AI development and integration with the controls a business needs.

Invoices and forms are read, requests are sorted, and replies are drafted inside the systems your team already uses. A person reviews anything uncertain, exceptions go to a queue with an owner, and accuracy is measured before rollout.

Discuss an AI use case How the work runs

Who hires us for this

Operations and technology leads with a repeated task that a person currently does by reading, sorting, or writing: invoices, applications, service requests, contracts, support questions. It is also for leaders who have seen demos and want to know what would actually hold up in their operation.

Why now

A team spends its day reading documents and re-typing what they say.
Requests arrive in free text and someone routes them by hand.
Staff answer the same questions from the same documents every day.

What this covers

What AI development and integration covers.

Document processing

Extracting fields from invoices, forms, and contracts into the system of record, with review of anything uncertain.

Classification and routing

Requests, emails, and tickets sorted and sent to the right queue, with the reasons visible.

Drafting and summarizing

First drafts of responses, summaries of long threads, and notes from calls, for a person to approve.

Assistants over your records

Answers from your own documents and data, with the source shown and nothing invented.

Integration into existing software

AI steps added inside the applications your team already uses rather than in a separate tool.

Evaluation and monitoring

Accuracy measured on your data before rollout and watched after it.

Controls

The controls every AI feature ships with

The model is the smaller part of the work. These are the parts that make it safe to run.

Control	What it means in practice
Data boundaries	Which data the model may see, where it is processed, and what is never sent outside your systems, agreed in writing.
Human review	Uncertain outputs go to a person; confident ones are still sampled. The threshold is yours to set.
Exceptions	Anything the model cannot handle lands in a queue with the reason and an owner, not in silence.
Sources shown	Assistants answer from your records and show where the answer came from.
Evaluation	Accuracy measured on your real documents before rollout, with a written result.
Monitoring and accountability	Volumes, accuracy, and costs visible after launch, with a named owner on your side.

When companies call us

If this sounds like your business, here is where we start.

Accounts payable re-types every supplier invoice

Extraction into the accounting system with line-level review of anything uncertain and an exception queue.

Service requests arrive as free-text emails

Classification and routing into the work-order system, with the reason shown and a person able to override.

Staff answer the same policy questions daily

An assistant over the approved documents that shows its source and declines what it cannot find.

Sales spends hours summarizing call notes

Drafted summaries and follow-ups in the CRM for the rep to approve.

Documents, requests
Model stepextract · classify · draft
confidentSystem of recordsampled for accuracy
uncertainReview queueperson decides

Nothing posts without a threshold, a review path, and a sample. The exception queue has a reason and an owner.

How the work runs

Scoped in writing, built in releases, handed over.

01
Pick a repeated task with a measurable outcome
One task, representative data, and a definition of correct.
02
Set the data boundaries
What the model may see and where it runs, agreed before anything is built.
03
Prove it on your data
Accuracy measured on real examples, with the result written down, before rollout.
04
Build it into the workflow
The AI step inside the application people already use, with review and exceptions.
05
Monitor and expand
Accuracy, volumes, and cost watched after launch; the next task only once the first proves reliable.

Deliverables

What you receive.

ScopeAgreed

First release · version 1.2Scope and acceptance

Includes: The core workflow, roles and permissions, and the first integration
Left for later: Reporting and the second integration
Acceptance: Criteria agreed with your team
Price: Fixed before the build begins
Start date: Agreed in writing

Approved by operations Approved by finance

RunbookHanded over

Operations · version 1.0Running the system

01Deploy a releasePipeline
02Roll back a releaseTested
03Restore from backupTested
04Alerts and who respondsCurrent
05Access and secretsBy role

Written scope
What the first release includes, what it leaves for later, the integration decisions, the acceptance criteria, and the price, agreed before development begins.
Release notes
What changed in each release, what was tested, what the security review covered, and how to roll it back.
Documentation and runbooks
How the system is built, how to run it day to day, and what to do when something fails.
Training and handover
Sessions with the people who will use and run the system, and a handover checklist your team signs off on.

Every release ships with tests, a security review, and documentation, so your team can run what we build.

Your first release

What the first release could include.

The scope is written and agreed before development starts. This is the shape it usually takes.

In the first release

One task, inside the existing workflow, with review and an exception queue
Data boundaries and processing location agreed in writing
An evaluation on your own data with a written result
Monitoring of accuracy, volume, and cost
Documentation and a named owner on your side

Usually later

The second task, once the first proves reliable
An assistant over the records the first task cleaned up
Automation of the confident cases, with sampling

Systems it may connect

Built to work with what you already run.

Accounting and ERP for document processing
CRM and ticketing systems for routing and drafting
Document stores: SharePoint, Google Drive, file servers
Email and messaging
Model providers chosen for the task and the data
Your existing applications, where the AI step belongs

Healthcare staffing agency

Coordinators review uncertain credential documents instead of typing every license by hand.

At a healthcare staffing agency, new clinicians send licenses, certifications, and training certificates as scans and phone photos, and coordinators type the details into the staffing system. An AI step inside that system reads each document, extracts license identifiers and expiration dates, matches them to the clinician’s record, and sends anything uncertain to a review screen with the original alongside. Confident results are filed and sampled for accuracy. A sensible next phase flags credentials nearing expiration before a coordinator books a shift.

Where this shows up

Industries where we do this work.

Examples, not a closed list. We take substantial projects from independent businesses in other industries too.

Logistics and distribution businesses

Orders, stock, and shipment status that agree across the ERP, the warehouse, and the carriers, so customers see where their order is without calling.

Field service businesses

Every branch runs one process from first call to paid invoice, with the office and the crews working from the same board.

Private equity portfolio companies

Software across the hold period, with a portfolio assessment for the fund and a delivery path for each company.

Another industry entirely

Describe the workflow and the systems around it. Fit depends on the work and the stakes, not the sector.

In plain terms

What is AI development?

AI development and integration adds language and document models to the software a business runs: reading, classifying, drafting, and answering from its own records. The engineering around the model matters more than the model: data boundaries, review, exceptions, evaluation, and monitoring.

We build AI features inside existing workflows with those controls in place, and we say when a task does not justify a model. The work starts with the first useful task, built and measured.

Questions

Questions about AI development and integration.

What buyers ask before a project starts, answered plainly.

Which tasks are worth it?

Repeated tasks with representative data and a clear definition of correct: reading documents, sorting requests, drafting responses, answering from approved records. Novel judgment calls are not.

Where does our data go?

We agree on that in writing before anything is built: what the model may see, where it is processed, and what never leaves your systems.

How do you stop it inventing answers?

Assistants answer only from your records and show the source. Anything not found is declined rather than guessed. Extraction and routing send uncertain cases to a person.

How do we know it is accurate?

It is measured on your real documents before rollout, with the result written down, and monitored after. The review threshold is yours to set.

Does it replace the team?

It changes the work from typing to checking. The people who did the task usually become the reviewers and the owners of the exception queue.

Can it go inside our existing software?

That is how we prefer to build it: an AI step inside the application people already use, not a separate tool to switch to.

Discuss an AI use case.

Tell us about the work and the systems around it.
We’ll come back with a written scope for a first release.