2am.

Two ways to engage

Build it, you run it. We design and ship the AI-native service operation inside your business — your accounts, your repos, your team trained to own it. This is the standard Cravings shape: built to hand back.

Build it, we run it. For a defined function, we will stand the service up and operate it against an SLA — outcome-priced, billed by volume or by result, not by the hour. You get the capacity now; you can take ownership later. This is us putting the AI-native thesis on our own invoice.

How we de-risk it

We do not flip a regulated process to autonomous overnight. A two-week readiness audit sets the honest scope. Then the agent runs in shadow against live work — producing outputs no one acts on — until it clears the eval bar on your real volumes. Only then does it take queues, one at a time, behind feature flags, with the existing process still able to take over. The rollback radius stays small the whole way.

Typical engagement

Two-week readiness audit, then a twelve- to twenty-week build to a live, SLA-backed service handling a real slice of volume. From there it scales by adding queues and verticals, not headcount. See the case studies for what this has looked like in accounting, compliance, and healthcare administration.