Data & AI engineering for restaurant groups

Senior engineers who have run the P&L.

A contract data and AI team for multi-unit restaurant operators. One warehouse for every system you run, finance controls that tie to the cent, and apps your operators open the next morning. Documented, costed and proven before we call it done.

Start a project See what we build patrick@byte-and-bite.com
Pipeline Console · feeds
Feeds
Marts
Usage
Access
Cost
Anomalies
13 systems · 74 feedschecked 15 min ago
71
fresh
2
late
$0.41
cost today
FeedLast runStatus$/mo
pos.sales_checks06:40fresh10.74
ap.invoices07:20fresh0.69
processor.settlements06:31fresh1.32
ledger.gl_entries03:15late 2h—
weather.hourly05:02fresh0.53
surveys.responses—planned—
25+
years in restaurants, from the line to the data platform
45
documented releases in one eleven-week engagement
200
restaurants on one warehouse for our most recent client
11,042/11,042
store-days reconciled POS to warehouse, every column
<$1/mo
what most of our pipelines cost to run, measured
hours
to add a new source once the pattern exists
What we build

Nine tools we have shipped.

Each runs in production for a multi-unit operator. Pick one to see the screen and the numbers.

Foundation

Restaurant data warehouse

Every system you run, landed in one warehouse with marts that tie out.

→ Loaders, marts, status console
Finance

Reconciliation & close

POS to ledger to processor, by restaurant-day. Period readiness. Generated journals.

→ To the cent, every day
Finance

Invoice exception queue

Duplicates, price outliers, missing bills and fees, surfaced daily.

→ Runs for about $1 a month
Finance

Self-service spend explorer

Who, what, where, how much. Drill to the invoice. No SQL.

→ For VPs and department heads
Planning

What-if budget builder

Describe a scenario in plain English; approve the rules; get a versioned budget.

→ Location × period × account
Operations

Field visit planner

Where a director should go this week, why, and follow-ups that land in their calendar.

→ Plus a rules-driven scheduler
AI intake

Idea-to-spec assistant

An operator describes a problem; out comes a pitch and a priced technical spec.

→ Requirements in minutes
Operations

Speed-of-service analytics

Kitchen bump times by store, station, daypart and item, from the POS.

→ When your POS vendor won't
Enablement

Intranet, tickets & Workspace automation

One front door to every tool, a ticket and idea tracker, org-chart-driven groups.

→ Google-native first
Systems we integrate

We already know the quirks.

Which POS field lies, which API truncates, which vendor keeps only 31 days. That knowledge is most of the speed.

RosnetPAR BrinkOloToastMicrosoft Dynamics 365 Business CentralDynamics GPOttimate / Plate IQFiservFourth / MacromatiXHotSchedulesADPmeezYelp Guest ManagerLocal ClarityQualtricsOpen-MeteoGoogle WorkspaceAppSheetLookerBigQueryDataformCloud RunVertex AI · GeminiClaudeAirbyteAzure SQLPower BI
How we work

The release note is the contract.

Nothing is done until a versioned document says what shipped, how it is built, what it costs, and what is still unproven.

MEASURED

Every number pulled live, on a stated date.

Row counts, bytes scanned, dollars, test counts and run times come from production, with the date beside them. Estimates are marked. If a later run disagrees, the run wins.

TIED OUT

Marts tie to a source to the cent.

Breakdowns to daily totals. Tax marts to sales. P&L lines to the reports Finance already trusts. Differences are explained, not rounded away, and the tie-out query ships with the table.

COSTED

Per run, per month, per year, with a ceiling.

List prices, no free tier assumed, and the cap that stops a runaway. Most pipelines we build cost less than a dollar a month to operate.

GUARDED

Fixes stay fixed.

Policy files and guard scripts fail any deploy that would turn off access control or ship an older version. Refresh jobs recompile the transformation master so a push is never silently undone.

STANDARD

One standard for every screen and table.

Light and dark modes, English, Spanish and French, your brand themes held to accessibility contrast. Raw, mart and ops layers, one store key, your fiscal calendar on every row.

YOURS

Built in your cloud, run by your people.

Everything lives in your own project, under your groups, with documentation written for the analyst and the operator. We leave you able to keep going without us.

See services and engagement optionsMeet the team
What we build

Nine tools we have shipped.

Each one runs in production for a multi-unit operator. Screens are generic renderings; the numbers are what the tools measured.

Foundation

Data warehouse with a status console

One small loader per source system. Marts that assert their own correctness. A console showing every feed's freshness, cost, usage and release stage, with access granted by group.

74 feedsfrom 13 systems
13.3M rowsof store-level weather, $0.53/mo
168/168hourly runs in week one
<$1/mofor most feeds
BigQueryDataformCloud RunLooker
Pipeline Console
Feeds
Marts
Usage
Access
Cost
109 mart tablesstage set by editors
TableStageReaders
daily_totalslive41
gl_entry_detaillive18
card_reconciliationlive7
pl_statement_lineslive12
speed_of_serviceQA3
survey_responsestesting0
Finance

Reconciliation and close readiness

POS to ledger by restaurant-day. Card revenue to what the processor funded. One page that says whether a period can close, and why not. Generated sales journals run in shadow until they match line for line.

11,042/11,042store-days tied, every column
441 of 519journals exact on every line
6.2Mcard settlements reconciled
$2.85/moto check every 15 minutes
Dataform viewsAssertionsConnected SheetsChat alerts
Close Readiness
Periods
Variances
Journals
Cards
Period 10ready: not yet
96.2%
tied
38
not posted
4
variances
StoreDayPOSReason
1064Oct 278,412.60not posted
2203Oct 265,980.15tied
3110Oct 256,204.00−180.00
1181Oct 250.00closure
Accounts payable

Invoice exception queue

Where are you paying money you shouldn't? Duplicates, price outliers, missing recurring bills, fees and tax, rebuilt every morning from your AP platform and ledger. AP drills to the invoice and records a verdict.

195,755invoices scanned in 93 s
896open leads on day one
$1.01/moall-in
7 of 7QA checks every refresh
BigQuery proceduresAP APILedgerReview portal
Exception Queue
Queue
Duplicates
Prices
Missing
Tax
896 open31 new today
TypeLeadAt stake
dupSame amount, two invoice numbers2,418.60
priceCase price 31% over median187.20
missingLinen invoice absent, week 3—
feeLate fee, vendor paid on time45.00
taxTax on exempt category61.12
Self-service

Spend explorer

Who, what, where and how much, straight from the ledger, with drill-down to the invoice image. For VPs and department heads who will never write SQL. Every query is bounded, capped and logged with its cost.

17.7Mledger rows served
$0.00014per query
$3.25/moat 20 users
0SQL written by users
Apps ScriptBigQuerySheets export
Spend Explorer
Explore
Saved
Exports
Usage
Produce · P9 · by vendor$0.00014
VendorSpend%
Produce Co. A412,88038.4
Produce Co. B301,11528.0
Regional coop96,4029.0
Other (14)264,71124.6
Planning

What-if budget builder

Describe a scenario in plain English. A model proposes rule cards, a person approves them, and the warehouse writes a new versioned budget at location × period × account. Every version kept.

241 × 13locations × periods, complete
1.68Mscenario lines
$0.0068per scenario
<$1/momeasured
Cloud RunGeminiBigQuery engine
Scenario Builder
Scenario
Rules
Versions
Export
Grow sales 3% and add the new state minimum wage from July 1.
Proposed 2 rules. Approve to apply.
Net sales +3.0%
all concepts · P1–P13
approved
Hourly labor
min. wage · from Jul 1
pending
Operations

Field visit planner

Which restaurants a director should visit this week and why, ranked by the data. A checklist per visit. Follow-ups by voice or video that land in Tasks, Calendar and Chat. A scheduler with plain-English rules.

190+stores planned weekly
4.2 svideo to follow-up draft
0private keys stored
≈ $27/moto run
Cloud RunEvent ledgerGeminiGoogle Workspace APIs
Visit Planner
This week
Follow-ups
Schedules
Rules
Area 14 · 7 storesranked by issue
StoreWhyVisit
1064Food cost +2.4 pts, 3 wksTue
2203Schedule late 2 of 4 wksWed
3110Reviews 3.6★, was 4.2Thu
1181On planskip
M
T
W
T
F
S
S
GM
KM
AI intake

Idea-to-spec assistant

An operator describes a problem. The assistant asks three questions at most and returns a pitch card and a technical spec side by side. Hours and dollars come from your data, never from the model.

3 questionsmaximum
$0.004per message
≈ $22/moat 1,000 conversations
3 languagesand your brand themes
Cloud RunGeminiStreaming tools
Idea Intake
New idea
My ideas
Submitted
Accurate prep forecasting
Morning prep is guessed, not forecast.
30 min
per store per day
36,500 h
per year, all stores
Spec
POS → daily_totals
weather.hourly
forecast → prep sheet
GM approves
question 1 of 3
Kitchen

Speed-of-service analytics

Order and kitchen-display bump times pulled from the POS nightly, built into marts with assertions, published to dashboards by store, station, daypart and item. When the reporting vendor never built it, we go to the source.

± 1order per store-day vs the POS
P90found on the slowest item in week one
1.2Morders a year
$1.20/moto run
POS APICloud RunDataformLooker
Service Timer
Stations
Items
Stores
6.4 min
avg
11.2 min
P90
5,260
orders
StationAvgP90
Hot line7.913.1watch
Expo5.79.8ok
Cold2.34.0ok
Grill6.110.4ok
Enablement

Intranet, tickets and Workspace automation

One front door to every tool and dashboard, with an assistant that answers questions. A tracker where anyone reports a bug or files an idea. Org-chart-driven groups, a QR studio, a training-video studio. Google-native first.

200 groupssynced hourly from the org chart
412tests behind the tracker
$0/mofor the video studio
$0.0015per assistant message
Cloud RunApps ScriptCloud IdentityGemini
Tracker
Report
Ideas
Board
Plan
This week9 live tools
TicketTitleState
BUG-0031Export slow over 5k rowstriage
IDEA-0012Prep forecastingfrom intake
TSK-0088Add state tax tabledoing
BUG-0029Dark-mode contrastfixed · 5★
Also

The smaller jobs.

Migrating decades of ledger history out of a retiring system. Auditing competing fiscal calendars. Finding the four ways an export silently loses rows. Cleaning up a Looker menu with a revert for every change. Capturing a recipe library so “every dish with sesame” is a query.

ServicesStart a project
Services

Contract data and AI engineering for restaurant operators.

Senior people only. Everyone who touches your data has read a restaurant P&L, written SQL against a POS feed, and shipped software that operators used the next morning. We work in your cloud, under your groups, and leave documentation your team can operate from.

01 · Foundation

Data warehouse & integration

A BigQuery (or Snowflake / Azure) lakehouse fed by your POS, ledger, AP, payments, labor, back-of-house and guest systems, with transformation code that asserts its own correctness and a status console everyone can read.

  • One loader per source: idempotent, self-checking, alerting to Chat
  • Raw / mart / ops layering; one store key; your fiscal calendar everywhere
  • Looker, Connected Sheets, Looker Studio or Power BI on top
  • Usage census, cost per feed, release stages, group-based access
Deliverables · loaders · marts · status console · release note per source · runbooks
02 · Finance

Finance & accounting automation

Reconciliations, close readiness, journal generation, AP exception detection and self-service spend reporting, built next to your ledger and run in shadow until parity is proven.

  • POS ↔ ledger ↔ processor tie-outs by restaurant-day, to the cent
  • Daily invoice exception queue: duplicates, price outliers, missing bills, fees, tax
  • Automated daily sales journals with held-day rules
  • P&L statement lines, EBITDA and KPIs queryable for every scope
Deliverables · reconciliation views · exception portal · journal generator · close checklist
03 · Apps

Operational apps with AI inside

Small, fast web apps where the model does the parts it is good at (interviewing, drafting, summarizing, proposing rules) and the warehouse computes the numbers. Humans approve.

  • Field visit planner, scheduler, what-if budgets, idea intake, QR and video studios
  • Access by Google Groups, audit logs, fail-closed lookups
  • Light/Dark, English/Spanish/French, your brand themes, contrast gates
  • Measured per-message and per-run cost from day one
Deliverables · deployed app in your project · test gate · release note · handoff
04 · Enablement

AI & Workspace enablement

An intranet front door, guided lessons, Gemini Enterprise or Claude rollout, Workspace-native automation, and an access standard that lets you say yes to staff using AI with company data.

  • Groups-only access model: three roles, one config per app
  • Org-chart-driven Workspace groups, digests, training-video pipeline
  • Office hours and documentation your team keeps writing
Deliverables · intranet · access standard · group sync · training plan
05 · Assessment

Data & AI assessment

Two to three weeks reading your systems, job history and spreadsheets. We tie out what you have, price what you are running, and hand back a prioritized plan with the first three builds specified to the column.

  • Inventory of every pipeline, calendar and report with its readers
  • Where the numbers disagree today and why
  • Cost of the current stack and of the proposed one
Deliverables · written findings · costed roadmap · three build specs
06 · Leadership

Fractional data & AI leadership

A standing cadence of builds, a ship log your executives read, and one person accountable for the number on the P&L being right. For operators who need the function before they need the department.

  • Weekly ship log and monthly cost review
  • Vendor and platform negotiations with someone who has sat on the other side
  • Hiring and onboarding the team that takes over
Deliverables · roadmap · ship log · vendor scorecards · hiring plan
Engagement shapes

Three ways to work with us.

Fixed scope where the scope is knowable, retainer where it isn't. Every shape ends with your team running what we built.

2–3 weeks · fixed fee

Assessment

Findings, costed roadmap, and the first three builds specified. Useful before a platform decision or a budget cycle.

  • Read-only access to your systems
  • Interviews with Finance, Ops and IT
  • Written report you can hand to a board
6–12 weeks · fixed scope

Foundation build

Loaders, marts, status console and the first finance controls in your own cloud project, with a release note for each.

  • Weekly ship log
  • Your team paired from week one
  • Measured cost of the stack at handoff
Monthly · retainer

Fractional team

A standing cadence of builds and controls, prioritized with you every week, with the documentation to match.

  • Capacity reserved, not billed by the ticket
  • Monthly cost and usage review
  • Cancel with 30 days' notice
Questions we get

Before you email.

Do we have to be on Google Cloud?

No. Our deepest experience is Google Cloud end to end (BigQuery, Dataform, Cloud Run, Looker, Workspace), and that is where we are fastest. We have also built the same patterns on Azure SQL with Power BI, and can work in Snowflake. What does not change is the discipline: idempotent loaders, assertions, tie-outs, measured cost.

Who owns what you build?

You do. Everything runs in your cloud project, in your source control, under your groups. We keep no keys and no copies. The release notes are written so your analyst and your operator can run it without us.

How do you use AI in the work?

Two ways. We build with Claude and Gemini in the loop, which is why a new source takes hours instead of weeks. And we put models inside the tools where they belong: interviewing, drafting, summarizing, proposing. The warehouse computes the numbers; a person approves anything that posts.

What does a pipeline cost to run?

Usually less than a dollar a month. We publish a cost section with every release: list prices on a stated date, measured bytes and run times, per-run and per-year totals, and the ceiling a runaway could hit before the cap stops it.

Can you work with our POS or reporting vendor?

Yes, and we have, including building integrations the vendor never shipped. We have also sat on the vendor side for a decade, so we know how to get data out of a platform without starting a fight.

Start a project
About

Built by people who have closed a restaurant at midnight.

Patrick Bobrukiewicz started in restaurants at thirteen and has not left. He cooked a casual-dining line as a teenager, finished an accounting degree at eighteen, and moved into a franchisee’s corporate office after a prep tool he wrote in VBA spread from his store to the rest of the market. A decade at a restaurant data platform followed, on the vendor side of the same problems. He now leads AI strategy for a multi-brand operator and runs Byte & Bite with a senior team of data and AI engineers, accountants and operators.

The team’s edge is simple: we know what the numbers on a restaurant P&L mean, we know which POS field lies, and we have shipped the fix. We work from Kansas City and Wichita with operators between ten and three hundred units, and with the vendors who serve them.

  • 1990sFirst restaurant job at 13. Casual-dining line cook at 16.
  • Early 2000sAccounting degree at 18. Franchisee corporate office: accounting, then process automation after the VBA prep tool.
  • 2010sAbout ten years at a restaurant POS and labor data platform used across the industry.
  • 2020sData architecture and AI strategy for multi-brand operators. Byte & Bite Consulting founded.
  • 2026Forty-five documented releases in eleven weeks for one client on Google Cloud.

Links

Speaking, 2026

  • “Avoiding the Tokenomics Trap,” CapitalG’s AI in the Enterprise CXO summit, San Francisco
  • Restaurant data platform user group: what six months of AI builds look like inside an operator
  • Google influencer summit, New York
Data engineeringLoaders, Dataform marts, assertions, cost control. BigQuery, Snowflake, Azure SQL.
Accounting & APReconciliations, close, journal automation and AP controls from people who have run an AP team.
Product & appsCloud Run apps with Gemini or Claude inside; release gates for themes, languages and contrast.
OperationsFormer GMs and directors who know what a visit checklist and a schedule rule should actually say.
Contact

Send us the spreadsheet you don’t trust.

The best first conversation starts with a real problem: a reconciliation that never ties, a vendor feed nobody has loaded, a report that takes a week. Tell us about it and we will come back with what we would check first and what it would cost.

patrick@byte-and-bite.com Email Patrick
Based
Kansas City & Wichita, Kansas · working across the US
Clouds
Google Cloud first; Azure and Snowflake on request
Systems
Rosnet, PAR Brink, Toast, Olo, Business Central, Dynamics GP, Ottimate, Fiserv, Fourth / MacromatiX, HotSchedules, ADP, meez, Looker, AppSheet, Google Workspace
Models
Gemini on Vertex AI, Claude, open models on GPU when the job needs one
Typical client
Multi-brand restaurant operators, 10–300 units, and the vendors who serve them
Response
Within one business day
LinkedIn
Patrick Bobrukiewicz