Traza raises $2.1 million led by Base10 to automate procurement workflows with AI

For decades, procurement has been the back office that enterprise software forgot. Billions of dollars flow through vendor negotiations, purchase orders, and supplier communications every year at the largest manufacturers and construction companies in the country — and the vast majority of that work still runs on email threads, spreadsheets, and phone calls.

Traza, a newly launched startup headquartered in New York, believes the moment has arrived to change that. The company announced today the close of a $2.1 million pre-seed round led by Base10 Partners, with participation from Kfund, a16z scouts, Clara Ventures, Masia Ventures, and a roster of angel investors including Pepe Agell, who scaled Chartboost to 700 million monthly users before its acquisition by Zynga.

The funding is modest by Silicon Valley standards. But Traza’s pitch is anything but incremental: the company deploys AI agents that don’t just recommend procurement actions — they execute them autonomously, handling vendor outreach, request-for-quote generation, order tracking, supplier communications, and invoice processing without continuous human supervision.

«AI is redesigning the procurement category from the ground up,» said Silvestre Jara Montes, Traza’s CEO and co-founder, in an exclusive interview with VentureBeat. «This wave of AI won’t just build procurement software — it will rebuild how procurement works.»

Why procurement contracts silently lose millions after the ink dries

The market Traza is targeting is enormous and, by the company’s framing, spectacularly underserved. The procurement software market alone exceeds $8 billion and grows at roughly 10% annually. But the real cost sits in the labor — the armies of people, agencies, and ad hoc workarounds required to actually run procurement operations at scale. Most enterprises meaningfully engage with only their top 20% of suppliers. The remaining 80% — the vendor outreach, order tracking, invoice reconciliation, and compliance monitoring — goes largely unmanaged.

Research from World Commerce & Contracting and Ironclad finds that organizations lose an average of 11% of total contract value after agreements are signed, a phenomenon described as «post-signature value leakage.» As Tim Cummins, President of WorldCC, put it: «The research shows that the 11% value gap is not caused by poor negotiation, but by how contracts are managed after signature.» For a large enterprise with $500 million in annual contracted spend, that represents $55 million vanishing each year — not from bad deals, but from the operational void between what gets agreed at the negotiating table and what actually gets executed on the ground. Missed savings, unauthorized changes, and poor renewal planning are responsible for the biggest losses.

Jara Montes argues that Traza sits precisely in this gap. «The 11% spans commercial, operational, and compliance leakage. We own the operational layer — and that’s where the most recoverable value sits,» he said. «Supplier tail management that never happens, RFQ processes skipped because someone ran out of bandwidth, invoice discrepancies that slip through unnoticed. That’s where contracts bleed value after signing, and that’s exactly what we automate.» The numbers from Traza’s early deployments, while nascent, are striking: the company claims a 70% reduction in human hours spent on procurement tasks and procurement cycles running three times faster than manual baselines.

How AI agents crossed the line from procurement copilot to autonomous worker

To understand what makes Traza’s approach different, it helps to understand what «AI for procurement» has meant until now. For the past several years, the term largely described dashboards, analytics layers, and recommendation engines that surfaced insights but left every decision and action in a human’s hands. Products from incumbents like SAP Ariba and Coupa — as well as newer entrants like Zip, Fairmarkit, and Tonkean — have layered AI capabilities on top of existing systems of record. But the gap between piloting AI and achieving production-scale impact remains stark, with 49 percent of procurement teams running pilots but only 4 percent reaching meaningful deployment.

Traza’s bet is that 2026 represents an inflection point. AI agents now possess the multi-step reasoning, tool use, and contextual memory required to execute full procurement workflows autonomously — from vendor discovery through invoice processing. The company frames this not as an upgrade to existing procurement software, but as an entirely new product category. «The incumbents built systems of record. They organize procurement data and they’ve never executed procurement work — and their AI additions don’t fundamentally change that,» Jara Montes said. «What they’re shipping is a recommendation layer on the same underlying architecture. A human still has to act on every suggestion. We replace the operational layer entirely.»

Industry data supports the thesis that enterprises are hungry for this shift. According to the 2025 Global CPO Survey from EY, 80 percent of global chief procurement officers plan to deploy generative AI in some capacity over the next three years, and 66 percent consider it a high priority over the next 12 months. A 2025 ABI Research survey found that 76% of supply chain professionals already see autonomous AI agents as ready to handle core tasks like reordering, supplier outreach, and shipment rerouting without human intervention — and early deployments are demonstrably reducing supply chain operational costs by 20 to 35%.

Inside the workflow: what Traza’s AI does and where humans still make the call

In a typical deployment, Traza’s AI agent takes over the operational labor that currently lives in inboxes, spreadsheets, and manual follow-up chains. In a standard RFQ workflow, the agent identifies suitable suppliers, drafts and sends the request for quotes, monitors supplier responses, follows up automatically when responses lag, parses incoming quotes regardless of their format, and builds a structured comparison table ready for a human decision-maker. The key design principle is deliberate: humans remain in the loop at critical junctures.

«At critical steps — approving a purchase order, flagging a compliance issue, committing spend above a threshold — a human is always in the loop,» Jara Montes explained. «That’s not a limitation, it’s the design. It’s how you maintain the auditability enterprises require while moving faster than any manual process could. You earn expanded autonomy over time, as trust is built and results compound.»

When asked about the risk of AI errors — a wrong purchase order or a missed compliance check that could prove costly — Jara Montes was direct: «Anything with meaningful financial or compliance exposure requires human approval before it executes — that’s non-negotiable and baked into the architecture. Below those thresholds, the agent acts autonomously and logs everything.» He added a point that reveals a subtler product insight: «Most procurement operations today are a black box — nobody has a clear picture of what’s happening across the supplier tail. We make it legible.» In other words, the transparency the AI agent provides may itself be a product — giving procurement leaders visibility they have never had into the long tail of supplier relationships that most enterprises simply ignore.

How Traza plugs into legacy enterprise systems without ripping them out

One of the recurring challenges for any enterprise AI startup is the integration question: How do you plug into the deeply entrenched, often decades-old technology stacks that large manufacturers and construction companies rely on? Traza’s answer is to sit on top of existing systems rather than replace them. «We connect via API or direct integration into whatever the customer already runs — ERPs, email, supplier portals. We have reach across more than 200 enterprise tools,» Jara Montes said. «We don’t rip out their system, we sit on top of them.»

The go-to-market motion mirrors this pragmatism. Instead of attempting a big-bang deployment, Traza runs a two-to-three-month proof of value focused on a single, specific workflow. Integrations are built at the key steps that matter for that particular use case, then expanded as the scope of the engagement grows. «We don’t try to connect everything upfront — we compound integrations as we expand scope within each account,» Jara Montes said. «And every integration we build compounds across customers too. Each new deployment makes the next one faster.» Throughout the process, the company works side by side with the customer’s team, managing complexity and helping them transition into a new way of operating. It is a notably high-touch approach for a company selling automation.

The company is already working with large manufacturers and construction companies and says they are paying, though it declines to name them publicly. «We want to earn the right to grow inside each account, not land a pilot that goes nowhere,» Jara Montes said. «That’s how you build something that actually sticks in enterprise.»

Traza bets that vertical depth in physical industry will beat horizontal AI platforms

Traza enters a market that is rapidly heating up. The leading AI procurement solutions include platforms from Coupa, Ivalua, SAP Ariba, Zip, Zycus, and Fairmarkit. Keelvar provides autonomous sourcing bots capable of launching RFQs, collecting bids, and recommending optimal awards, while Tonkean offers a no-code orchestration platform using NLP and generative AI to streamline procurement intake and tail-spend management. Against this crowded field, Jara Montes draws a sharp distinction between horizontal automation tools and Traza’s focus on physical industry.

«We’re built specifically for the physical industry, where supplier relationships, compliance requirements, and workflow complexity are categorically different from software procurement,» he said. «A generic agent doesn’t survive contact with how procurement actually works in manufacturing or construction. Specificity is the moat.» The competitive dynamics with major incumbents are perhaps even more consequential. SAP Ariba, Coupa, and their peers have massive installed bases and deep enterprise relationships. Jara Montes frames their AI initiatives as surface-level additions to legacy architectures — but whether Traza can convert that framing into market share at scale, especially given the gravitational pull of existing vendor relationships, remains the central strategic question.

Beneath Traza’s product pitch sits a deeper strategic thesis about compounding data advantages. The company describes a two-layered learning architecture: at the agent level, Traza gets smarter across every deployment by absorbing supplier behavior patterns, RFQ response dynamics, pricing anomalies, and workflow edge cases. At the data level, each customer’s information stays fully isolated. «What we’re building is deep operational knowledge of how procurement actually runs in the physical industry — not how it’s supposed to run according to an RFP, but how it really runs, with all the exceptions and workarounds,» Jara Montes said. «That’s extraordinarily hard to replicate if you’re starting from scratch, and it gets harder to catch up with the more deployments we have.»

Three Spanish founders, one fellowship, and a plan to rewire industrial procurement

Traza was co-founded by three Spanish entrepreneurs — Silvestre Jara Montes, Santiago Martínez Bragado, and Sergio Ayala Miñano — who came to the United States through the Exponential Fellowship, a program that brings Europe’s top technical talent to the U.S. to build companies at the frontier of AI. Their backgrounds span both sides of the problem Traza is trying to solve. Jara Montes worked at Amazon and CMA CGM — one of the world’s largest shipping groups — at the intersection of operations strategy and supply chain optimization. Martínez Bragado built and deployed agentic AI at Clarity AI before joining Concourse (backed by a16z, Y Combinator, and CRV) as Founding AI Engineer. Ayala Miñano comes from StackAI, one of the fastest-growing enterprise AI platforms in San Francisco, where he was a Founding Engineer.

None of the founders carry the title of Chief Procurement Officer, a gap that the company acknowledges has occasionally surfaced in buyer conversations. Jara Montes’s response is characteristically direct: «Our work is the answer. The results we’re generating move that conversation quickly.» He noted that the company has senior procurement leaders serving as advisors who have run procurement at the scale of its target customers.

Base10 Partners, the lead investor, is a San Francisco-based venture capital firm that invests in companies automating sectors of what it calls «the Real Economy.» Its portfolio includes Notion, Figma, Nubank, Stripe, and Aurora Solar. Rexhi Dollaku, General Partner at Base10, framed the investment in emphatic terms: «Supply chain and procurement is one of the largest, most underautomated markets in the Real Economy. AI agents are finally capable of doing the work, not just assisting with it.» The supporting cast of investors reinforces the immigrant-founder narrative. Clara Ventures — founded by the executives behind Olapic’s $130 million exit — specifically invests in driven foreign founders building in the United States, and Agell adds operational credibility from building Chartboost into a $100 million revenue business in under three years as a Spanish founder in Silicon Valley.

Why $2.1 million may stretch further than it looks for an enterprise AI startup

At $2.1 million, this is a deliberately small round for a company selling to large enterprises with notoriously long procurement cycles. Jara Montes argues it goes further than it appears for structural reasons. «We leverage Europe as a tech talent hub, where we have a deep network of exceptional engineers — people who want to work at the frontier of AI but have far fewer opportunities to do so than their US counterparts,» he said. «We’re not just lean — we’re built to outcompete on capital efficiency while others are burning through runway trying to hire in San Francisco.»

The go-to-market motion is designed for speed to revenue. Proofs of value are scoped, time-bounded, and converted to paying partnerships. The company says it is not running 18-month enterprise sales cycles before seeing a dollar. The milestone for the next raise is explicit: more paying customers, meaningfully stronger annual recurring revenue, and a repeatable sales motion that makes the seed round, as Jara Montes put it, «an obvious conversation.»

Looking ahead, he outlined an ambitious three-year target: 20 to 30 large industrial enterprises in the U.S. and Europe running Traza across their procurement operations, with over a billion dollars in procurement spend flowing through the platform. Whether that vision is achievable depends on several interlocking variables — the pace at which AI agent capabilities continue to improve, the speed of enterprise adoption in a traditionally conservative buyer segment, and Traza’s ability to navigate the competitive gauntlet of incumbents adding AI features and well-funded startups attacking adjacent workflows.

But the underlying math may be on Traza’s side. In procurement, the money that disappears does not look like waste. It vanishes into inefficiency, missed obligations, unmanaged risks, and forgotten commitments — the kind of silent losses that no one tracks because no one has the bandwidth to track them. The traditional mandate of procurement, as currently configured, ends where the value gap begins: at signature. Traza is building an AI workforce that picks up where the humans leave off. For an industry that has spent decades losing $55 million at a time to the back office nobody watches, that might be precisely the point.