TL;DR: Quote-to-fabrication automation means an AI agent does the reading and reconciling between an RFQ email and a released job: it pulls the drawing and STEP file out of the inbox, drafts the routing and BOM from your own past jobs, prices material and outside processing, writes the quote, chases it, matches the PO, and builds the job and traveler in your ERP. Your estimator still approves every price. The shops that get real value start by fixing the data underneath (part numbers, revisions and actual hours that agree across JobBOSS, E2, ProShop, Epicor or Global Shop) and by naming one countable unit, such as minutes per quote or quoted vs actual variance, before they automate anything. Buyers already expect a quote inside a day, so the speed matters, but the money is in closing the loop between what you quoted and what the job actually cost.

It is 7:40 on a Tuesday morning and your lead estimator has 23 unread RFQs. One is a 6061-T6 bracket with a STEP file, a PDF at Rev C, Type II anodize, quantities of 25, 100 and 250, and a buyer who wants an answer by Thursday. Another is a weldment from a customer you have quoted forty times, but the drawing says Rev F and your last quote was Rev D. A third has no drawing at all, only "same as last time, qty 500." Before she prices a single part, she will spend the first two hours of her day opening attachments, renaming files, looking up old jobs in JobBOSS, and emailing the anodizer for a lot charge.

Meanwhile, the buyer on the other end is not waiting. In Paperless Parts' 2020 Part Buyer Survey, 67% of buyers said they expect a quote in less than 24 hours, up from 53% in 2019. Most custom shops win only a fraction of what they bid (Paperless Parts puts the average at about 30%), so every hour spent re-typing a drawing into a spreadsheet is an hour spent on work you will mostly lose.

This guide is for owners, general managers, estimators and VPs of operations at job shops, metal fabricators, machine shops and custom manufacturers. It walks the whole path from RFQ email to released job and back again: what can be automated today, what should stay with a person, what the data layer underneath has to look like, and how to measure whether any of it paid off.

Key takeaways

• Buyers expect a quote within a day. Paperless Parts' 2020 Part Buyer Survey found 67% of buyers expect a quote in under 24 hours, up from 53% the year before.

• Most quotes do not turn into revenue. Paperless Parts' 2022 quoting guide puts the average job shop win rate at about 30%, which is why the cost of each quote matters so much.

• Disciplined quoting separates the best shops. Modern Machine Shop's Top Shops benchmarking reported a median quote-to-book ratio of 70% for Top Shops against 51% for other surveyed shops.

• You will not hire your way out of the estimating bottleneck. Deloitte and the Manufacturing Institute estimate that 1.9 million of the 3.8 million manufacturing jobs needed by 2033 could go unfilled, and 65% of manufacturers called attracting and retaining talent their primary challenge.

• Most AI projects stall before they hit the P&L. MIT NANDA's 2025 "GenAI Divide" report found about 95% of organizations saw no measurable profit or loss impact from generative AI, mostly because tools did not fit real workflows.

• The quote is not the finish line. The biggest margin leak in most shops is the gap between quoted hours and actual hours, and almost no quoting tool closes that loop by itself.

What is quote-to-fabrication automation?

Quote-to-fabrication automation is the use of software, increasingly AI agents, to carry an inbound request for quote all the way to a released, costed job on the shop floor, and then compare that job's actual cost back to the quote. It covers more ground than "quoting software," which usually stops when the PDF goes out the door.

In a typical custom manufacturer the path looks like this:

1. RFQ intake: an email (or portal upload, or EDI release) arrives with drawings, models, specs and quantities.

2. Drawing and model review: someone reads the PDF and STEP or IGES file for material, tolerances, finish, certifications, weld content and anything unusual.

3. BOM and routing estimate: operations, work centers, setup and run times, purchased components, hardware.

4. Material and outside processing pricing: bar, plate or sheet cost with drop and scrap, plus anodize, plating, heat treat, passivation, powder coat or NDT from outside vendors.

5. Quote generation and follow-up: price breaks, lead times, exceptions, terms, and a reminder when the buyer goes quiet.

6. Quote-to-order conversion: matching the incoming PO to the quote, checking revision, price and quantity, and catching anything that changed.

7. Job and traveler creation: building the job, routing and material requirements in JobBOSS, E2, ProShop, Epicor, Global Shop, Fulcrum or whatever runs your floor.

8. Close-out: comparing quoted vs actual hours, material and outside processing cost once the job ships, and feeding that back into the next quote.

Each of those steps lives in a different place today. The email is in Outlook. The model is in SolidWorks or a viewer. The old job is in the ERP. The anodizer's price is in someone's inbox from March. The actual hours are in the shop floor data collection module, if anyone clocked in correctly. That fragmentation is the real problem, and it is why automating one step rarely changes the numbers.

How is this different from CPQ for manufacturing?

CPQ (configure, price, quote) tools were built for configurable products: you pick options from a known catalog, rules check compatibility, and a price falls out. That works well for a company that sells 40 models of pump with optional seals. It works poorly for a job shop, where nearly every RFQ is a new part number, the "configuration" is a drawing, and the price depends on how your specific machines and people will actually make it. If you build to print, you need estimating, not configuration.

How is it different from instant quoting?

Instant quote portals price simple geometry automatically from a CAD upload. They are useful for prototype and low-complexity work. Most mid-market shops, though, win on the jobs that do not fit an instant quote: tight GD&T, weldments, assemblies, mixed processes, customer-specific quality requirements. Quote-to-fabrication automation is aimed at that harder work, where a person still decides, but the reading, looking up and re-typing is done for them.

Where does the time actually go in a job shop quote?

Most of the time in a quote goes to gathering and reconciling information, not to pricing judgment. Stopwatch a handful of quotes and the pattern is usually the same: the decision about what a part should cost takes minutes, and the hunting around it takes the rest.

Practitioners describe the same thing. In a long-running eMastercam forum thread on quoting process, programmers describe scanning first for material and tolerances, then flagging special requirements like EDM, heat treat, coatings and custom tooling, then digging up comparable historical jobs. Several mention spending "an hour or so" building a quick toolpath just to avoid missing an undercut. Paperless Parts' quoting guide makes a related point: shops often spend hours building out a quote before realizing they are missing information needed to finish it.

If you list the tasks honestly, they fall into three buckets:

• Clerical: saving attachments, creating the RFQ record, typing customer and part data, renaming files, sending the quote, logging the follow-up.

• Reconciliation: finding the last time you made this part (or one like it), checking the revision, finding what it actually took on the floor, getting a current material price, getting an outside processing quote.

• Judgment: deciding whether to bid at all, how to fixture it, what margin this customer and this load can bear, and what to say no to.

The first two buckets are where an agent belongs. The third is why you hired your estimator. The goal is to give her back the hours in buckets one and two so she spends her day in bucket three.

How do you automate RFQ intake from email?

You automate RFQ intake by having an agent watch the quoting inbox, classify each message, pull out the attachments and the request details, and create a structured RFQ record that links to the right customer and part history. Done well, the estimator opens a queue of ready-to-review RFQs instead of an inbox.

What a good intake step does with each email:

• Classifies it: new RFQ, re-quote, revision change, PO, expedite request, question about an existing order, or noise.

• Extracts the request: customer, buyer contact, part numbers, revisions, quantities and price breaks, requested delivery, quality clauses, and any attached terms.

• Unpacks the files: PDFs, STEP, IGES, DXF, Parasolid, zipped packages, and spec sheets, filed against the RFQ.

• Matches history: "this customer, this part number, last quoted at Rev D in 2024, made twice, last job ran 18% over on the mill."

• Flags gaps immediately: missing drawing, model and print disagree, revision not stated, quantity unclear. A short clarification email goes out as a draft within minutes instead of on day three.

The "same as last time, qty 500" email is a good test. A tool that only reads the email cannot answer it. An agent that can see your quote history, your job history and the customer's PO history can draft the re-quote, note that material went up since last time, and flag that the last run had a scrap problem on op 30.

Can AI read drawings, PDFs and STEP files well enough to quote?

Yes for the extraction, not yet for the final call. Current models can reliably pull material, finish, title block data, general tolerances, notes and certification callouts from a clean PDF drawing, and geometry tools can measure a STEP file's envelope, volume, hole counts and features. What still needs a person is interpretation: whether a tolerance stack is achievable on your equipment, whether a note means what it appears to mean, and whether the model and the print actually agree.

A practical way to think about it:

• Reliable to automate: title block (part number, revision, material, finish), bill of materials on assembly drawings, general tolerance block, explicit spec callouts (for example MIL-A-8625 Type II, AMS 2700, AWS D1.1), envelope size and stock size from the model, hole and thread counts, bend counts on sheet metal.

• Automate with review: tight GD&T callouts, surface finish requirements, weld symbols and weld length, flag notes buried in the margin, hardware callouts.

• Keep with a person: manufacturability calls, fixturing strategy, whether a feature is a trap, and anything where the print and model disagree.

Our position is that the drawing read should come back as a checklist the estimator confirms, with every extracted value linked to where it came from on the print. If an agent cannot show you which note produced "passivate per ASTM A967," you should not trust the number it produced from it.

How do you estimate routing, material and outside processing without guessing?

You estimate them from your own history first and from standards second. The most accurate predictor of what a part will take on your floor is what similar parts actually took on your floor, by work center, adjusted for quantity. Most shops have years of that data sitting in the ERP and almost never use it at quote time because it is too slow to find.

Routing and hours

Here is the arguable part: most shops should stop quoting from their published standards. Standard setup and run rates in the ERP were set once, often years ago, and drift every time you buy a machine, lose a setup person or change a fixture. CAM cycle time estimates are better than guessing but routinely miss tool changes, probing, deburr and handling; machinists on forums like Autodesk's Fusion community are blunt about how far off they can run. Your clocked actuals from JobBOSS, E2 or ProShop include all of that.

An agent can find the five most similar past jobs (by material, envelope, feature count, tolerance class and process), show their quoted and actual hours by operation, and propose a routing with setup and run times based on what actually happened. The estimator sees the evidence, not just a number. On a part with no close match, the agent says so plainly and falls back to standards, flagged as lower confidence.

Material

Material pricing should come from current supplier quotes and recent purchase orders, not last year's price list. A good material step calculates stock size with saw kerf and facing allowance, nests sheet parts against your standard sheet sizes, applies your drop and remnant rules, and checks whether you already have usable stock on the shelf. When a price is older than your threshold (30 days for aluminum and stainless is common in volatile markets), it drafts a request to your metal service center rather than silently using an old number.

Outside processing

Outside processing is where quotes quietly lose money. Anodize, plating, heat treat, passivation, powder coat, grinding and NDT vendors have lot charges, minimums, rack constraints and lead times that change, and estimators often carry them in their heads. An agent can pull the last price and lead time by vendor and process from your POs and vendor invoices, apply the lot charge correctly across price breaks, and flag when the requested delivery date does not leave room for the vendor's actual turnaround. That last check alone prevents a lot of late jobs that were really quoting errors.

How does a won quote become a job and traveler in the ERP?

A won quote becomes a job when someone matches the customer's PO to the quote, confirms the details, and builds the order, job, routing and material requirements in the ERP. Automation here is mostly about catching mismatches and removing re-keying.

The steps an agent can do as a draft for approval:

1. Match the PO to the quote: customer, part number, revision, quantity, unit price, delivery date and terms.

2. Flag every difference: PO at Rev F against a quote at Rev E, quantity of 300 against price breaks at 250 and 500, a delivery date shorter than quoted lead time, a quality clause that was not in the RFQ.

3. Build the sales order and job: carry the quoted routing, operations, work centers, materials, outside processing steps and notes into the ERP job, instead of rebuilding them.

4. Generate the traveler: with the right drawing revision attached, inspection requirements, and any customer-specific notes.

5. Create purchasing requirements: material and outside processing POs drafted for the buyer to release.

Dedicated quoting platforms already handle part of this. Paperless Parts, for example, lists integrations with ECI's JobBOSS, JobBOSS², E2 Shop and M1, Epicor (10, 11 and Kinetic), Global Shop Solutions, ProShop, Fulcrum, Infor SyteLine and VISUAL, Plex and others. If you already use one of those quoting tools, keep it. The question is what happens around it: the email before the quote exists, the PO that does not match, and the actual cost after the job ships.

Quoted vs actual: how do you close the loop?

You close the loop by comparing each completed job's actual hours, material and outside processing cost against what was quoted, by operation, and feeding the differences back into the next quote for similar parts. Most shops know they should do this. Very few do it consistently, because it requires pulling data from several screens and nobody owns it during a busy quarter.

A useful close-out review answers four questions per job:

• Where did hours go over or under? By operation and work center, not just in total. A job that came in on budget overall can still hide a mill that ran 40% over and a deburr step that was never quoted.

• Was the difference a quoting problem or a floor problem? Scrap, rework, a wrong first article, or a machine down are floor issues. A consistently long setup on a certain fixture is a quoting issue.

• Did material and outside processing match? Actual vendor invoices against quoted amounts, including lot charges and expedite fees.

• What should change next time? A specific note attached to the part, the customer or the part family, so the next quote reflects it.

Across many jobs, the pattern is more valuable than any single job. If every 17-4 PH part with a heat treat step runs 25% over on finishing, that is a rule your estimator should see the next time she quotes 17-4. This is the step that turns a quoting tool into an estimating system that gets better every month, and it is the step we would build first if a shop could only pick one.

What has to be true about your data first?

Before an agent can quote from history, it has to be able to find the history and trust it. In most shops it cannot, because the same part, customer and job are recorded differently in each system.

Common examples in mid-market manufacturers:

• The customer's part number "BRK-2210" appears as "BRK2210," "2210 BRACKET" and "BRK-2210-C" across the quote log, the ERP and the shop's file server.

• Revision letters live in the drawing title block, in a free-text field in the ERP, and in the file name, and they do not always agree.

• The customer "Acme Industrial Holdings" is three different customer IDs because it was set up by three different people over ten years.

• Actual hours are clocked to the job but not to the operation, or are clocked to the wrong job when someone forgets to scan out.

• Outside processing costs sit on vendor invoices in accounting, not on the job.

The fix is a connected data layer that sits on top of the systems you already run and resolves those differences: this part number, in these five places, is the same part; this customer, under three IDs, is one customer; this invoice belongs to this operation on this job. Technically, that is entity resolution plus a knowledge graph, a map of how parts, revisions, customers, jobs, operations, work centers, vendors and costs relate to each other. In plain terms, it is the "what did we do last time and what did it really cost" memory your best estimator carries in her head, written down where software can use it. We go deeper on this in AI agents and knowledge graphs in manufacturing, and it is the job of the company brain layer.

Nothing has to be migrated for this to work. Your ERP stays your system of record. The data layer reads from it, from your quoting tool, from email and from accounting, and keeps the connections current. That matters for adoption: estimators keep working in the screens they know.

What should you not automate in quoting?

Do not automate the bid or no-bid decision, final pricing, or anything that commits your shop to a customer without a person signing off. Those are judgment calls that depend on your load, your relationships and your strategy, and they are exactly where a veteran estimator earns her salary.

Keep a person in the loop on:

• Whether to quote at all. Paperless Parts' guide makes the point that treating every quote the same is a major reason shops lose about 70% of what they bid. Deciding which RFQs deserve a full effort is strategy.

• Margin and price. The agent can show cost, history and what this customer has accepted before. The price is yours.

• Manufacturability calls and exceptions. Proposing a tolerance change, a material substitution or a different finish to the customer.

• Anything regulated or contractual. ITAR-controlled data handling, first article requirements under AS9102, customer quality clauses and terms and conditions.

• Releasing the job. The agent drafts the job and traveler. A person releases them to the floor.

There is also a reason to keep the human visible that has nothing to do with accuracy: customers buy from people. The buyer who has worked with your estimator for eight years wants her name on the quote, and she should be the one who calls when something on the print looks wrong. The agent makes that call faster to prepare. It does not replace it.

How do you measure ROI on quoting automation?

Pick one countable unit before you start, measure it for four weeks on the current process, and measure it again after. Without a baseline, every ROI story becomes an opinion. The candidates that matter most in a job shop:

• Estimator minutes per quote, split by simple, standard and complex.

• RFQ turnaround time, from email received to quote sent, in business hours.

• Quote hit rate (won quotes over sent quotes), by customer and part family.

• Quote-to-order cycle time, from PO received to job released.

• Quoted vs actual variance, as a percentage of quoted cost, per completed job.

A worked example (hypothetical numbers)

Take a fictional 90-person precision machining and fabrication shop with two estimators. These figures are illustrative, not drawn from any customer.

• Volume: 60 RFQs a week, about 3,000 a year.

• Baseline effort: 75 estimator minutes per quote on average, or 75 hours a week across two people who are also answering the phone and walking the floor.

• Baseline turnaround: three business days. Baseline hit rate: 25%. Average quote value: $8,000.

• Baseline leak: 10% of won jobs run over quote by an average of $1,200 in cost.

Now assume intake, drawing read, history lookup and outside processing pricing are drafted by an agent, and the estimator reviews and prices:

1. Time: minutes per quote drop from 75 to 35. That is 40 minutes times 60 quotes, or 40 hours a week returned to the estimating team, roughly one full person.

2. Speed and hit rate: turnaround moves to same or next day. If hit rate rises from 25% to 28%, that is 90 more won jobs a year, about $720,000 in revenue. At a 25% contribution margin, roughly $180,000.

3. Margin leak: at a 28% hit rate you win about 840 jobs a year. If the quoted vs actual loop cuts over-runs from 10% of jobs to 5%, that is about 42 fewer over-runs at $1,200, or roughly $50,000 a year back.

The time savings get the headlines, but notice where the money is. Recovered estimator hours only pay if they turn into more quotes, better quotes or more follow-up. The hit rate and the quoted vs actual loop are what show up in EBIT. If you only measure hours saved, you will have a nice story and no change in your financials, which is exactly the pattern MIT NANDA's 2025 report describes and the reason we wrote about why AI pilots stall before EBIT.

Quoting software, CPQ or an agent layer: which does a job shop need?

Most job shops need a dedicated estimating and quoting tool, an ERP, and something that connects them to email, purchasing and actual costs. CPQ is usually the wrong fit for build-to-print work. Whether that connecting layer is something you build or buy depends on your team, not on the technology.

• ERP quoting module (JobBOSS, E2, ProShop, Epicor, Global Shop): already paid for, already connected to jobs. Weak at drawing review, geometry and quoting speed. Fine for repeat work.

• Dedicated quoting platform (Paperless Parts and similar): strong at geometry-driven estimating, collaboration and sending quotes, with published ERP integrations. Less focused on the inbox before the quote and the actuals after the job.

• CPQ: strong for configurable catalog products and dealer networks. Poor fit for custom parts from drawings.

• Agent layer on a connected data foundation: reads email, drawings and history across systems, drafts the quote and the job, and closes the quoted vs actual loop. Requires the data work described above, which is the real cost.

On build vs buy: a shop with an in-house developer and clean ERP data can absolutely script email intake and a few reports. Where homegrown efforts usually stall is keeping part, customer and job identities matched across systems as data changes every day, and getting estimators to trust and use the output. We cover that trade-off in more depth in buy vs build for the AI context layer. The biggest competitor in this decision is not another vendor. It is "we will look at it next year," while the estimator who knows everything gets closer to retirement.

How to start: a 90-day plan for quote-to-fabrication automation

Start narrow, measure from day one, and expand only after estimators are using it daily. A realistic plan for a mid-market shop:

Days 1 to 30: baseline and connect

1. Pick one countable unit (minutes per quote or quoted vs actual variance are the best first choices) and start measuring it on the current process.

2. Pick one quote stream: one customer group, one process family (say, machined aluminum parts), or one estimator's queue.

3. Connect the quoting inbox, the ERP (jobs, routings, actual hours, POs), your quoting tool if you have one, and vendor invoices for outside processing.

4. Resolve part numbers, revisions and customer IDs for that stream, and have your lead estimator review the matches. She will find mistakes, which is the point.

Days 31 to 60: draft, do not send

1. Run the agent in shadow mode: it drafts intake records, drawing checklists, routing proposals with evidence, and outside processing prices, while estimators quote the usual way.

2. Compare drafts to what estimators actually quoted. Track where they disagree and why.

3. Turn on the quoted vs actual review for every job closed in the stream, even jobs quoted before the pilot.

4. Write down the rules estimators apply that are not in any system ("never quote under a $450 lot charge for that anodizer," "add a second setup for anything over 18 inches on the Haas").

Days 61 to 90: live with approval

1. Estimators start from the agent's draft and approve, edit or reject. Every edit is logged and becomes training for the next draft.

2. Turn on follow-up drafts for open quotes and PO-to-quote matching for won work.

3. Draft jobs and travelers into the ERP for approval, without auto-release.

4. Re-measure your countable unit against the baseline and decide, with numbers, whether to expand to the next stream.

The single best predictor of success in this plan is whether your estimators use the drafts in week ten. If they are working around it, stop and find out why before adding anything. Built is not adopted.

If you want to see how the pieces fit together end to end, the Quote to Fabrication agent page shows the flow from emailed drawing and PO to costed routing and draft work order, priced from the hours your floor recorded and approved by a person before anything is released.

Frequently asked questions

How do I automate quoting for my machine shop?

Start by measuring minutes per quote, then automate the clerical and lookup work first: RFQ intake from email, drawing data extraction, finding similar past jobs and their actual hours, and outside processing prices. Keep pricing and bid decisions with your estimator, and add quoted vs actual reviews so estimates improve over time.

Can AI read engineering drawings and STEP files to generate a quote?

AI can reliably extract title block data, material, finish, general tolerances, spec callouts and basic geometry from PDFs and STEP files. It still needs a person to judge manufacturability, tight GD&T, conflicting notes, and whether the model and print agree. Treat the output as a checklist the estimator confirms.

How long should it take to quote a machining job?

It depends on complexity, but buyer expectations are clear: in Paperless Parts' 2020 Part Buyer Survey, 67% of buyers expected a quote in under 24 hours. A practical internal target is same-day for repeat and simple parts and next business day for standard new parts, with complex assemblies scheduled explicitly.

What is a good quote win rate for a job shop?

Paperless Parts puts the average job shop win rate at about 30%, and Modern Machine Shop's Top Shops benchmarking reported a median quote-to-book ratio of 70% for Top Shops versus 51% for others. Rates vary widely by customer mix, so track hit rate by customer and part family rather than one shop-wide number.

What is the best quoting software for a job shop?

There is no single best tool. Your ERP's quoting module handles repeat work, dedicated platforms like Paperless Parts are strong at geometry-based estimating and integrate with JobBOSS, E2, Epicor, Global Shop, ProShop and Fulcrum, and an agent layer adds email intake, PO matching and quoted vs actual feedback. Pick based on where your hours are actually going.

Can quoting software create the job and traveler in my ERP when the PO comes in?

Yes, many quoting tools can push a won quote into the ERP as an order and job. The weak spot is the PO itself: revisions, quantities and dates that do not match the quote. An agent that checks the PO against the quote and drafts the job for approval catches those before they reach the floor.

What should a job shop measure to know if quoting automation is paying off?

Measure one countable unit with a four-week baseline: estimator minutes per quote, RFQ turnaround, hit rate, quote-to-order cycle time, or quoted vs actual variance. Hours saved alone rarely show up in the P&L. Hit rate and margin leak on completed jobs do.

Should a fabricator build its own AI quoting tool or buy one?

Build if you have developer time and clean, consistent ERP data, and keep the scope to email intake and reporting. Buy or partner when the hard part is matching parts, revisions, customers and costs across several systems and keeping that current, because that ongoing maintenance is where homegrown projects usually stall.

Sources

• Paperless Parts, "The 7 Fatal Mistakes of Quoting #3: Responding Too Slowly" (2020 Part Buyer Survey: 67% of buyers expect a quote in under 24 hours, up from 53% in 2019). paperlessparts.com

• Paperless Parts, "7 Fatal Mistakes of Quoting" guide, October 2022 (average job shop win rate of about 30%; citing the 2022 Aptean Manufacturing Survey Report). paperlessparts.com

• Paperless Parts, ERP integrations list. paperlessparts.com

• Modern Machine Shop, Derek Korn, "Because There's More to a Shop than Machining" (Top Shops median quote-to-book ratio 70% vs 51%), December 2013. mmsonline.com

• The Manufacturing Institute and Deloitte, "Taking charge: Manufacturers support growth with active workforce strategies," April 2024 (3.8 million jobs needed by 2033, 1.9 million could go unfilled, 65% cite talent as primary challenge). themanufacturinginstitute.org

• Virtualization Review, "MIT Report Finds Most AI Business Investments Fail, Reveals GenAI Divide," August 2025 (MIT NANDA, "The GenAI Divide: State of AI in Business 2025"). virtualizationreview.com

• eMastercam forum, "What's your quoting process?" practitioner thread. emastercam.com

• Autodesk Community, Fusion Manufacture forum, "Machining time estimates are junk." forums.autodesk.com

OutcomeCatalyst connects the systems you already run into a governed intelligence layer your team and your agents can reason over. Demos on this site use fictional data. To see this on your own operation, start a conversation.