A custom app or portal with AI inside is a web application built for one job in your firm, where staff or clients sign in and the AI reads, checks and drafts as part of the screen. You need one built rather than bought when no existing tool fits the way your firm handles its own documents, steps and approvals.
It is a website your people sign in to, made for one purpose. A client portal, a partner dashboard, an intake form, a scheduling page. The difference from an ordinary web app is that the AI sits inside the screen. It reads what was uploaded, compares it with what was asked for, drafts the reply or the summary, and hands the result to a person to approve.
The app works with the systems you already run, so nobody types the same facts twice. Every action is written to a record you can read afterwards.
Staff or clients sign inSingle sign-on
Works with your systemsSystems you already run
AI reads and draftsChecked before it confirms
A person approvesWhat matters
Record writtenAppend-only
Is a custom app the right fit?
It fits when
Staff or clients repeat the same request, upload or question many times a week.
A shared spreadsheet or a pile of email threads is standing in for a proper screen.
The job depends on your own documents, rules and wording, which no packaged tool knows.
Clients should see status without calling the office to ask.
You want a record of who did what, kept by the software itself.
It is the wrong tool when
An off the shelf tool already does the job well enough for your firm.
The job happens a few times a year and a checklist would do.
The rules change every time, so nothing can be written down once.
A person must use judgment at every step and the screen would only slow them down.
What can you build for a firm like ours?
These are illustrations of the kind of app or portal that can be built. They are examples of the job, not client work. Real work is further down the page.
CPA and accounting firms
Example
Client document portal
Today
Staff email clients a list of what is needed for the return, then chase the missing items and open each attachment to see whether it is the right one.
The build
Clients upload to a portal that shows the request list. The AI reads each upload and checks it against what was requested, flagging a missing page or the wrong year.
A person still decides
The preparer accepts or rejects each item and decides when the file is complete.
Example
Engagement status dashboard for partners
Today
Partners ask managers where each engagement stands, and the answer comes from a spreadsheet that is updated when someone remembers.
The build
A dashboard reads status from the workpapers and the practice system and shows each engagement by stage. The AI drafts a short note on what is holding up the ones that have stalled.
A person still decides
The partner decides which engagements need attention and what to tell the client.
Investment advisers and asset managers
Example
Onboarding portal for new advisory clients
Today
New clients receive a bundle of forms by email, return them in pieces, and an associate checks each one for blanks and missing signatures.
The build
A portal walks the client through each form and shows what remains. The AI reads the uploaded identity and account documents and lists anything incomplete before the associate opens the file.
A person still decides
The compliance officer approves the client file before the account is opened.
Example
Operations dashboard that replaces a shared spreadsheet
Today
The daily tasks of the operations team live in a spreadsheet that several people edit, with no record of who changed what.
The build
A web dashboard holds the same tasks with owners, due times and sign-off. The AI reads the morning files and marks which tasks are ready, late or waiting on someone.
A person still decides
The operations lead assigns the work and signs off the day.
Law firms
Example
Matter intake app
Today
A prospective client calls or emails, a staff member retypes the details into a form, and a conflict check starts only after that.
The build
The prospective client completes an intake form on the firm's site. The AI reads the free text, pulls out the parties and the type of matter, and prepares the conflict check list.
A person still decides
An attorney decides whether the firm takes the matter.
Example
Secure client question desk
Today
Clients email the same questions about fees, deadlines and what to bring, and a paralegal answers each one from memory or from old emails.
The build
Clients sign in and ask in a secure portal. The AI drafts an answer from the firm's own written policies and cites which policy it used.
A person still decides
A paralegal or attorney reviews each draft before the client sees it, and legal advice stays with the attorney.
Mortgage and lending
Example
Borrower document portal
Today
Processors email borrowers for pay stubs and statements, then open each attachment to see whether it covers the right period.
The build
Borrowers upload to a portal that lists the conditions still open. The AI reads each document, names it, files it against the right condition and flags a missing page.
A person still decides
The processor clears each condition and the underwriter makes the credit decision.
Example
Pipeline dashboard for loan officers
Today
Loan officers keep their own lists of which loans are waiting on what, and the lists drift away from the loan system.
The build
A dashboard reads the loan files and shows each loan by stage with open conditions. The AI drafts the follow-up message to the borrower or the title company.
A person still decides
The loan officer chooses what to send and when.
Operations-heavy businesses
Example
Dispatch board
Today
A dispatcher assigns jobs from a whiteboard and a phone, and drivers call in to report what is finished.
The build
A board shows each job, truck and driver, updated as drivers mark work done. The AI reads incoming requests and proposes which job goes to which truck.
A person still decides
The dispatcher accepts or changes each assignment.
Example
Customer self-service page for quotes and scheduling
Today
Customers phone for a quote and a date, and the office checks the price list and the calendar before calling back.
The build
Customers describe the job on a page, and the AI reads it, applies the firm's written pricing rules and offers open dates from the calendar.
A person still decides
Office staff confirm any quote that falls outside the written rules.
Built and running
One real example of a web app build, in daily use.
Built and running
A financial services firm
Before
Daily operations ran on manual work and desktop tools that only a few people could maintain.
What it does
Several internal operations dashboards serve the firm's teams. The tools moved to hosted web apps that run in the firm's own Azure environment.
Step 1ScopeOne working session. A plan and a fixed price.
Step 2BuildWorking software early and often, on your own documents and systems.
Step 3LaunchYour people trained on their real work.
Step 4RunHosted, watched, improved.
The plan sets the timeline. If you want to see something working before committing to the full build, a two week scoping sprint produces a working prototype on your own documents, a plan and a fixed price.
Describe the job
Walk through who uses the screen, what they upload or ask, and what happens next.
Gather real samples
Collect the documents, forms and messages the app will handle.
Agree the rules
Write down what the AI may read, draft and change, and what needs a person.
Build and test on your files
Early versions run on your own documents so you see real behavior.
Train the people
Staff learn the app on their real work, and clients get a plain guide.
Switch on with the record running
The record starts on day one, so you can review what the software did.
What do you get at handover?
Working software
Running in your environment, built on your documents and systems.
The record of actions
An append-only record of what the software did.
Written rules
What the app may touch and what it may do.
Training
For the people who use it, on their real work.
A named security review
Completed before the app goes to users.
Someone to call
DoubleChecked hosts, watches and improves it after launch.
How is our data handled?
The AI runs in private, client-controlled environments, and client data does not go to public AI services. DoubleChecked contracts directly with Anthropic under commercial terms and a data processing agreement. Deployments include single sign-on, role design, retention settings, audit logging, spend limits and network allowlisting. Zero data retention is available through the API, not the chat interface. See how security works.
Inside your environment
Staff and client sign-inYour documentsThe AI modelThe record
Outside
Public AI servicesResellers
Company data does not go to public AI services.
What does it connect to?
Accounting and general ledger
Read balances and post approved entries.
Document management
Store and fetch client and matter files.
Email and calendar
Send notices and offer open dates.
CRM
Keep client records in step with the app.
Bank and card feeds
Show payment status to staff.
Line of business systems
Loan, practice and job systems you already run.
Questions buyers ask
How long does a build take?
The plan sets the timeline, and you see it before the build starts. A two week scoping sprint gives you a working prototype on your own documents first, so you can judge the idea with something real in front of you.
What will it cost?
Every project gets a fixed price after one working session, and you know the number before the build starts. A working session is 45 minutes on one idea from your company.
Where does our data go?
AI runs in private, client-controlled environments, and client data does not go to public AI services. DoubleChecked contracts directly with Anthropic under commercial terms and a data processing agreement, with no reseller in between.
Who checks the output?
The software checks before it confirms an action, and a person at your company reviews and signs off on outputs that matter. Each build keeps an append-only record of what the software did.
What happens when it is wrong?
The check before confirmation catches many errors, and the rest reach a person who can correct them. The record shows what the software read and did, so the cause can be found and the rule fixed.
What do you need from us?
One person who knows the job, a set of real documents or examples, and access to the systems the app will work with. Your people are trained on their real work before they use it.
Can clients use it without a new login to remember?
Deployments include single sign-on and role design, so staff and clients each see only what their role allows. How clients sign in is agreed in the plan, based on the job and your systems.