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Service · AI Automation Agency
Taking new engagements · Reply < 1 business day

AI automation: done-for-you systems that run your operations.

Done-for-you AI automation: systems that read your email, your documents, your tickets, and your data — and do the work your team is too busy to do.

Practice
AI automation
Pattern
Agentic + deterministic
Stack
Claude · OpenAI · custom
Bias
Auditable by default
§ 01 — What’s included

Capabilities, stated.

What you get when this service is part of a Yab engagement. Scoped per project.

01
Document automation

Intake forms, invoices, contracts, clinical notes — extracted, normalized, validated, and routed automatically. With confidence scores and human review where the stakes warrant it.

02
Email & ticket triage

Inbound mail and tickets classified, routed, and (where appropriate) drafted automatically. Your team handles the exceptions, not the volume.

03
Internal copilots

RAG-powered assistants over your policies, runbooks, product docs, or case history. Your team gets the right answer without having to know where it lives.

04
Workflow agents

Multi-step agentic pipelines — research, draft, review, execute — built with deterministic guardrails so they're predictable, not chaotic.

05
Data extraction & enrichment

Unstructured data turned into structured records and pushed into your CRM, ERP, or data warehouse. The integration is part of the deliverable.

06
Observability & governance

Every prompt, every response, every decision logged and reviewable. Drift detection, escalation paths, and the audit trail your compliance team will ask for.

07
Human-in-the-loop

Designed so humans intervene only on the exceptions and edge cases. Most volume processed without touch; high-stakes outputs reviewed before they go.

08
Run it for you

We can build and hand off, or build and run. Many clients want the agency to operate the automation in production while their team focuses on the work.

§ 02 — Standards

The bar we hold ourselves to.

Operating numbers and posture you can quote back to us. These are commitments, not aspirations.

Prompts logged
100%
Audit trail
Always
Pilot timeline
4–8 weeks
Run model
DIY or managed
§ 03 — Relevant work

Where we’ve done this.

Real engagements where this service was load-bearing. Click through for full case studies.

§ 04 — Approach

Four phases.
Fixed sequence.

The shape of a typical engagement. Phases overlap on larger projects, but the sequence is the same.

01
Workflow audit

We sit with the team doing the work and map the steps. Where can a model help? Where would it be reckless? We end with 1–3 candidate automations and an honest ROI estimate.

1–2 weeks
02
Prototype

A working prototype against real data within two weeks. Stakeholders see it run on their own examples before any commitment to production.

2 weeks
03
Productionize

Integration with your systems, observability and governance wired in, error handling and escalation paths defined. Deployed with monitoring and rollback in place.

4–8 weeks
04
Run & improve

We monitor the automation in production, tune prompts and models as patterns change, and report monthly on volume, accuracy, and cost.

Ongoing
§ 05 — Industries

Where this work
fits best.

Sectors where we’ve done this service often enough to know the patterns — and the patterns that don’t work.

§ 06 — FAQ

Questions,
asked in advance.

The things people ask before they reach out. If yours isn’t here, send it over — we’ll add it.

An AI automation agency designs and builds AI-powered systems that take recurring work off a team's plate — document processing, email triage, workflow agents, internal copilots. We sit between your operations and the AI providers, making sure the system actually delivers and stays auditable.

The right toolchain depends on the workflow. Anthropic Claude and OpenAI are strong general-purpose engines; LangChain and LlamaIndex are common for orchestration; vertical products (like Cognosys or Lindy) are useful when the workflow is narrow. We benchmark for your specific use case rather than committing in advance.

RPA and traditional business automation move structured data between systems on fixed rules. AI automation handles unstructured input — natural language, documents, images, conversation — and makes judgment calls. The two often complement each other: AI handles the messy part, RPA handles the structured part.

Workflows with high volume, repetitive judgment calls, and unstructured input are usually the best fit: customer support triage, document review, claims processing, intake forms, contract review, lead qualification, and internal Q&A. Workflows that are already well-automated by deterministic systems usually shouldn't be touched.

Pilots start around $25k and most productionizations land between $50k and $150k depending on the integrations and the volume. Managed-run engagements (where we operate the automation) are billed monthly on top of build. Quoted in writing.

Yes — every automation does, AI or not. The difference is that we design for it: confidence scoring, human-in-the-loop for high-stakes decisions, escalation paths, and observability. The goal is not zero errors; it's predictable, auditable, and correctable behavior.

§ 07 — Start

Hand the repetitive work to a system.

One reply within a business day. No sales pipeline. We’ll either propose a path or point you somewhere better.