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AJAI
A modern field-service workshop and operations space at dusk.

Custom software + AI systems

When off-the-shelf software stops fitting, we build the system around the work.

Your business shouldn't have to change how it operates to satisfy generic software. We learn how the work actually happens, identify what keeps getting in the way, and build the custom system around it.

Built around your workflowConnected to what you useAI where it earns its placeProven against real work

Built around the business

Every business has a different problem hiding inside the work.

Below are examples of what becomes possible when technology is designed around how a business actually runs—not the other way around.

Top Tier Watersports · Payroll operations

Payroll went from a week of coordination to under two minutes.

Payroll depended on a shared spreadsheet, scattered notes, and rules that lived in one employee's head. We clarified how the business actually handled every case, built PayCanvas around that operation, and proved the new system against the old process before switching over.

01 / 04Before
01 · Before

Payroll started as a recurring rescue.

A shared sheet, scattered notes, and critical rules held in one person's memory.

Glass spreadsheet panels and scattered cells flowing into one organized payroll record—an illustration of the transformation.

Three people could spend several days assembling a single payroll run.

02 · Map the operation

We made every payroll rule and dependency visible.

The actual Top Tier payroll blueprint connected sources, normalization, pay streams, business rules, and outputs before the system was built.

Top Tier payroll blueprint mapping source systems through normalized data, payroll streams, business rules, and traceable outputs.
03 · Shadow run

The new system proved itself against the old one.

PayCanvas ran alongside the existing process, surfacing hidden differences while keeping every result connected to its source and rule.

Pay periods  /  Payroll worksheet

Payroll worksheet

28 employees · Feb 08–21

Estimated payroll$47,862.14
Employees28
Exceptions surfacedReview
Search employees
EmployeeSunMonTueWedTotal
ACAvery ColeHourly · Multiple$218.44$184.20$205.76$608.40
JLJordan LaneHourly · Multiple$302.18$194.60$663.18
MRMorgan ReedHourly · Multiple$188.12$227.30$241.06$853.73
TBTaylor BrooksHourly · Multiple$264.80$218.48$176.20$659.48

Illustrative interface · synthetic names and payroll data

04 · Outcome

Payroll became a result the business could trust.

Historical payroll preserved. Every result connected to its source and rule.

PayCanvas Payroll, simplified
BeforeSeveral days.

Three people coordinating a payroll run.

With PayCanvasUnder
2 minutes.

Accurate. Reliable. Traceable.

Rules clarified
Results traceable
History preserved

Regional field-service company · Connected operations

A paper-driven business became one connected operation.

Nearly two decades of handwritten contracts, customer history, job details, quotes, sketches, and accounting records were spread across filing cabinets and paper packets. We built one mobile and web system around the company's familiar workflow—from the first appointment through quoting, engineering, permitting, installation, invoicing, and payment.

What would change if your operation worked as one system?
01 / 07The opportunity
01 · The opportunity

Years of customer and project history existed only on paper.

Handwritten contracts held valuable customer relationships and job history, but none of it was searchable or reusable without finding the original file.

Tabbed customer files arranged inside a physical filing cabinet.
2008 → todayCustomer history locked in paper records
02 · Recover the history

Scanned contracts became usable customer and project records.

Batch intake and AI-assisted extraction turn the paper archive into structured records ready for human review and everyday use.

Archive import interface splitting a scanned filing-cabinet batch into individual project records for review.

Actual product screen mock · exported from Paper

03 · Schedule the work

Appointments and travel time were planned together.

Office staff could assign appointments, account for drive time, and keep every sales route visible without rebuilding the schedule by hand.

Desktop scheduling interface coordinating sales appointments, routes, and travel time.Mobile sales schedule keeping appointments, customer details, mapping, and quote actions with the sales rep.
04 · Measure the site

The property itself became part of the project record.

GPS and parcel-map context let the team measure footprints and understand offsets before engineering and permitting began.

Mobile property measurement interface using a site map to capture a project footprint.
05 · Quote digitally

Handwritten math became a complete digital quote.

Scope, pricing, project visualization, site plans, payment schedule, and approval now travel together in one customer-ready record.

Digital quote preview connecting scope, pricing, project visualization, site-plan details, and approval.

Scroll the quote to see the complete customer-facing record.

06 · Follow the project

The project record stayed with the work.

Customer details, files, quotes, crews, materials, reminders, timelines, and original scans remained connected from the office to the field.

Connected desktop project record containing customer details, files, quotes, project management, timeline, and original scans.Mobile project list keeping active field work and customer status available to the team.

Scroll the record to follow the operational context.

07 · See the operation

Office, field, and accounting finally shared one view.

Collections, open invoices, project balances, crew payouts, and recent activity became visible without rebuilding the story from separate paper records.

Financial performance interface connecting collections, invoices, balances, and projected payouts.

Engage · Customer conversations

Every customer gets an answer. Your team doesn't have to answer everything.

Calls, website chats, emails, and texts were pulling teams away from the work only they could do. We built Engage as one AI-assisted conversation layer across voice, website chat, email, and SMS—grounded in the business's real knowledge, ready to qualify the request, and able to bring in a person with the full context attached.

01 / 06The interruption
01 · The interruption

The same questions kept pulling the team away from the work.

Calls, chats, texts, and emails arrived throughout the day—often asking for information that already existed somewhere on the website.

An editorial service-business setting behind the Engage conversation layer.
Website chat
02 · One conversation layer

Engage meets the customer in the channel they already chose.

Voice agents, website chat, SMS, and email share one responsive layer instead of creating four separate queues for the staff.

A business owner taking a customer call.
Engage
Engage native iPhone sign-in screen.
Secure operator access
Engage native iPhone unified conversation inbox.
Live conversation inbox
NATIVE iPHONE APP
Every customer conversation, wherever the operator is.

Website, Messenger, human takeover, leads, and live activity stay together in one working inbox.

03 · Grounded answers

Answers come from the business—not from a generic script.

Engage uses the company’s services, policies, coverage area, and real knowledge to answer clearly without making the customer hunt through an FAQ.

A quiet workspace behind the Engage mobile conversation.
9:41● ●
Engage
Jordan Miles Voice · qualified
ENGAGE SUMMARY

Cooling issue · urgent · 36608
Earliest appointment requested

My AC is running but the house is still 82°. Can anyone come this week?
I can help. You’re in our service area, and I found two available windows. Is tomorrow morning or Thursday afternoon better?
Tomorrow morning.
Appointment ready
Tomorrow · 8–10 AMDiagnostic visit · 2412 Royal Crest Dr.
Write a reply…
Knowledge-backed conversation
04 · Qualify the opportunity

The next conversation starts with the right context already collected.

Engage can identify the need, urgency, location, and preferred next step before the request ever reaches the team.

An editorial service-business setting behind the Engage conversation layer.
SMS follow-up
05 · Human takeover

When judgment matters, the person steps into a conversation—not a blank screen.

The full transcript and collected context move with the customer, so staff can take over without asking them to start again.

An editorial service-business setting behind the Engage conversation layer.
Human takeover
06 · The outcome

The customer gets an answer. The team gets a qualified next step.

Questions become useful conversations, qualified opportunities, and booked appointments—without making the front desk repeat the same information all day.

A qualified service professional ready for the next appointment.
Engage
PERFORMANCE · LAST 30 DAYS
Conversations that moved the business forward.
All channels Live
Qualified leads386↑ 28% from prior period
Appointments booked14237% of qualified leads
Conversion rate12.0%↑ 3.2 points
Staff hours returned61hRepetitive questions handled
Qualified conversationsConversation volume that reached a useful next step
+28%
300200100
W1W4W8W12
Channel mixWhere conversations begin
1,184started
Voice42%
Website28%
SMS18%
Email12%
Conversation funnelVisitors to confirmed appointments
4.6×
Visitors8,420
Conversations1,184
Qualified386
Booked142
Today's outcomesQualified by Engage

Cooling diagnosticJordan Miles · Voice

Booked

Patio estimateAvery Chen · Chat

Booked

Warranty visitTaylor Brooks · Email

Ready

Local service businesses · Opportunity intelligence

The first credible response often gets the customer.

Every day, people ask local groups for recommendations, estimates, and help finding reliable businesses. We built Lurker to identify the posts showing relevant buyer intent, alert the business while the opportunity is fresh, and help the team prepare a useful response without giving up control of what gets posted.

01 / 05The noise
01 · The noise

High-value buying signals were buried in everyday conversation.

Local groups generate hundreds of posts, comments, and recommendations. Only a few may describe the exact service a business provides—and finding them manually means reading everything.

Lurker
Lurker Today screen showing a focused queue of relevant local opportunities.
Find the signal without reading every post.
02 · The missed moment

An opportunity discovered too late is often no longer an opportunity.

For high-value services, response time matters. By the time someone on the team notices a relevant post, another company may already have started the conversation.

Lurker
Lurker opportunity queue preserving a fresh local buying signal.
09:14Request postedBuyer intent becomes visible
09:16Lurker alertRelevant post reaches the team
NowOpportunity readyOriginal context attached
Useful while the conversation is still open.
03 · The opportunity queue

Lurker brings the posts worth reviewing into one focused queue.

Selected communities are monitored continuously. Relevant requests are identified, organized, and delivered with the original context while the buyer's intent is still fresh.

Lurker
Lurker Today screen showing identified service opportunities and their source context.

01Selected communitiesThe business chooses where Lurker watches.

02Relevant intentPotential requests are separated from general activity.

03One review queueThe original post and context stay together.

Current native Lurker app · opportunity queue
04 · The response

The team can respond quickly without sounding automated.

Lurker helps prepare useful, brand-aligned language and keeps approved imagery ready for the conversation. The business still decides what gets posted.

Lurker
Lurker Content Library containing approved business imagery for opportunity responses.
Response workspaceHelpful language. Approved visuals. Human decision.

The team gets a faster starting point while keeping control of the final response.

Context retainedBrand readyReview before posting
Current native Lurker app · content library
05 · The outcome

Attention moves from monitoring feeds to winning the right conversations.

The business can respond earlier and more consistently, with the context needed to be credible. In a high-ticket category, one recovered opportunity can be meaningful.

Lurker
Lurker Account screen showing monitoring sources and account health.
Lurker Today screen showing the resulting queue of opportunities.
The operational shiftFrom searching through feedsto reviewing opportunities.

Monitoring stays active. The team's attention goes to the conversations worth joining.

Monitoring health and opportunities stay connected.

Independent R&D · Market data + machine learning

A model is only as trustworthy as the pipeline feeding it.

Financial markets are one of the most demanding environments in which to keep data timely, consistent, and comparable. We built an ETL and machine-learning research system that collects and reshapes historical futures and options data, applies the same data contract to live feeds, and compares potential volatility expansion with the volatility already priced into the market.

Where is inconsistent data limiting the decisions you can make?
01 / 05The raw feeds
01 · The raw feeds

The difficult part starts before a model sees the data.

Futures trades, option chains, volatility surfaces, timestamps, expirations, and contract changes arrive from different sources with different assumptions.

FuturesTrades + quotesMultiple contracts · rolling expirations
OptionsChains + GreeksStrike · expiry · implied volatility
HistoricalResearch archiveCleaned observations · known outcomes
LiveStreaming marketChanging prices · incomplete intervals

Different sources. Different clocks. One decision has to reconcile them.

02 · Historical + live parity

Live information must have the same meaning as the history used for testing.

A model trained on carefully prepared history cannot be trusted if the live pipeline calculates, aligns, or timestamps the same inputs differently.

Historical research
Tested feature shape
Same definitionsTime · contracts · features
Live analysis
Production feature shape
03 · The data contract

One repeatable pipeline turns fragmented feeds into comparable features.

The ETL system validates source data, normalizes contracts and time, engineers the required features, and preserves the transformation used for every observation.

01IngestFutures + options feeds
02ValidateMissing, late, or malformed
03NormalizeTime + contract continuity
04TransformModel-ready features
05ObserveTraceable live output
One data contractHistorical and live inputs arrive at the model with the same meaning.
04 · Forecast versus pricing

Expected volatility becomes useful when it can be compared with what the market already priced.

The research system evaluates potential volatility expansion and compares the model's expectation with the implied volatility embedded in options pricing.

Payoff and convexity visualization comparing strike, spot, and positive gamma.
Model expectation Market-implied pricing
05 · The research loop

Research, live analysis, and inspection now operate through the same system.

The result is a continuously operating loop where assumptions can be traced, discrepancies can be inspected, and new ideas can be tested without rebuilding the data foundation.

Research loopMeasured · comparable · inspectable
01CollectHistorical and live feeds
02TestWalk-forward evaluation
03CompareForecast versus pricing
04InspectTrace the discrepancy

Ongoing independent research · Not investment advice or a performance claim

Start with the friction

What do you dislike most about running your business?

Tell us where the work feels harder than it should. Then, if it makes sense, choose a time directly from AJAI’s calendar.