Visual quality measures
Assesses visible crust colour, shape and crumb structure from standardised photographs.
Crust colourL* 52 → 58 (paler)
Process intelligence for commercial baking
Understand what changed between baking batches.
Batchbloom helps commercial bakeries understand inconsistent production results. AI connects product images, ingredient records, and process measurements to highlight quality changes and support better-informed investigations.
- Images, ingredient lots and process records together
- Batches compared with genuinely similar ones
- Associations shown as leads, never as proven causes
Deviation queue Sample data
BCH-2201Reduced loaf height — 4 batches
Quality deviation
BCH-2202Paler crust colour
Visual deviation
BCH-2203Proofing profile anomaly
Process anomaly
Flags show associations, not causes. Technologists decide what to investigate.
Understand what changed between baking batches.AI · Food Technology · Manufacturing Analytics
Why Batchbloom exists
Why Batchbloom exists
Bakers know the same recipe never bakes quite the same twice. The difference hides in flour, temperature, time and heat. We want every bakery to see what changed, so good bread becomes reliable bread.
Our purpose
Help commercial bakeries understand inconsistent results and investigate them with evidence.
Why it matters
Inconsistent batches mean downgraded product, rework, waste and unhappy customers. The data to explain them often exists but sits in different places.
The stakes
Why consistency matters in food manufacturing.
1.7Mt
Estimated food waste from UK food and drink manufacturing each year.
Source: WRAP, cited in Sustainable Energy & Fuels review51%
Share of that manufacturing food waste estimated to be avoidable.
Source: WRAP, cited in Sustainable Energy & Fuels review50k t
Bakery materials in Australian manufacturing wasted or diverted to animal feed each year.
Source: Bread and Bakery Sector Action Plan (NSW)Figures are published estimates from the sources linked; they describe the wider problem, not results from this product.
What changes
From guesswork to focused investigations.
Today
- Quality problems noticed at the end of the line
- Records split across sheets, logs and notes
- Every possible cause discussed at once
- Trials run without recording what was learned
With Batchbloom
- Deviations flagged against similar batches
- Images, lots and settings linked per batch
- A short list of associated variables to check
- Trial results captured as new evidence
The problem
Same recipe. Different bread. Nobody can say why.
Flour characteristics, ingredient temperatures, mixing, proofing and oven behaviour all shift from batch to batch. When loaves come out smaller, paler or with an uneven crumb, the records that might explain it are spread across production sheets, equipment logs and quality notes.
- 01Loaf height drops for a week, then recovers
- 02Crust colour drifts between shifts
- 03A new flour lot arrives with no one comparing results
- 04Quality notes on paper, process data in the oven log
The solution
Connect what you measured to what you made.
Batchbloom links standardised product images and quality results to the ingredient lots, recipe versions and process settings behind each batch, compares like with like, and highlights the variables most associated with a quality change.
Process anomaly detection
Flags unusual mixing, proofing and oven profiles compared with relevant historical batches.
Proof time+11 min vs similar
Contributing factors
Highlights variables associated with a change, clearly labelled as association, and suggests controlled trials.
AssociatedFlour lot · proof profile
How it works
From a quality complaint to a focused investigation.
- 01
Record
Log batches, ingredient lots, recipe versions and process settings, by import or entry.
- 02
Capture
Operators add standardised product images and measured quality results.
- 03
Compare
Batchbloom compares batches made under sufficiently similar conditions and flags deviations.
- 04
Investigate
Technologists review suggested investigations or trials; confirmed results feed back as new data.
Batchbloom supports decisions. It does not control equipment or change process settings.
Who it’s for
For bakeries producing at scale.
Industrial bread manufacturers
Track consistency across lines, shifts and flour deliveries.
Faster root-cause workRegional bakery chains
Keep central production consistent for every store.
More consistent loavesFrozen-dough manufacturers
Connect dough processing with bake-off results downstream.
Fewer quality surprisesContract bakeries
Show brand clients evidence-based quality investigations.
Evidence clients trustNamed organisation types are prospective customers. No affiliation or endorsement is implied.
Honest limits
What photographs and models can, and cannot, tell you.
Batchbloom is designed around the evidence a bakery actually has. Some quality questions need instruments, tasting panels or laboratory tests, and we say so.
- Reliable size measurement needs a calibrated camera or reference scale
- Texture, taste, moisture and food safety cannot be established from ordinary photographs
- Quality predictions need enough labelled data before they are trusted
- An association is a lead to investigate, not a proven cause
AI, used carefully
The model spots the pattern. Technologists establish the cause.
A change in loaf height can have several causes at once. Batchbloom narrows the search; people confirm what is really going on.
What the AI does
- Measures visible colour, shape and crumb from standardised images
- Flags unusual mixing, proofing and oven profiles
- Highlights variables associated with a quality change
- Suggests controlled trials within approved process ranges
What it never does
- Declares a confirmed root cause
- Changes recipes, setpoints or equipment
- Makes food-safety or batch-release decisions
- Claims taste or texture from a photograph
People decide
Every batch deviation is confirmed, dismissed or escalated by bakery technologists and quality managers, never by the model.
Evidence first
Each flag links to the batch records, ingredient lots, process settings, product images and quality results behind it, so a reviewer can check the reasoning before acting.
Confidence shown
Scores come with a confidence level and the data gaps that lower it, so weak signals are not mistaken for facts.
Role-based access
Permissions are set per role: who can view, comment, assign, decide, escalate or export.
Complete audit trail
Assignments, notes and decisions are time-stamped with the person and reason, and can be exported.
Your data stays yours
Customer data is isolated per tenant, encrypted at rest and in transit, and never used to train models for other customers.
What we believe
What we believe about bakery data.
- 01
Association is not cause
We say “associated with” until a trial says more.
- 02
Compare like with like
Batches are compared only with genuinely similar ones.
- 03
Technologists decide
Investigations and trials are chosen by people.
- 04
Know what photos can’t show
Taste, texture and safety need other evidence.
- 05
Start narrow
One product family, one line, done properly.
- 06
Every trial teaches
Confirmed results improve the next comparison.
A note from the founders
Why we are building Batchbloom.
Bukola has stood at the end of a bread line looking at loaves that were fine yesterday and smaller today, with a production sheet in one hand and an oven log on another screen.
Nnamdi has worked on food manufacturing lines and knows how much useful data never gets connected. Rahmat builds vision systems for inspection and is careful about what a camera can and cannot measure.
Batchbloom brings those records together and shows bakers what changed, while keeping causes, trials and decisions with the technologists who understand the bread.
Bukola Fashanu, Founder/CEO · Nnamdi Okafor, Co-founder · Rahmat Yusuf, CTO
Where we’re going
Where Batchbloom is going.
Decision support first, validated step by step.
Now
Concept and prototype
An interactive prototype on fictional batch data, and conversations with bakeries.
Next
One product, one line
Batch import, standardised images, visual measures, comparisons and anomaly flags for pan bread.
Then
Quality prediction
Predictions of selected outcomes once enough labelled data exists.
Later
Equipment integration
Live readings from mixers, provers and ovens, with any control far behind validation.
Pricing
Simple plans that grow with you.
Subscription per production site or line, with onboarding charges for data integration and camera calibration where needed.
Line
For one product family on one line.
$1,200/ line / month
- Batch data import
- Standardised image capture
- Visual quality measures
- Anomaly flags and notes
Site
For a production site with several lines.
$3,400/ site / month
- Up to 4 lines
- Everything in Line
- Contributing-factor analysis
- Trial tracking
- Unlimited users
Group
For multi-site manufacturers.
Custom
- Multiple sites
- Equipment data integration
- Camera calibration support
- Cross-site reporting
Illustrative pricing for a proposed product. Final pricing is agreed with early-access customers and excludes taxes.
Proposed production design
Architecture on Amazon Web Services
How Batchbloom could run in production, starting with batch records and images and adding equipment readings later.
- 01
Store
Amazon S3Product images and historical batch data.
- 02
Connect
AWS IoT CoreFuture equipment readings from mixers, provers and ovens.
- 03
Model
Amazon SageMakerVision models for visible quality and process anomaly models.
- 04
Record
Amazon RDS for PostgreSQLBatches, ingredient lots, recipe versions, results and investigation notes.
- 05
Summarise
Amazon BedrockInvestigation summaries grounded in measured results.
- 06
Govern
AWS CloudTrail · AWS KMSAccess logging and encryption per customer.
Proposed, not deployed. No AWS services are connected to this website or the prototype, which run in your browser on fictional data.
The team
The people building Batchbloom
A proposed founding team for this concept.
Founder/CEO
Bukola Fashanu
Bakery technologist with experience in industrial bread production and quality.
Co-founder
Nnamdi Okafor
Process engineer focused on food manufacturing lines and data.
CTO
Rahmat Yusuf
Computer vision and ML engineer working on industrial inspection.
Do we need special cameras?
Standardised images are needed for consistent colour and shape measures. Reliable size measurement needs a calibrated setup or reference scale, which can be part of onboarding.
Will it tell us the cause of a problem?
It highlights variables associated with a change and suggests what to investigate or trial. Technologists confirm causes.
Can it control our ovens?
No. Batchbloom starts as decision support. Automatic process control would need much more validation and equipment integration.
Is Batchbloom a live product?
Not yet. Batchbloom is a proposed startup concept. The team is fictional, pricing is illustrative and the prototype runs on fictional data in your browser.
Does the AI make decisions?
No. Batchbloom flags, ranks and explains. Bakery technologists and quality managers review the evidence and record every decision with a reason.
How would our data be protected?
In production, data would be encrypted, isolated per customer, access-controlled by role and never used to train models for other customers. Data-sharing terms are agreed before any pilot.
Early access
Pilot Batchbloom with your own team.
We are looking for a small number of early partners to shape Batchbloom. A pilot starts with a sample of your historical data and a fixed review period, so you can judge the results before committing.
- Tell us about your team and the records you work with.
- A founder replies personally to arrange a short call.
- We agree a pilot scope, data-sharing terms and how success will be measured.