Work that stops being manual

AI connected to the tools you already pay for.

You do not need to replace your stack to get value out of AI. RAWR wires Claude into the website, CRM and spreadsheets your team already uses, and automates the repetitive work in between — qualification, triage, drafting, data entry.

The short version

What is AI integration and automation?

AI integration connects a language model such as Claude to the systems a business already runs — its website, CRM, inbox, documents and databases — so routine work happens automatically. Common examples are qualifying and routing leads, drafting replies and content, extracting data from documents, and syncing records between tools.

Most small businesses do not have an AI problem. They have a dozen small transfer points where a human copies something from one place to another: an enquiry into a CRM, an invoice into a spreadsheet, a voicemail into a follow-up list, a product update into four channels. Each one is minor. Together they are a part-time job nobody was hired for.

Integration work targets exactly those points. The model reads the thing that arrived, decides what it is, writes the structured version, and puts it where it belongs — with a human approving anything consequential. The tools stay the same, the hours do not.

The engineering that matters here is not the prompt. It is the plumbing: authenticating to each system, handling the record that already exists, retrying what fails, and logging every action so you can see what happened and why. That is the difference between an automation that runs for two years and a demo that breaks in week three.

What we automate

What gets connected and automated

01

Lead qualification and routing

Every enquiry read, summarized, scored against your criteria and routed to the right person with context attached — instead of a form notification nobody opens until Tuesday.

02

Support and FAQ answers

An assistant grounded in your real policies, pricing and documentation that answers common questions on the site or by email, and escalates the rest with the thread attached.

03

Content drafting

Product updates, service pages, social posts and newsletters drafted in your voice from source material you already have, with a human approving before anything publishes.

04

Document and data extraction

Invoices, applications, resumes, contracts and PDFs turned into structured records in your system, with the fields you actually use and confidence flags on the ones to check.

05

CRM and system sync

Records kept consistent between your website, CRM, accounting and email tools, so nobody is pasting the same customer into three screens.

06

Reporting and summaries

Weekly digests that read the data and tell you what changed, instead of a dashboard you have to interpret yourself.

07

Internal search

Ask a question and get an answer out of your own documents, contracts, notes and past projects, with the source cited so it can be verified.

08

Guardrails and logging

Approval steps on anything customer-facing, spend limits, retries, and a log of every automated action — so the system is auditable rather than mysterious.

2–4 wks
To the first live automation
↓ 60%
Support time, typical build
Month to month
Support, no contract
How it compares

A custom integration vs. an off-the-shelf AI add-on

Off-the-shelf add-onCustom integration from RAWR
KnowledgeGeneric, or a pasted FAQ listYour live content and records
FitWhatever the vendor builtYour actual process
Systems coveredOne tool at a timeAcross the stack, end to end
Approval stepsRarely configurableWherever you want one
Cost shapePer seat, per month, foreverFixed build, metered usage after
DataIn the vendor's accountIn systems you control
When it breaksSupport queueYou have the code and the logs
How it works

How an automation project runs

  1. 01

    Process audit

    We follow the work for a week on paper: what arrives, who touches it, how long it takes and where it stalls. Free, and the map is yours either way.

  2. 02

    Pick the first target

    One workflow with a measurable cost and a clear owner. Starting narrow is what makes the second and third automation easy to justify.

  3. 03

    Build and ground

    Connections, prompts, retrieval and guardrails built against your real data, not a sanitized sample.

  4. 04

    Shadow run

    The automation runs alongside the human for a week and the outputs are compared. Nothing goes live on faith.

  5. 05

    Go live with approvals

    Launch with a human approving anything customer-facing, then loosen the approvals once the accuracy is proven.

  6. 06

    Expand

    With one workflow running and measured, the next ones are quick. Most clients add two or three over the following months.

Who it's for

Who this is for

This is for teams of two to fifty where the same handful of tasks eat the week and hiring is not the answer — enquiry triage, quoting, data entry, reporting, follow-up.

It also fits businesses who bought an AI feature in an existing tool, found it generic, and want something that actually knows their pricing, policies and history.

If what you need is a standalone tool rather than a connection between existing ones, that is AI app development, and the free audit will point you there instead.

Questions

AI Integration & Automation FAQs

AI automation uses a language model to handle routine work that currently moves through a person — reading an enquiry and routing it, drafting a reply, pulling fields out of a document, or keeping two systems in sync. The tools you already use stay in place; the manual steps between them do not.

Anything with an API or a supported integration: WordPress, Sanity, HubSpot, Salesforce, Pipedrive, Google Workspace, Microsoft 365, Slack, Stripe, QuickBooks, Airtable, Notion and most databases. Where no API exists, scheduled imports or email parsing usually cover it.

A single workflow typically goes from audit to live in 2–4 weeks, including a shadow-run week where the automation and the human work in parallel and the outputs are compared.

Anything customer-facing goes through an approval step until accuracy is proven, every action is logged, and the system flags low-confidence results instead of guessing silently. The shadow run exists to measure error rates before anything is trusted.

No. Integrations run on Anthropic's commercial Claude API, which does not use your prompts or outputs to train models by default. Data stays in the systems you already control.

A fixed build price, then metered API usage, which for most small-business workflows is a few dollars to a few tens of dollars a month. There are no per-seat fees and support is month to month.

Stack the advantage

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What is eating your team's week?

The free audit maps where the manual work actually is, what it costs you in hours, and which piece is worth automating first. No pitch deck, no retainer trap.

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