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AI implementation

AI implementation built around how your company actually works.

Off-the-shelf AI tools solve generic problems. Your bottlenecks are specific: your systems, your exceptions, your approval rules. We build for those.

Where AI helps first

Manual taskWhat it looks likeWhat we build
Re-keying between systemsOrders typed from email into ERPIntegration with validation and an exception queue
Reading documentsInvoices, delivery notes, intake formsExtraction with confidence checks and human review
Answering the same internal questionsPolicy and how-to questions to HR or ITPrivate assistant over your documents, permission aware
Chasing approvalsRequests stuck in inboxesRouted approvals with owners and escalation
Reconciling two sourcesSpreadsheet built before every meetingOne system of record per field, then automated checks
Preparing reportsWeekly status assembled by handScheduled report drafts for a person to review

What solutions can connect to

ERP, CRM, HR and payroll, ticketing and service management, email and office suites, document stores, databases and file shares.

ERPCRMHR and payrollTicketingEmail and office suitesDocument storesDatabases and file shares

Our build principles

  1. One owner per field. Agree the system of record before connecting systems or adding AI.
  2. Design the exception path before the happy path.
  3. Human approval for anything that changes a record or reaches a customer.
  4. Measure before and after, with your numbers.
  5. Hand over with documentation your team can run.

Engagement models

Assessment

Ranked plan and savings estimate for the workflows agreed at scoping.

Pilot

One workflow built and run beside your team.

Rollout

Production deployment, training, handover.

Run and improve

Monitoring, rule reviews, changes, or a dedicated engineer.

Pricing is scoped to your project. The aim of moving manual work to AI is a lower operating cost, and the assessment estimates that saving for your own workflows before you commit to a build.

Questions

What do you need from us to start?

One workflow owner, sample data or screenshots, and time with the people who do the work today. An NDA is signed before that first meeting.

Which AI models do you use?

We choose per use case: commercial models through your own cloud account, or open models hosted in your environment when data must stay on your servers. We explain the trade-offs in cost, accuracy and data handling.

What if AI is not the right answer?

Then we say so. Many bottlenecks are fixed by a clear rule, an integration or a better form. We recommend the simplest thing that works.

What does it cost?

Pricing is scoped to your project. The aim of moving manual work to AI is a lower operating cost, and the assessment estimates that saving for your own workflows before you commit to a build.