Systems that dothe work.AI automation examples, from paperwork ready for your sign-off to systems that run a whole workflow. Our own firm runs on them.

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What runs our businessThe first business we automated was our own.

Most companies meet AI as a pile of separate tools. One person drafts emails with it, another summarises calls, and none of it adds up, because the way the business really runs still lives in people’s heads.

We built ours as one system that holds how we work and does the work. HAIBRID Consulting runs on it every day. Strategy, research, content, prospecting in three markets and client delivery all run through it. It built the page you are reading.

Four things make it a system.

  1. Agents with one job each

    An agent is AI software given a job, the tools for that job and limits it cannot cross. It works out the steps itself. Ours each have a narrow job and no more access than the job needs. The agent that writes never touches the database. The agent that changes the database does nothing else.

  2. Rules it follows every time

    Our standards are written into the system, so they hold on the busiest day. A claim is checked against its source before a client sees it. A new job is tested on a small batch before it runs at full size.

  3. A memory that grows

    Every decision we make and every problem we solve is written down where the system reads it before it acts. It does not repeat a solved mistake, and nobody explains the same thing twice.

  4. A second check before anything ships

    AI supplies the judgment and fixed code makes the final call. Important work is reviewed by a second AI model from a different maker. Whatever needs a person arrives in the chat we already use, with the reason attached.

The day board: one day of our company, midnight to midnight, in four lanes: Research, Content, Prospecting and Delivery. Sources are read every hour, rising subjects are counted every four hours, new companies are found at 6 a.m. and the top leads go to chat, and client work runs through the day. Each lane has its own agent, each passes a rule, every finished job adds a card to the memory shelf, finished work gets a second check, and three items go to a tray marked Needs a person. One marker on the Content lane reads This page.

Running a company this way is how we know what holds up in daily use. It is also the working model for what a Fractional AI Officer sets up inside your company: your rules, your knowledge, your people in charge.

See the Fractional AI Officer

Win customersSales and lead generation systems that find the right customers and tell you why.

Built for a client · Delivered in 25 days

A sales lead reviews the day’s best prospects in ten minutes, from a phone.

The problem

Your salespeople know what a good customer looks like. Finding the next one takes hours of searching, and the company CRM is shared, locked down and not yours to rebuild.

Team chatThis morning
Company name removedFit 82
Near one of your warehouses · Expansion announced
Morning review done.

Account 3 of 26

Sales lead’s workbookAccounts
AccountPriorityContacts found
Account 1Urgent
Account 2SuppressNever shown again
Contacts found5
Suppressed: 1
What we built

For a manufacturer’s sales team, a system that searched a database of 35 million companies every night. It scored each one on what this client cares about: how close the company sits to one of the client’s 12 plants and warehouses, and which of 63 buying signals it shows. Competitors and poor fits were removed before anyone saw them.

Each morning the best 20 to 30 arrived as cards in the team’s chat, with four buttons: Urgent, High, Standard, Suppress. The button decided how many contacts the system went and found, five people for an urgent account and none for a suppressed one. One tap set both the priority and the spend. A suppressed company never came back.

We built it on a workbook the sales lead owned, so nothing waited on the CRM, and we designed it to move into the CRM later with no rebuild. The next phases were drawn into the same design: outreach written in the sales lead’s voice, and a briefing before every meeting.

Built from
  • Scheduled rules
  • AI scoring
  • Connected software
  • One person’s tap
  • 25 days from contract to delivery
  • 20 to 30 ranked companies each morning
  • 204 contacts delivered with name, title and email

ProvesWorkflow AutomationSystems Integration

Runs our firm

Two whole markets, researched and ranked: 1,588 home-services companies and 1,477 private-equity firms.

The problem

A bought list tells you a company exists. It does not tell you whether to call, who to ask for, or what to say.

What we built

For home services, the system finds companies across 23 US metro areas, reads each one’s website and rates how the company runs today: how fast it promises to respond, whether customers can book online, what software it shows.

For private equity, it starts from public regulatory filings and adds each firm’s deal history, 3,085 deals and 2,818 portfolio companies so far. Five automatic checks disqualify the firms that could never be a fit. The rest are scored on seven weighted measures. A daily news watch sorts each headline by what it means: an acquisition, a new fund, an exit, a change of leadership. For the firms that rank highest, the system writes a brief: why this firm, why now, the likely problem, and a first message to send.

The second market reused about half of the first one’s parts and was running within weeks.

The same research, put to a second use

Our home-services research also became a published study: what 2,477 contractors said in public forums about running their businesses, sorted into 503 distinct problems.

Read the contractor study
Built from
  • Scheduled rules
  • AI reading and scoring
  • Rules that disqualify
  • Connected software
  • 1,588 companies
  • 23 metro areas
  • 1,477 firms
  • 163 briefs written

ProvesWorkflow AutomationSee alsoHome servicesPrivate equity

Built with a partner

The Bahamas Digital Readiness Index: 2,600 businesses scored on how customers find them.

The problem

Everyone in a market has a feel for it. Almost nobody has it measured, so nobody can say who is ahead, who is invisible, or where the quick wins are.

A street of 28 shopfronts, each with four doors a customer might knock on: Google listing, website, Facebook page, WhatsApp. On this street of 28, one shopfront has all four doors lit and six are fully dark. The street sorts itself into groups, then one shopfront lifts out and becomes its two-page scorecard, its four doors down the side and the industry average beside each.

  • What it found4 in 100About four businesses in a hundred have all four doors open.
  • 1 in 5About one in five has none open.
  • 590590 appear on Google Maps with a listing nobody has claimed.
What we built

With our partner DDigiLeads, a digital agency in The Bahamas, we built the first structured measure of how ready Bahamian service businesses are for customers who look online first.

Six sources feed it. Rules match the same business across them by phone, name, address and map listing, and a doubtful match is thrown out, never guessed. Every business is checked on four doors a customer might knock on: its Google listing, its website, its Facebook page and WhatsApp. Then it is scored from 0 to 100 and placed in a tier.

What it does next

The index feeds our partner’s sales work. A business can receive a two-page scorecard of its own four doors, set beside its industry, before anyone asks it for a dollar.

Point the same build at your territory, your competitors or your own customer list.

Built from
  • Six connected sources
  • Matching rules
  • AI summaries
  • Scoring in code
  • 2,600 businesses
  • 8 industries
  • 6 sources

ProvesBusiness IntelligenceSystems Integration

Run the operationDocument automation that reads the paperwork, fills your systems and keeps the deadlines.

Built in 48 hours

A police report becomes an open case, a calendared deadline and a client email in 23 seconds.

The problem

In personal-injury law, the firm that responds first has the advantage. Intake is where the time goes: 30 to 60 minutes of a paralegal reading a scanned report and typing it into the case system.

What we built

A scanned report goes in. AI reads it directly, between 191 and 275 details per report, and rates each one High, Medium or Low for how sure it is. On our test reports the Low ratings fell on the handwriting a person would squint at too.

The system then does what a paralegal would do next. It writes the case into the case-management software, puts the filing deadline on the attorney’s calendar, generates the retainer agreement, sends the client a personal email and files a copy of that email on the case.

Two things make it safe to run every day. Run the same report twice and it updates the case; it never creates a second one. If one step fails, the others still finish, and the summary names the step that did not.

The case software had five behaviours its documentation does not describe. We found each one, tested it and built around it. The finished system then ran 11 times in a row without a failure.

Built from
  • An AI reading step
  • Fixed rules
  • Five connections into the case software
  • A custom document service
  • 48 hours to build
  • about 23 seconds per report
  • 5 of 5 test reports
  • 11 clean runs in a row
Read the case study

ProvesSystems IntegrationWorkflow AutomationSee alsoPersonal-injury law

Our own build · Regulated data

A tax-preparation system that starts from the tax agency’s own record and keeps personal data locked.

The problem

Regulated paperwork is dense, the forms have to agree with each other, and the data in them must never leak. Most mistakes happen in the hand-offs between documents.

What we built

An agent works the tax agency’s online account and pulls the filing history, so the work starts from what the agency has on record. Statements, exports and scanned receipts come in from wherever they live and are sorted into the categories the return needs.

The forms are produced by tested code with every field defined, so a missing value fails before filing and not after. A cross-check compares the finished forms to the agency’s record and to the source documents. Then the system fills the filing site from the checked figures.

The privacy rules are enforced by the build itself. Personal data is encrypted when it is not in use and kept out of the code entirely. A scan blocks any change that carries a personal identifier. The automation holds no personal values of its own; they are supplied when it runs.

The same build fills payer portals, carrier sites and state filings from a checked record, and waits for a person’s approval wherever you want one.

Built from
  • Agents
  • Tested code for the arithmetic
  • Open-source parts where good ones exist
  • Encryption and a blocking scan
  • First working version in 3 days
  • 7 stages built, from the agency’s record to the filing

ProvesWorkflow AutomationSee alsoAccounting and taxInsurance agenciesMedical billing

If one of these is your problem

See your own work here? Get the Operations X-ray, our free first look, or book a call and tell us about it.

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Steer the businessBusiness intelligence that warns you early and answers when you ask.

Built at a Fortune 250 enterprise, before HAIBRID

An account-risk system that retained $1.8B of revenue at risk of leaving.

The problem

In a large book of clients, the ones about to leave rarely say so. The signs are scattered across systems, and by the time anyone adds them up the decision is made.

$1.8Bof at-risk revenue retained, 2013 to 2018

A drawn service screen of the kind a rep works in all day, with a concession about to be approved and an orange risk flag on that row. Under the screen, three stacked layers: a wide base, one view of each client; a middle layer, the workflow; a top layer, the model.

What was built

Our founder built the client view this system stands on, launched the risk system, and went on to lead the analytics organization around it. It was built in three steps.

First, one view of each client. Payroll, benefits, case and call-centre records were joined. A question that used to take one analyst two weeks to answer became a live screen.

Second, the warning went where the work already happened. The risk flag appeared inside the steps service teams were already taking: approvals, pricing look-ups, concessions. Using the system was easier than working without it, so nearly everyone it was built for used it.

Third, the work fed the next build. Every flag, every save and every concession was recorded as it happened. Years later, that record trained the model that predicted which clients would leave.

When the company moved its service teams to a new platform in 2018, the data and the documented workflows moved with them.

Built from
  • Joined systems
  • A workflow people chose to use
  • A prediction model trained on the work

ProvesBusiness IntelligenceSystems IntegrationFractional AI OfficerAbout the founder

Runs our firm

A market watch that reads 35 sources every hour and passes on only what matters.

The problem

The news that should change your plan arrives mixed in with everything that should not. Somebody has to read all of it, or nobody does.

What we built

Each hour the system collects everything new from 35 sources. Plain rules throw out the obvious junk before any AI is paid to read it. AI then scores what is left on four measures. Fixed code, not the AI, decides which tier each item lands in, so the bar can be moved without touching the AI. The top tier goes straight to our chat.

Every four hours the system counts which subjects are rising and saves the count. That record shows what changed since last week or last month, which a live feed cannot.

Two of every three items never reach a person.

Give it your competitors’ prices, your regulators, your customer reviews or the deal news in your sector and it does the same job.

Built from
  • Scheduled rules
  • AI scoring
  • Code that makes the call
  • Alerts in chat
  • 35 sources
  • more than 34,000 items read
  • 2,261 passed on

ProvesBusiness IntelligenceWorkflow Automation

Live demonstration · Sample data

Ask a company’s numbers a question in plain words.

The problem

Your numbers sit in three systems. To learn why one moved, somebody builds a report, and the answer arrives after the moment to act.

Try it
Practice numbersSample data
QuestionWhy are collections down this week?

Collected is down 8% because 2 deposits ($3,680) haven’t cleared. Billed and visits are in range.

The figure behind it
Collected, this week$42,140down 8% vs $45,820 last weekBank feed
Your question4 weeks agoThis week
What moved firstSince last week
Held: 2 deposits not cleared$3,680Bank feed
Billed, ties out$48,210Accounting
Visits, in range386Practice system
Accounting·Bank feed·Practice system
What you get back

The answer in a sentence, the figure behind it, and the system it came from. What moved since last week shows first.

How it is built

One screen sits over the systems a business already runs: the accounting, the bank feed, the job or practice software. Every number is defined once and produced the same way every time, so each answer traces to its source. Between your questions, an agent watches the same numbers and tells you when one moves.

Built from
  • Connected software
  • One definition per number
  • AI that answers in words
  • An agent on watch

ProvesBusiness Intelligence

Find it by serviceEvery build on this page proves a service you can hire.

A police report, an insurance claim, a work order and a vendor invoice are different documents and the same job: read it, check it, enter it, follow it up. A list of plumbers and a list of investment firms are ranked by the same build with different measures. We start from how your work runs, and we never start from zero.

Bring us the job you want built.

Every build on this page was proved on real work before anyone relied on it. Yours starts the same way: a short, paid Proof Sprint that ends with a first build running on your own data.

Start with the Operations X-ray, our free first look at your operation. Or book a call, tell us the job, and we will tell you which of these builds it is closest to and what we would prove first.

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See how we work
Eric LopezScoping call
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