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B2B SaaS company (illustrative, anonymized pattern)

A tier-1 agent that resolves account and billing issues end-to-end

This pattern fits SaaS teams where tier-1 tickets repeat: invoices, seat counts, plan mismatches, and refund policy questions. The agent reads account state with least privilege, proposes actions within policy, and escalates when confidence drops or a human must approve money movement.

At a glance
Engagement shape
B2B SaaS company (illustrative, anonymized pattern)
Window
Rapid POC: 14 days · Production: phased
Disclaimer
Anonymized composite. How to read these
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Directional outcomes

What this pattern usually moves.

Ranges typical to the pattern, not audited figures for a named account.

Directional pattern: containment on in-scope tier-1 issues often rises materially while escalations remain high quality because specialists receive structured summaries.

Directional pattern: average handle time for eligible tickets frequently moves from minutes to seconds on the straight-through path.

Directional pattern: policy violations and tool errors trend down week over week as eval gates tighten.

Illustrative engagements based on the patterns we deliver. Anonymized and composited. Real client references available under NDA.

The pattern

Context, constraints, and approach.

The shape of the problem and how we ran it. Written for technical evaluators and business owners in one pass.

Context

Support leaders wanted faster first response without expanding headcount linearly with ARR. A generic chatbot was unacceptable because wrong billing actions create churn and audit risk. The program needed explicit policies, traceability, and a staged rollout.

Approach

We implemented a tool matrix: read-only tools by default, write tools behind budgets and approvals, and destructive actions blocked or routed. Retrieval grounded answers in internal policy docs operators trust. Evaluations used redacted transcripts and a golden question set from real tickets.

Related patterns

Same disclaimer applies. Anonymised composites with directional outcomes.

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Unstructured Data Pipelines

From adjuster email to structured claim intake at scale

This pattern is for carriers where adjusters and third parties send facts as email threads and attachments, not as clean ACORD feeds. The goal is reliable structured records for routing, reserving, and downstream fraud checks, without asking adjusters to retype what they already wrote.

Read the pattern

AI Workflow Automation

Freight booking automation across six carrier portals

This pattern fits teams where capacity checks and booking confirmations require logging into multiple carrier systems that were never meant to integrate cleanly. The goal is fewer clicks for operators, fewer missed slots, and a replayable record when a carrier UI changes.

Read the pattern
FAQ

Questions buyers actually ask.

Honest, specific answers about scope, accuracy, security, and what production looks like. If something isn't covered here,ask us directly.

Will customers know it is an agent?

Disclosure and UX are part of the design. Most programs label the assistant and provide a human path without dark patterns.

How do you prevent over-refunds?

Hard caps, dual approval for large credits, and dry-run modes until metrics prove containment quality.

What tools does it use?

Billing APIs, subscription admin APIs, ticketing updates, and internal knowledge retrieval. Never arbitrary shell access.

What is the proof path?

Shadow mode, then partial traffic with kill switches, then expansion as error budgets allow.

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A short note is enough. We will reply within one business day with a Rapid POC scoping call.