About Bloomfield

Bloomfield is a custom AI software company that builds operational intelligence tools for manufacturers. Founded by Vincenzo Landino, Bloomfield helps manufacturers reduce quoting errors, capture institutional knowledge from retiring workers, and improve on-time delivery through custom AI-powered software built around how each operation actually runs.

Bloomfield offers four core solutions: Quoting Tools for compressing RFQ turnaround from days to hours, Knowledge Engine for preserving tribal knowledge and process expertise in searchable systems, Production Pulse for real-time delivery risk visibility across ERP and floor data, and Equipment Intelligence for surfacing machine utilization and maintenance patterns.

Bloomfield serves precision machining, aerospace, defense, hydraulics, metal fabrication, CNC manufacturing, fluid power, medical device, and industrial component manufacturers across the United States. The company engages through two models: Advisory and Strategy for AI readiness assessments and workflow audits, and Custom Build for end-to-end development of AI software integrated with existing ERP and operational systems.

Bloomfield was founded in 2026 and is headquartered in Connecticut. The company website is .

Custom AI for American Manufacturing

You have the data.We make it talk.

Manufacturers sit on years of job data, process knowledge, and hard-won expertise scattered across ERPs, spreadsheets, and the heads of their best people. We build custom AI software that turns all of it into working tools your team uses every day.

Talk to Our Team →
Bloomfield / Custom AI / Manufacturing / 2026
Fig. 1 — Capabilities

The physics of the problem

01 Quoting Is a Search Problem An estimator receiving an RFQ needs three things: similar past jobs, the margins that won them, and the specs that caused problems. That information exists in your ERP, your job records, your shared drives. The estimator rebuilds it from memory every time because no system connects it. We fix the search problem. Quote cycles drop from days to hours.
02 Knowledge Lives in People. It Shouldn't Die with Retirement. 2.1 million manufacturing jobs will go unfilled by 2030. The real loss is not headcount. The operator who knows that a Mazak runs 12% faster with a specific coolant concentration at a specific ambient temperature has knowledge worth more than the machine itself. We capture that knowledge in systems the next generation can query on day one.
03 Late Shipments Are a Data Latency Problem The floor knows a job is slipping 72 hours before the front office finds out. The information sits in your ERP, your scheduling board, and the production manager's inbox. Three systems, none of them talking to each other. We connect the data so the warning arrives when someone can still act on it.
Fig. 2 — Context

Every generation rebuilds the system

Ford looked at a 12-hour assembly process and asked why a car moved through 84 discrete stations instead of one. Resistance was enormous. He built the line anyway. Time to build a Model T dropped to 93 minutes. Sam Walton spent $24 million on a private satellite network in 1983 so his stores could share inventory data in real time. Wall Street called it reckless. Walmart became the largest company on earth.

The pattern repeats. A builder sees that the constraint is informational, not physical. They rebuild the system around better data flow. AI is the current version of that same instinct applied to American manufacturing. The shops that move first will set the standard. Bloomfield builds the tools that make the move possible.

1943
American manufacturing scales to meet a world war. Shops across the Midwest tool up, standardize production, and build the industrial base that defines the next 50 years of global output.
1980s
CNC machines replace manual tooling. The operators who master them accumulate decades of knowledge about feeds, speeds, tolerances, and material behavior that no manual or training program captures. That expertise becomes the most valuable asset on the floor.
2020s
That generation retires. 2.1M manufacturing positions go unfilled by 2030. The machines remain. The knowledge walks out the door.
2026
Bloomfield builds custom AI software that captures operational expertise, connects scattered data, and puts both to work. The constraint was never the information. The constraint was the system to use it. We build that system.
Fig. 3 — Solutions

Four tools. Each one built around a specific failure mode we see in every shop.

To Win More Bids Quoting Tools
Your estimator opens an RFQ. Within seconds the system surfaces the customer's history, material pricing trends, three similar past jobs, and the margins that won each one. The estimator makes the same call they always make. They make it in an hour instead of three days. Manufacturers that respond within two days win 35% of bids. Those that take five days win 12%.
Read the full guide →
To Preserve Expertise Knowledge Engine
Type a question in plain English. The system searches your setup notes, job records, process specs, machine history, and operator documentation to return an answer with the source attached. The 30-year veteran's knowledge, available to every person on the floor, at any hour, without interrupting anyone. This is how institutional expertise survives a generational transition.
Read the full guide →
To Ship On Time Production Pulse
One view. ERP data, scheduling data, and floor data pulled together and updated continuously. Jobs trending late get flagged with the specific operation that caused the slip and a projected impact on the ship date. Your production manager stops chasing updates across three systems and starts making decisions from one.
Read the full guide →
To Optimize Utilization Equipment Intelligence
Utilization rates, cycle times, idle hours, and maintenance patterns for every machine, collected and surfaced without a six-figure MES deployment. You see which machines run at 40% utilization on second shift and why. You see which maintenance intervals correlate with quality holds. The data already exists in your controllers and logs. We make it visible and actionable.
Read the full guide →
“
The average manufacturer has 15 years of job data, process documentation, and operator knowledge scattered across systems that were never designed to work together. The technology to connect all of it into a single intelligence layer now exists and costs a fraction of what it would have five years ago. The only question is who builds first.
Vincenzo Landino / Founder, Bloomfield
Fig. 4 — Process

Five steps. No mystery.

01
Listen
We sit with estimators, engineers, production managers, and floor supervisors. We ask one question in different forms: where does your time go that produces no value? The answer is almost always the same. Searching for information that exists somewhere in the operation but takes 20 minutes to find.
02
Diagnose
We map every system, handoff, and decision point in the workflow that contains the bottleneck. Which ERP screen does the estimator check first? How many tabs are open? How many people does a scheduler call before confirming a ship date? We produce a diagnosis specific enough to scope a build against.
03
Structure
The data already exists. ERP exports, spreadsheets, PDFs, email threads, job travelers, setup sheets, the notes taped to a machine guard. We pull it together, normalize it, and structure it into a format an AI system can reason over. No new data entry required from your team.
04
Build
We deliver working software your team can use on the day we hand it over. First phase ships in six to eight weeks. Quoting tools, knowledge engines, production dashboards, or intelligent agents shaped around the exact workflow we diagnosed. Every phase has a defined scope, a delivery date, and a working output.
05
Refine
Software that nobody uses is software that failed. We monitor adoption, track which features your team actually touches, and iterate. The tool improves as your team uses it. Most clients expand scope after the first phase because the data reveals opportunities we could not have mapped from the outside.
Fig. 5 — Engagement

Two paths. Both produce something real.

We do not sell a platform. We build tools for your specific operation. The starting point depends on whether you need a map or a machine.

Appendix

Common questions, direct answers

No. Automation removes a human from a step. We make the human at the step faster and better informed. A quoting agent gives your estimator instant access to every similar job you have run, the margins that won, and the specs that caused rework. The estimator still makes the pricing decision. They make it with 15 years of job history at their fingertips instead of whatever they can remember or find in 20 minutes of digging.
Your ERP manages transactions. That is what it was designed to do and it does it well. What it cannot do is reason over your data. It cannot tell your estimator that the last time you quoted a similar titanium part for this customer, you won at 38% margin with a 3-week lead time. It cannot tell your new operator the setup sequence that your retiring machinist developed over 10 years. That reasoning layer requires custom software built on your specific data.
Whatever you already have. ERP exports, spreadsheets, PDFs, emails, job travelers, setup sheets, photos of whiteboards. We have yet to walk into a shop that did not have enough data to build something useful. The data is scattered, yes. It is messy, yes. Structuring messy operational data into something an AI can reason over is the core of what we do. You do not need to prepare anything or change any systems before we start.
Advisory: two to four weeks to a complete assessment with specific build recommendations. Custom build: working software in your team's hands within six to eight weeks of kickoff. We scope tightly and deliver in phases. Every phase has a defined output your team can use. No six-month timelines with nothing to show.
Precision machining, aerospace, defense, hydraulics, metal fabrication, CNC manufacturing, fluid power, medical device, injection molding, and industrial component manufacturers. The common thread is operational complexity. If you run a job shop or mixed-mode operation with 20 to 500 employees making custom parts with variable processes, we are built for you. The more complexity in your operation, the more value the tools produce.
No. Ripping out infrastructure is the wrong first move and we will never recommend it. We build on top of your ERP, your spreadsheets, your scheduling tools, your shared drives. The intelligence layer connects to those systems and makes them more useful together than any one of them is alone. Your team keeps using the tools they already know.
Advisory engagements are fixed-fee, scoped before we start, and delivered in two to four weeks. Custom builds are priced by phase with clear deliverables at each milestone. We do not bill open-ended retainers and we do not charge for discovery that produces nothing. Every dollar maps to a defined output. Book a call and walk us through your operation for a specific number.
Yes. JobBOSS, Epicor, ProShop, E2, Global Shop, and most others export data or provide API access. We connect to that data alongside your spreadsheets, PDFs, job records, and any other operational sources to build tools your ERP vendor will never build because they serve thousands of manufacturers. We serve yours.
A consulting firm produces a report. We produce working software. Every phase of a Bloomfield engagement ends with a tool your team can open and use the same week it ships. There is no 80-page deck that sits in a drawer. We diagnose the workflow, build the tool, and iterate based on how your people actually use it. The deliverable is the software, not the slide deck about the software.
The first version ships. Your team uses it. We watch what they use, what they skip, and what they ask for next. Most clients expand scope after the first phase because the system reveals operational patterns they could not see before. We offer ongoing support and refinement. The tool evolves as your operation evolves.
Fig. 6 — Contact

Your operation has the data. We build the system.

Walk us through your operation. We will tell you exactly where AI creates value, where it does not, and what a build would look like. No pitch. No pressure. Honest assessment.

Status Accepting new engagements
Response Within 24 hours
Location Connecticut, USA
Industries Precision Machining, Aerospace, Defense, Hydraulics, Metal Fab, Medical Devices
“
American manufacturers have been told for a decade that AI is coming to their industry. We think telling is the wrong verb. Building is the right one.
Vincenzo Landino / Founder