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Solution: AI automation and integrations

Employees copy the same data between systems every day

We connect the tools you already pay for so data moves automatically, and use AI only for the parts that arrive as text or documents. People review the exceptions.

Sounds familiar?

Signs this is costing you time

Manual data entry hides in every business. An order comes in by email and is typed into the order system. A new customer is added to the CRM, then to accounting, then to the project tool. A spreadsheet is exported from one system and imported into another every Friday. Each step takes a few minutes and creates a chance for a typo.

Across a team, those minutes add up to days every month, and the errors add up to real costs: wrong invoices, missed orders and reports nobody trusts.

  • The same customer or order details are typed into two or three systems.

  • Weekly exports and imports keep systems roughly in sync.

  • Typos and missing fields cause errors that surface weeks later.

  • Skilled staff spend hours on copy-paste work.

  • Information is always a day or a week out of date somewhere.

Before and after

How it works today, and how it works after

Before

How it works today

  1. Data arrives by email, form, spreadsheet or phone.

  2. Someone reads it and types it into the first system.

  3. The same details are copied into other systems later.

  4. Mismatches are found by accident, often by customers.

  5. Nobody is sure which system has the right version.

After

How it works after

  1. Structured data flows between systems through their APIs.

  2. Emails and documents are read by AI and turned into structured records.

  3. Each record is validated before it is saved.

  4. Exceptions go to a review queue instead of silently failing.

  5. One system is the source of truth for each type of data.

What we build

What we build

System integrations

API integrations that move data between your tools the moment it changes.

AI extraction

Emails, PDFs and free text turned into structured records by a language model.

Validation rules

Required fields, formats and reference data checked before anything is saved.

Exception queue

Records that fail checks wait for a person, with the reason shown.

Workflow automation

Automated steps such as creating tasks or notifying people when data arrives.

Audit log

A record of what moved where, when and why.

In practice

What it looks like in practice

An illustrative walk-through, not a client story.

A logistics company receives shipment requests by email from dozens of customers, each in their own format. Today, two coordinators read each email and type the details into the dispatch system, then again into the invoicing tool.

After automation, each email is read by AI, which extracts pickup and delivery addresses, weights, dates and references into a standard record. Validation checks the addresses and customer account. Clean records go straight into dispatch and invoicing through their APIs. About one in ten, usually with missing or unclear details, goes to a review queue where a coordinator fixes it in under a minute. The coordinators now spend their time on customer calls and problem shipments.

Models and tools

AI models and MCP servers that usually fit

We choose models by task, data sensitivity and cost, and test them on your real examples before we commit. These are common starting points, not a fixed recipe.

How we build it

The services behind this solution

Most solutions combine two or three of our services. These are the ones this one usually needs.

API integration

Make two systems share data reliably, with retries, logging and alerts when something goes wrong.

Business process automation

Map a process that runs on email and spreadsheets and turn it into software with clear steps and owners.

AI workflows

Step-by-step automations where AI handles the reading, sorting and drafting inside a process you control.

CRM integration

Connect your CRM to forms, email, billing and support so customer records stay complete without typing.

Industries where it fits best

Honest limits

Limits and human checks

Not every data entry task should be automated. If a process happens a few times a month, the setup cost may never pay back. We start by measuring volume and error rates, then automate the flows with the clearest return.

AI extraction is very good but not perfect, especially with messy emails or poor scans. We never let uncertain extractions into your systems silently. Confidence thresholds, validation rules and a review queue keep a person in charge of anything unclear. Structured flows between systems do not need AI at all, and we do not use it where plain integration code is more reliable.

Keep exploring

FAQ

Questions about AI automation and integrations

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