High-Density AI Cluster Quoting: Why Liquid Cooling CPQ Is Hard (and How to Fix It)

Modern cloud and AI service providers are racing to deploy high-density data center infrastructure – and running into serious pain when quoting and designing these projects. Massive GPU clusters and dense AI training racks are pushing 30–50 kW per rack, far beyond the limits of legacy air cooling. Neocloud upstarts and hyperscalers alike are embracing liquid cooling, but the configure-price-quote process (CPQ) for such systems is breaking down. Traditional quoting tools and static configurators can’t capture the complexity. In this post we’ll explore why quoting high-density AI infrastructure is so difficult (think rack densities, coolant loops, leak detection, and more) and why a model-based CPQ approach is essential. We’ll also look at how a new generation of design automation platforms – for example, ArchiLabs Studio Mode – can turn a tangle of inputs into a validated design, complete with Bill of Materials (BOM), pricing, drawings, and installation plans. This isn’t a simple coolant distribution unit configurator or one-off liquid cooling quote tool – it’s an AI-driven, design-to-quote workflow for next-gen data centers.

The Challenge of Quoting High-Density Liquid-Cooled Infrastructure

Quoting a traditional air-cooled data hall largely meant counting racks and power feeds. Quoting a liquid-cooled AI cluster, on the other hand, means engineering an entire cooling ecosystem up front. The difficulty comes from the tight interplay of thermal, mechanical, and facility factors. Here are some of the intertwined issues that make high-density data center CPQ so challenging:

• Extreme Rack Densities: Today’s AI and HPC clusters can pack tens of kilowatts (or more) into a single rack. For context, some immersion-cooled deployments can support 80–250 kW per rack – an order of magnitude beyond air cooling (geo.supmea.com). Quoting these systems isn’t just “how many racks?” but “how do we cool each rack reliably?” A minor error sizing the cooling capacity could mean hardware throttling or failures in operation.
• Coolant Distribution Units (CDUs): A coolant distribution unit is the heart of a liquid cooling loop, acting as the pump, reservoir, heat exchanger, and controller between the IT coolant and facility water. Choosing and sizing CDUs is non-trivial – it depends on total load, redundancy needs, coolant type, supply temperature, and how far the water must travel. There’s no simple CDU picker widget for this; you must model the thermals and flow. Each CDU also consumes space and power and needs integration with racks. Most units are designed to sit in-row near the servers they cool (www.datacenterdynamics.com), which means the quote has to account for floor space or rack slots taken up by CDUs, plus the manifolds and piping connecting them.
• Manifolds and Direct-to-Chip Loops: In direct-to-chip (cold plate) liquid cooling, each rack (or group of racks) is fed by a manifold that distributes coolant to server cold plates. A hyperscale D2C deployment might have 20+ racks on one cooling loop, and the CDU must balance flow across all of them in real time. One industry guide notes that if flow to even a single rack is imbalanced, that rack’s GPUs can overheat and throttle long before any room-level alarm sounds (geo.supmea.com). In quoting, this means you must consider valves, flow controls, and extra capacity for balancing – not just main pipe sizes. It’s a dynamic hydraulic network, not a static BOM item.
• Immersion Cooling Systems: Immersion tanks eliminate the need for server fans and traditional air handling, but introduce their own design factors. The tanks (imagine a bathtub filled with servers and dielectric fluid) are heavy – often far heavier than a standard rack. Many raised-floor sites weren’t built for that concentrated load. Quoting immersion might involve adding floor reinforcements or locating tanks on a slab or a lower floor. The cooling distribution is different too: heat is removed via fluid-to-water heat exchangers at each tank, and you may need plumbing for dielectric fluid circulation as well as facility water. Quotes must capture not just “tank hardware” but the engineering for integrating those tanks into the facility (pumps, heat exchangers, fluid, structural supports, etc.). All of this is highly site-specific.
• Facility Water Connections: Unlike air-cooled equipment, liquid-cooled racks need to tie into the facility’s water infrastructure. This means running supply and return piping from CDUs or heat exchangers to the building’s chilled-water loop or dry coolers. In existing data centers, adding these plumbing runs is often the toughest part. Pipes might go under a raised floor (potentially blocking airflow if not planned carefully) or overhead on a cable tray or hangers. In raised-floor data centers, poorly planned piping can create obstructions in cooling airflow – one Vertiv technical article recommends using CFD modeling to optimize pipe routes and avoid messing up underfloor air distribution (www.datacenterdynamics.com). In slab (non-raised) facilities, pipes usually go overhead with drip trays under every valve and joint to catch leaks (www.datacenterdynamics.com). A good quote needs to consider the route, length, diameter, and materials of new piping, and often the installation phasing as well (you may only retrofit one area at a time to avoid downtime (www.datacenterdynamics.com)).
• Leak Detection and Risk Mitigation: Introducing liquids around servers raises the specter of leaks. Modern liquid cooling installations mitigate this with everything from drip pans and leak sensors to negative-pressure coolant loops. (For example, one open industry white paper details strategies like leak containment, sensing cables, and automated shutoff valves at various points in the system.) (www.datacenterdynamics.com) Quoting a high-density project means planning for these leak detection systems and failsafes. That could include floor leak sensor strips, CDU leak detectors, smart PDUs that kill power on moisture, or even choosing dielectric fluid over water if the client is extremely risk-averse. Each choice affects cost, layout, and maintenance. A data center operations blog notes that while water-based cooling is very effective, it “introduces water throughout the facility, creating a critical need for reliable leak detection” (ambient-enterprises.com). In other words: every quote for liquid-cooled infrastructure should budget for leak mitigation measures (and perhaps higher insurance!).
• Floor Space and Service Clearances: High-density configurations often introduce new equipment on the floor – in-row CDUs, pump cabinets, heat exchangers, manifolds, etc. These units need floor space that might otherwise be used for IT racks, impacting revenue if not minimized. They also require service clearances so technicians can get in and swap filters, fix pumps, open immersion tank lids, and so on. A quoting tool has to know, for instance, that an immersion tank can’t be placed flush against a wall, or that a CDU needs a certain clearance in front for maintenance. Otherwise, the design might work on paper but be unserviceable in reality. Clearances and aisle space also relate to safety codes and hot aisle/cold aisle layouts, which need to be respected even as we cram more gear into the room.
• Hose Routing and Connectivity: Unlike air-cooled racks which just need power and network cables, liquid-cooled racks need coolant hoses or rigid piping connections. These have to be routed without causing tripping hazards or kinks, and ideally with quick-disconnect fittings for service. Quotes should account for hose packs, brackets, trays, or other routing hardware. If a design has, say, overhead coolant distribution with drop-down hoses to each rack, the quote needs those drop assemblies included. If it’s underfloor distribution, one might need to include floor cutouts, penetration seals, and so on. Every additional component is an opportunity for error if not properly configured.
• Heat Rejection Systems: Cooling 1 MW of IT load with liquids just moves the heat elsewhere – usually into the facility’s external heat rejection system (cooling towers, dry coolers, chillers). High-density clusters often run warmer water (to improve cooling efficiency), but still you need sufficient capacity in the final heat rejection. This might mean quoting new dry coolers or adiabatic coolers, or upsizing pumps and heat exchangers in the existing plant. The approach can differ by location too: in a cool climate, you might use dry coolers; in a hot climate, you might need evaporative assist. The heat-rejection strategy and equipment should be a part of the CPQ process. Ignoring it can be costly – if you deliver racks with liquid loops but the site can’t get rid of the heat, nothing works! It’s common to leverage existing infrastructure where possible (tie into a present chilled water loop or cooling tower), but upgrades may be required. For example, operators might add an adiabatic cooling module to a dry cooler to achieve the lower water temperatures that 2-phase immersion or cold plates require (www.datacenterdynamics.com). These nuances need to be reflected in both the design and the quote.
• Redundancy and Reliability: High-density clusters are often mission-critical (think AI training jobs running for days, or HPC simulations). Downtime is unacceptable, so cooling can’t be a single thread of failure. Quoting must consider redundant pumps, loops, and power feeds. In practice this could mean an N+1 pump configuration in each CDU, redundant CDU units (with automatic switchover) for critical loops, backup power for coolant pumps (so cooling continues through generator startup), and perhaps a fail-safe air cooling backup if the liquid system is down. These aren’t afterthoughts – they have to be baked into the design from the start. A state-of-the-art CDU typically includes dual pumps and automated controls to handle a pump failure without downtime, as well as electronic valves and sensors to adjust flow on the fly (www.supermicro.com) (www.eaton.com). All that redundancy adds cost and complexity, which a quoting tool needs to accurately reflect (e.g. don’t “forget” that second pump in the BOM, or the controls integration engineering).
• Controls and Integration: Unlike a simple air-cooled rack (which just plugs into power and maybe a thermostat feedback), liquid cooling systems come with their own control systems. There are PID controllers maintaining coolant temperatures, flow regulators, alarm panels for leaks, and often integration points to the facility’s BMS (Building Management System) or DCIM software for monitoring. When configuring a liquid-cooled environment, quoting should include the software and integration effort: licenses for control software, PLCs or control boards in CDUs, commissioning of sensors, tie-ins to existing monitoring dashboards, etc. For example, a direct liquid cooling setup might need a software integration so that if a CDU detects low flow, it can signal to slow down server workloads or alert operators. These control loops are part of the deliverable, and a static spreadsheet quote might easily miss them.
• Commissioning and Support: Installing high-density liquid cooling isn’t a “rack and stack Friday, go live Monday” affair. It requires careful commissioning – pressure testing the piping, filling coolant loops and purging air, checking every connection for leaks, calibrating sensors, and verifying failover behaviors. Many end-users will require a formal commissioning and testing plan as part of the project scope. A robust quote tool should be able to generate at least a draft of this plan or account for the services hours needed to do it. Additionally, there’s operator training, documentation for procedures (like how to safely do a server swap in an immersion tank), and possibly ongoing maintenance contracts for the cooling system. All these “soft” costs and tasks need to be considered early, or the quote may seriously underestimate time and expense. The complexity of commissioning is one reason design-to-quote AI clusters can’t be handled with a simple online configurator – you need to model not just the gear but the process of bringing it online.
• Retrofit Constraints: Many AI deployments are going into existing data centers (often colocation facilities or enterprise sites that were originally built for air cooling). Retrofitting is far trickier than a greenfield build – space is tight, live IT equipment is nearby, and you often have to keep part of the hall running while upgrading the other part. This phased approach must be reflected in the plan and quote. For example, if you’re quoting 10 liquid-cooled racks into an active room, you might include temporary cooling setups or nighttime installation work in the services. It’s common to start by converting one pod or corner of a room to liquid cooling as a pilot, then expanding (www.datacenterdynamics.com). As a result, the full quote might need options for a phased build-out, with pricing for each phase. Site constraints like available power, existing chiller capacity, and floor layout will dictate how you can deploy the new equipment. All of these factors mean that a static product configurator will fall short – you can’t just fill out a form with “10 liquid racks, please” and get a reliable quote. The quoting tool needs to understand the site and design around its realities.

It’s clear that quoting a high-density liquid-cooled data center is as much an engineering design exercise as it is a sales exercise. The huge number of variables listed above all interact. For instance, the choice of coolant (water vs dielectric) affects whether you need leak containment and what materials are used in piping; the decision to go direct-to-chip vs immersion affects rack layout, which in turn affects floor loading and pipe routing; the facility’s existing cooling defines what heat rejection upgrades are needed; and so on. Manually juggling all these considerations in Excel or in a basic quoting GUI is error-prone and slow. This is why model-based CPQ is emerging as the better approach.

Why Model-Based CPQ Beats Static Configurators

Given the complexity, the industry is recognizing that quoting tools must evolve. A static product catalog or configurator can’t effectively handle high-density AI clusters – there are too many conditional rules and spatial constraints. Instead, we need model-based CPQ: tools that dynamically generate a design model of the solution as part of the quote. In practice, this means using a parametric model (often a CAD or BIM model under the hood) to validate that all the pieces actually fit and perform before finalizing the quote.

Static Configurator Limitations: Traditional CPQ software or quote spreadsheets are essentially forms – you input numbers (racks, kW, cooling units) and they spit out a list of parts and a price. They struggle with combinatorial rules. You might encode some if/then logic (e.g. “if more than 5 racks, add a second CDU”), but it’s hard to capture geometric and physical constraints. For example, a static tool might let you configure 10 racks and 2 CDUs, but it won’t tell you how to arrange them in a single row, whether they’ll fit in your room, or whether the piping length exceeds what the pump can handle. It also likely won’t check things like “does this CDU have enough ports for 10 racks?” or “will this configuration meet ASHRAE cooling guidelines at the chosen water temperature?” – at least not without an enormous amount of custom coding.

Model-Based Approach: A model-based CPQ uses a parametric design engine behind the scenes. When you input requirements, it doesn’t just look up part numbers; it actually builds a virtual model of the data hall or cluster and simulates the design. The model knows the dimensions of racks and CDUs, the bending radius of hoses, the heat output of servers, the cooling capacity of heat exchangers, etc. It can then spot conflicts or necessary components that a human might overlook. The result is a much richer output: not just a price quote, but a validated design and documentation.

A true model-driven quoting tool for liquid cooling needs to generate or provide all of the following (not as static templates, but tailored to each solution):

• Physical Layout & Geometry: A 2D/3D layout of racks, cooling units, and pipe routes in the space, ensuring everything fits and is serviceable. This might include plan drawings, elevation views, or even 3D models. Generating these layouts catches issues early – e.g. if an extra CDU won’t physically fit in the row, the model will show it so the configuration can change before quoting.
• Thermal & Hydraulic Assumptions: Calculations or assumptions for coolant flow rates, temperatures, and heat loads. The CPQ should output key parameters (like “inlet water 32°C, 30 L/min per rack, ΔT 8°C”) that were used to size the equipment. This not only proves the quote is based on sound engineering, it also carries forward to deployment (the ops team knows what to expect). Ideally, the tool would flag if a certain design is pushing these limits – for instance, highlighting that the chosen pump has only 5% headroom at the required flow, which might warrant upsizing or a different approach.
• Bill of Materials (BOM): A detailed BOM down to not just major equipment but ancillary parts: pipes, fittings, valves, sensors, cable trays, pumping modules, etc. This ensures the quote is all-inclusive. It’s easy to forget things like valve kits or a spare parts package in manual quotes. A model-based system can compile everything that was “placed” in the virtual design into a structured BOM. Grouping and formatting the BOM for the customer (e.g. by subsystem: IT gear vs cooling infrastructure vs services) can also be automated.
• Quote Pricing Options: The tool can apply pricing (from a database or ERP) to the BOM to generate the quote. But more than that – it can enable optionality and alternative scenarios with minimal extra effort. Because the design is generated from rules, you could quickly get variations: for example, one option with direct-to-chip cooling and another with immersion, or an option that uses two larger CDUs vs four smaller ones. In a manual process, each of those options would be a full rework. Model-based CPQ lets you spin up alternatives by tweaking inputs, with the confidence that each option is properly engineered. Faster turnaround on complex quotes means a better chance of winning deals in a competitive market (velispec.com) (velispec.com).
• Proposal Drawings & Visuals: Beyond raw technical drawings, a good quote package should include clear visuals – for instance, rendered views of the cabinets and cooling system, schematic diagrams showing how the coolant loop connects to facility chillers, etc. Manually creating these is time-consuming; a model-based approach can auto-generate them from the design model. These visuals are hugely helpful for customers (and for internal stakeholders who need to approve the plan). They make the proposal tangible, turning abstract numbers into concrete plans. A dynamic configurator might even generate an interactive 3D model that the client can rotate and inspect in a web browser.
• Installation and Commissioning Packages: Since high-density installations are complex, the quote often needs to outline the plan for installation. A model-driven CPQ could produce an installation sequence, method of procedure, or at least notes on what the installation will entail (e.g. “Step 1: tap into chilled water main in Room 2, Step 2: install underfloor piping to Row B,” etc.). It can also generate testing and commissioning checklists specific to the configuration. For example, if the design includes 20 leak sensors and 2 pumps, the commissioning checklist will ensure each sensor and pump is tested. Automating these documents ensures nothing is forgotten and streamlines the hand-off to implementation teams.

In short, model-based CPQ transforms quoting from filling out a spreadsheet to designing a solution in real-time. It reduces errors (because the model won’t forget that, say, each rack needs 2 hoses and you have 10 racks, so it adds 20 hoses automatically). It also saves time – a process that used to take days or weeks of back-and-forth engineering can now be done in hours or minutes, with much richer results. As an example, one visual CPQ tool for cloud infrastructure reported cutting quote turnaround from days to minutes by automating configuration logic (velispec.com) (velispec.com). For AI data centers, where speed to market is critical, this speed and accuracy in quoting are a competitive advantage.

From Requirements to Layout: Design-to-Quote in Action

Let’s walk through what a design-to-quote workflow might look like for a high-density AI cluster using a model-based platform. Imagine a liquid cooling vendor or integrator is using an advanced CPQ system (such as ArchiLabs Studio Mode) to configure a solution for a client. Here’s how it could go:

1. Input Key Requirements: The user starts by entering the core specs of the deployment. For example: 20 racks of AI servers at 30 kW per rack, to be cooled with direct-to-chip liquid cooling. They specify the facility details – say it’s an existing data hall with 18″ raised floor, warm water supply at 35°C, and a need for N+1 redundancy on cooling. They also input any site constraints like room dimensions, desired rack placement (e.g. two rows of 10), and existing cooling capacity (maybe the building has 500 kW of unused chiller capacity we can tap). This can be done through a web form with guided inputs or even via natural language (e.g. “Design a solution for 20 racks at 30kW each with direct liquid cooling in a 1000 sqft room”).
2. Automated Design Generation: Once the parameters are in, the CPQ engine kicks off a recipe or script that generates the design. In ArchiLabs Studio Mode, this would be a parametric CAD model assembled by code. The algorithm places 20 rack objects in two neat rows (ensuring hot-aisle/cold-aisle orientation and adequate spacing for maintenance). It then adds the cooling infrastructure: based on 600 kW of IT load and N+1 redundancy, it might select 3 CDUs of 300 kW each (2 active, 1 standby) and place them at the ends of the rows. It routes piping from the CDUs to each rack’s manifold, following rules to avoid blocking floor tiles and to minimize bends. Perhaps it chooses overhead routing since the raised floor is crowded – the model adds pipe racks above the rows, and runs supply/return lines with drop-downs to each rack manifold. It also places quick-disconnect fittings at each rack and a leak sensor under each CDU and manifold. Throughout this process, the system is checking constraints: if a pipe run gets too long or a bend too tight, it flags it; if the CDUs won’t fit where intended, it might try an alternate location (or alert the designer to adjust rack layout). The end result is a virtual 3D model of the entire solution, created in minutes. Every component – from the big pumps down to every elbow fitting – is represented.
3. Validation & Optimizations: With the model assembled, the platform runs validation checks. For example, it sums up the total flow rate needed and verifies the selected pumps can handle it with some margin. It checks the weight loading: each rack is, say, 1000 kg with servers and coolant, each CDU maybe 200 kg – it compares this against floor specs and might warn “Raised floor capacity exceeded in this area” if necessary. (If so, the designer could move the CDUs off the raised floor or specify floor supports, which the model can incorporate.) It checks clearances: every rack and CDU in the model carries metadata about required front/rear clearance, so the software highlights any overlaps or violations of service space. It also evaluates heat rejection capacity: the model knows the facility’s existing cooling limit (500 kW in this example) and will warn if our 600 kW design needs extra heat rejection. Sure enough, it flags that gap – so the user quickly updates the design input to add two 300 kW dry coolers outside. The recipe then adds those to the model, connecting them to the CDUs in the schematic. These “smart components” (racks, CDUs, coolers, etc.) carry their own rules and know to report errors if conditions aren’t met. By the end of this step, we have a validated design: one that meets the technical requirements and respects the site constraints. Any errors are resolved in the digital model, not left to be discovered during construction or after installation.
4. Output Generation – BOM, Drawings, Pricing: Now the platform generates all deliverables. First, it compiles the Bill of Materials: 20 liquid-cooled racks (if the vendor is providing the IT racks too), 3 CDUs with model numbers, X meters of piping, Y fittings, Z leak sensors, etc., plus the two dry coolers and so on. The BOM lists all part numbers and quantities. Pricing is then pulled in for each from a pricing database – applying customer-specific discounts or regional labor rates as needed – to produce an itemized quote. Next, the software produces proposal drawings: a plan view showing the rack layout and CDU placement in the room, an isometric diagram of the piping network, and a schematic of the cooling architecture (CDUs connected to the facility water loop and to the racks). Because this is all generated from the live model, these drawings are to scale and accurate. If the user were to move a rack or change a part and re-run, the drawings would update. The tool might also export a 3D model (for example, a glTF or IFC file) that the client’s facilities team can review or plug into their BIM model. Finally, the system packages any installation notes or documents. For instance, since this was a retrofit scenario, it attaches a recommended phasing plan: “Week 1: install overhead supports; Week 2: install piping to Row A; cut over Row A cooling; Week 3: install piping to Row B…” etc. It may also include a commissioning checklist derived from the components (e.g. “Pressure test circuit A for 24 hours”, “Verify alarm integration with BMS”, “Test failover pump on CDU 2”, etc.). These details give the customer and implementation team a clear roadmap, all auto-generated from the validated model.
5. Iterate and Refine Quickly: With this model-based approach, making changes is fast. Suppose the client says, “Actually we need 25 racks, and we prefer an immersion solution instead of cold plate – what does that look like?” In a traditional process, that’s basically a whole new design effort. But with our parametric model, the engineer (or even the client, through a controlled portal) can adjust the rack count to 25 and switch a parameter from direct liquid cooling to immersion. The underlying recipe would swap out the cold-plate racks for immersion tank modules, perhaps choose a different CDU or cooling distribution (since immersion might use centralized pumping plus secondary heat exchangers), and re-run the validations. Within a short time, the new design and quote are ready, allowing the client to compare options side by side. This flexibility is only possible because the quoting tool is actually doing real design in the background, not just static configuration. It captures the engineering knowledge of how to design these systems so that exploring a different scenario doesn’t require going back to the drawing board each time.

This hypothetical scenario highlights how design-to-quote workflows can revolutionize capacity planning for complex data center builds. Instead of treating design and quoting as separate steps (with weeks of delay in between), they happen together. The sales engineers, infrastructure designers, and even the end customers can all collaborate on the model – often via a web interface – to fine-tune the solution. The CPQ platform ensures that any configuration presented is feasible and optimized, not just theoretically correct but physically and thermally sound.

ArchiLabs Studio Mode: An AI-First Platform for Data Center Design Automation

One example of this new breed of model-based CPQ and design tools is ArchiLabs Studio Mode. ArchiLabs is a web-native, AI-driven CAD and automation platform built specifically for complex infrastructure like data centers. Unlike legacy desktop CAD tools that have scripting bolted on as an afterthought, ArchiLabs Studio Mode was designed from day one with automation in mind – code is as natural as clicking, and every design decision is tracked and traceable. At its core, Studio Mode includes a powerful geometry engine with a clean Python interface. It supports full parametric modeling (think extrude, revolve, sweeps, booleans, fillets, chamfers – all the solid modeling operations you’d expect in a tool like SolidWorks or Revit) but in a fully programmable, web-based environment. You build models with code or interactive tools, and the history of operations is captured in a feature tree that you can rollback and edit parametrically.

In ArchiLabs, design components carry their own intelligence – ArchiLabs calls these smart components. For a data center context, this means, for example, a rack object in the model “knows” its attributes: its physical dimensions, its maximum power draw, how much cooling it needs, what clearance around it is required for airflow or service, etc. A cooling manifold component knows how many servers it can support and might enforce a max pipe length. A CDU unit knows its pumping capacity, port count, and redundancy options. These smart components can have built-in rules and even analytics. So if you drop a cooling layout into the model, it can auto-check capacity and flag violations (e.g. “Rack 7 is 5 kW over the cooling loop limit” or “Leak sensor coverage not provided at this low point”). The platform provides real-time impact analysis – as you adjust a design parameter, you can see the effects (load, cooling headroom, electrical demand, etc.) recomputed live. Validation is proactive and computed, not manual, meaning design errors or rule breaches are caught inside the platform by the model, instead of being left to humans to catch in review or, worse, during construction.

ArchiLabs Studio Mode also treats collaboration and iteration as first-class citizens. The platform has git-like version control for designs – you can branch a data hall layout, try an alternate cooling configuration on the branch, then diff the changes to see exactly what changed (maybe the diff shows “added 5 racks and 1 CDU, changed water supply temperature from 35°C to 45°C”). Team members can work concurrently with real-time updates (no more emailing CAD files or waiting for someone to finish editing). Every change is logged with who, when, and what parameters were used, providing a full audit trail of design decisions. This is crucial for large projects where multiple engineers, contractors, and stakeholders are involved – you always have a source of truth and accountability.

Where ArchiLabs really stands out for CPQ purposes is its automation and AI integration. It features a Recipe system, which are essentially versioned, executable scripts or workflows that can automate any sequence of design operations. For instance, a Recipe could be “Place rows of racks to fill available space, then add necessary power and cooling infrastructure based on input load.” Domain experts (like senior data center engineers) can write these recipes in Python, encoding their best practices and rules of thumb. But recipes can also be generated or suggested by AI: Studio Mode has an AI assistant that can translate natural language (like “optimize this layout for 2N redundancy and generate a CFD report”) into actions on the model. Users can compose complex workflows from a library of smaller recipe steps as well. Over time, a company can build up a library of proven recipes – their institutional knowledge captured as reusable, testable code. Whenever a similar project comes along, they can run the recipe and get a baseline design in minutes.

The platform’s web-first architecture means no software installs, and performance is not limited by your local PC. Heavy geometry computations happen on the server side (with caching so that identical components or sub-assemblies don’t get recomputed unnecessarily across projects). This allows ArchiLabs to handle massive facilities – 100 MW+ campuses with thousands of components – without the lag and crashes you’d experience if you tried to jam it all in one traditional BIM file. In fact, Studio Mode uses a system of sub-plans that load independently, so you might break a campus into buildings or a building into rooms, working on each in context without bogging down the whole model. It’s a similar concept to modular game engines or large software projects – divide and conquer – which legacy CAD often struggles with.

Crucially, ArchiLabs doesn’t try to be a silo. It’s built to connect with your entire tech stack and toolchain. There are integrations and APIs to pull in data or push out results: you can connect to Excel sheets, enterprise ERP databases, DCIM systems, traditional CAD tools like Revit or AutoCAD, simulation software, and more. This means your CPQ process can tie directly into procurement systems (updating pricing as costs change), or push a finalized design into Revit for detailed engineering, or sync with DCIM so that the as-built model stays up to date in operations. All these connections ensure that the design data and metadata stay as a single source of truth, rather than fragmenting into multiple out-of-sync documents. For example, if an equipment vendor changes a specification, you update it in one place and all recipes and models using that component can update accordingly.

ArchiLabs has been used to automate many repetitive data center workflows beyond just initial layout. Teams have created recipes for things like: optimal rack and row layout given a room shape and power/cooling constraints; auto-routing of cable trays and fiber pathways once racks are placed; equipment placement checks (e.g. ensuring CCTV cameras have no blind spots, or access control readers are at every door); even automated commissioning tests – generating test procedure documents, then later running actual telemetry through a checklist to validate that each system performs as designed, and flagging any discrepancies. The platform logs test results and can produce completion reports, tying it back to the design for full traceability. Document control is also improved: ArchiLabs can interface with document repositories or version control, so your PDFs of floor plans, cable schedules, etc., are automatically updated when the design changes and are tagged with the relevant version.

Perhaps most exciting is the concept of custom AI agents running on ArchiLabs. Because the system is code-first and has APIs, companies can train AI models or agents to handle specific workflows end-to-end. For example, imagine an “AI data hall planner” agent that takes in a plain English description of requirements (power, cooling, timeline, budget constraints) and automatically invokes the right recipes and steps to produce a complete project outline. It might generate a few layout options, query an external database for equipment availability, ensure the design meets uptime requirements, and even draft the proposal document – all without human intervention. While human experts will always be in the loop for final decisions, much of the heavy lifting can be offloaded to these domain-specific AI agents. ArchiLabs makes this feasible because everything in the platform is accessible via code and API – the AI isn’t a bolted-on chatbot, it’s essentially driving the same operations a power user could do manually, but at machine speed. These agents can also interact with external systems: for instance, pulling real-time pricing from a supplier API, or retrieving a BIM file from a repository, or writing an updated CAD file back into a client’s system for record-keeping. The key point is that domain-specific knowledge is encapsulated in swappable content packs and recipes – if you’re designing data centers, you load the data center pack and the AI knows about racks, PDUs, CRACs, CDUs, etc.; if you were designing something else (say a battery storage facility or a factory), you’d load a different pack. ArchiLabs doesn’t hard-code data center behavior into the platform – it provides the tools to encode that behavior yourself (or via content libraries), which makes it extremely flexible as technology and best practices evolve.

From a business standpoint, the value of a platform like ArchiLabs Studio Mode is that it turns your best engineers’ knowledge into reusable, testable workflows. Instead of every new high-density project being a one-off fire drill with tribal knowledge and custom spreadsheets, you build up a set of proven design rules and let the software enforce and execute them. This not only saves time and reduces errors, it also means new team members can ramp up faster (since they can leverage existing recipes) and the whole organization can take on more complex projects confidently. The risk of human error drops, and engineering effort shifts from low-level grunt work (drawing ducts, calculating pipe sizes) to high-level decision-making and optimization. Moreover, by integrating with all the legacy tools (yes, that includes Revit and AutoCAD as just one of many integrations), ArchiLabs can slot into existing workflows gradually – you’re not forced to rip out everything and switch to a new ecosystem overnight. You might start using it just for rapid CPQ studies on new projects, then gradually use it for detailed design as trust in the system grows, all the while pushing final designs to Revit for documentation because the industry still asks for it. But over time, as more of the ecosystem becomes web-enabled and code-driven, ArchiLabs is positioned to be the central AI-first design hub that orchestrates all these pieces.

Positioning for the Future: Quoting and designing high-density, liquid-cooled AI clusters is arguably one of the most complex configuration challenges data center teams have ever faced. But it’s also becoming routine – these projects are happening every day as companies deploy AI infrastructure at scale. To stay competitive, vendors, integrators, and operators need to leverage automation and AI in their design processes. A model-based, AI-driven CPQ approach, exemplified by platforms like ArchiLabs Studio Mode, turns a daunting engineering project into a fast, collaborative, and reliable workflow. It lets you go from concept to validated design to quote in a single streamlined process, with the computer handling the heavy lifting of layout, checks, and documentation.

For data center engineering teams focused on capacity planning and infrastructure automation, adopting these tools is rapidly moving from a nice-to-have to a necessity. It means being able to respond to customer demands for 100 kW racks or liquid-cooled retrofits in days instead of weeks. It means catching design issues (insufficient cooling, physical fit problems, compliance violations) early in the virtual model, not late in the field when fixes are expensive. And it means capturing your company’s hard-won expertise about what works and what doesn’t in a form that’s scalable and continuously improving. In the era of AI-driven everything, it’s only fitting that the data centers themselves – the very homes of AI – are designed and delivered with the help of AI-first platforms. Model-based CPQ is how we get there, and the companies that embrace it will deliver more robust solutions faster, with fewer surprises, positioning themselves as leaders in the new wave of high-density infrastructure. The bottom line: quoting high-density liquid cooling might be hard, but with the right tools, we can make it seamless – and maybe even fun, as teams watch their ideas come to life in real time.