Quotr has raised $4 million in a seed round led by Llama Ventures, with participation the company describes only as “strategic angel investors.” The round went out on Business Wire on 30 September at 8:45am Eastern.
The release does not give a valuation or a round letter beyond “seed,” and we found no outlet that has filed either; funding trackers put Quotr’s total raised at the same $4 million. The company came through Berkeley SkyDeck’s accelerator, so this is the first priced round on the public record rather than necessarily the first outside money it has taken. The company was founded in 2024, is based in Berkeley, California, and is, per its own boilerplate, “a product of FLOZ Inc.” It is a Berkeley SkyDeck alum, and the release’s subheading frames the announcement as launching “out of stealth.”
The Claim the Round Is Built On
Most estimating software is sold on accuracy. Quotr’s chief executive is selling something else, and the distinction is the most interesting sentence in the announcement.
“Contractors don’t miss out on bids because their numbers are wrong, but because they’re not able to get to the bid step at all,” said Hanyang Liu, co-founder and chief executive. “The estimate isn’t just a document. It becomes the commercial operating system for the entire project. Every decision that follows traces back to it.”
That is a claim about throughput rather than correctness, and it is a more defensible place to stand. A subcontractor who prices a job well but can only price four a month is capacity-constrained, not skill-constrained, and no amount of accuracy improvement fixes that. If the binding limit on a contractor’s revenue is how many bids they can physically produce, then the product that matters is the one that raises the count — which is also, conveniently, the easiest kind of improvement for a customer to notice.
What the Platform Does
The workflow Quotr describes is the whole front end of a job in one place: quantity takeoff, cost estimating, bidding and procurement. Contractors upload PDF or image plan sets, which the release says Quotr’s AI “reads to count quantities, generate a priced estimate, and create a client-ready proposal.”
On the company’s own site that splits into three things rather than one. There is the software — AI-driven takeoff that counts symbols, measures lengths and calculates areas, with either the contractor’s own cost database or zip-code-based pricing behind it. There is a managed estimation service for developers who, as the release puts it, “prefer a hands-off approach” and want “detailed cost estimates and pro formas within days without running the software themselves.” And there is procurement: the site advertises factory-direct sourcing through “50+ audited” manufacturers across windows, doors, cabinetry, flooring and bath fixtures, with the sourcing work — “negotiation, samples, QC, and certification” — done in “Foshan & Guangdong” and “final-mile delivery to your CA jobsite” at the other end.
Two features are worth separating out because they are where the product’s thesis actually lives.
The first is the AI Agent, which both contractor and developer can query in natural language “to trace cost decisions directly back to the drawings.” That is an auditability feature dressed as a chat interface. An estimate whose every line can be walked back to the sheet it came from is a different commercial object from a spreadsheet — it survives a client’s challenge.
The second is what the release calls a live procurement data layer, tracking “material price movements, supplier availability, and quote expiry windows so bids reflect what materials cost today, not what the database said six months ago.” Stale unit costs are the standard way an estimating system quietly goes wrong, and a quote-expiry window is an unusually concrete thing to build against.
The Numbers, As the Company Gives Them
Quotr’s reported results come from the company and are presented here as such.
Contractors using the platform have cut takeoff time by up to 80%, from roughly 20 hours to one to two hours, and report 40% more bids submitted per month. Bid turnaround has moved “from days to hours.” Since launch, the company says, it has supported estimating workflows on projects “totaling over $1.2 billion in cumulative construction value to date,” across residential, multifamily and commercial work, with developers, general contractors and subcontractors all in production. Pricing begins on a per-seat basis across solo, team and enterprise plans.
The release itself names no customers. Quotr’s own site is where the names are, in a section headed “Testimonials” and subtitled “Trusted on real projects,” each entry labelled “customer perspective.” Maricruz of RL Electric is quoted saying “Takeoffs that used to take us 20 hours can now be completed in just 1–2 hours with Quotr.ai,” and Victor of Biltwise credits the platform with turning “the expertise I’ve built over the past 50 years into a scalable system, making it much easier to onboard and train new estimators.” Those are the company’s own published attributions, not anything we have independently confirmed — and the same section carries a testimonial from a Kyle at Llama Ventures, the firm leading this round.
A separate carousel headed “Our Customers & Partners” shows eight names — Biltwise Structures, the National Science Foundation, Panoramic Interests, RL Electric, Salisbury Moore, Sequoia Commercial Group, UC Berkeley and Vanderbilt University. The site’s own alt text calls each one a “partner logo,” and it does not say which are buying the software, so we are not calling any of them customers.
Of the two headline figures, the 40% is the one that matters more. Takeoff hours saved is a productivity claim of the sort every tool in this category makes. Bids submitted per month is a claim about commercial output, and it is the number that would show up in a contractor’s own accounts.
Where the Money Is Pointed
The release is unusually explicit about which market Quotr is chasing with the proceeds, and it is the same market behind several of the largest construction-technology rounds we have covered this year: data centres.
Quotr plans to “deepen enterprise tooling and expand into the heavy industrial and mission-critical segment where scope errors carry the highest cost.” Its reasoning is that these builds “often span 20+ trade categories (electrical, HVAC, structural steel, fire protection), each requiring committed equipment and specialized labor before construction begins.” The release cites Mordor Intelligence for a projected 8.9% compound annual growth rate in the US data centre construction market from 2026 to 2031; that figure is the research firm’s, as quoted by the company, not ours.
This is a sensible place for an estimating product to aim. A twenty-trade mission-critical job is where the combinatorics of preconstruction get genuinely hard and where a missed scope item is most expensive — and it is also, as Buildots’ $130 million round in September showed, where the contracts have become large enough to change a software company’s trajectory. The company says it will be presenting at the NECA Convention and Trade Show, the ULI Fall Meeting and Chicago Build Expo in October, and Phoenix Build Expo in December.
The Investor
Llama Ventures is an early-stage firm whose own site says it “invests early in AI-native companies across the full intelligence stack — from infrastructure to vertical applications,” lists a San Francisco address, and states a “$300M+” Direct Investing Fund alongside a “$600M+” fund-of-funds vehicle, a typical cheque of “$500K – $5M” and a typical stage of “Pre-seed – Series A+.”
Its visible homepage copy names no sector verticals, describing the mandate only as “the full intelligence stack” — but its Spotlight Companies wall already carries BIM Engine, which pitches itself as “one platform for every built-environment workflow” and sells to architects, general contractors and suppliers, a few logos along from Quotr. Our read, then: a generalist AI fund with a small existing position in design software, rather than a construction-technology specialist adding to a thesis it has held for years.
“Preconstruction is where projects are won or lost, yet most teams are still doing it on spreadsheets, isolated point solutions, or gut feel,” said Kyle Qi, principal and investor at the firm. “We backed Quotr because they’ve built the infrastructure layer the industry is missing. And the traction they’re seeing in the field tells us this is a category moment.”
Where This Sits
Qi’s “category moment” is the right read, though it cuts both ways. Reading a drawing set for what it actually commits you to has gone from an opening to a crowd, and quickly.
LightTable raised $22 million on 27 May to catch preconstruction errors before ground breaks. Primepoint raised $10 million on 13 April against construction drawings specifically, with Yann LeCun among its backers. The founder who sold Pype to Autodesk raised $5 million on 10 September to extract scopes from the same documents on the American side, and Kuro raised €10 million on 30 September — the same day as this round — to find contradictions between drawings and specifications for tier-one German contractors.
Against that field, $4 million is a small cheque, and Quotr’s differentiator is not the reading. It is the attempt to carry one artefact — the estimate — forward into bidding and then into actual material purchase, so that the thing which won the job is also the thing that buys the windows. None of those four, on the descriptions they give of themselves, sells the materials.
Whether that is an advantage or a distraction is the open question. Procurement margin and software margin are different businesses with different balance sheets, and a two-year-old company with $4 million has to pick which one it is defending. The release says the money will “expand enterprise deployments, grow its supplier network, and scale its platform” — which is, read plainly, all three at once.
