Amberd.ai is a company that provides a private, LLM-native decision platform that enables enterprises to query internal structured and unstructured data using plain language to receive secure, actionable insights for boardroom decision-making. Pulse 2.0 interviewed Amberd.ai co-CEO Zaré Baghdasarian to learn more.
Zaré Baghdasarian’s Background
Could you tell me more about your background? Baghdasarian said:
“I’m a serial entrepreneur and technology executive with over 30 years of experience building and scaling enterprise technology companies. I hold a BSEE from Villanova University, a Master’s in Engineering Management from Santa Clara University, an Executive Management degree from UCLA Anderson, and an Executive MBA from the TRIUM program — a joint degree from NYU Stern, the London School of Economics, and HEC Paris.”
“I co-founded Monterey Networks, sold to Cisco Systems, where we built the industry’s first wavelength router, filed 18 IP technology patents, and delivered investors a 33x return in less than two years. I also co-founded the IRIS Group and have served as a venture investor and operating partner across many early-stage companies. Most recently, I co-founded and led Avata Intelligence, acquired by Procore Technologies. My career has consistently operated at the intersection of deep technology and real-world business outcomes.”
“My co-CEO, Mazda Marvasti, Ph.D., brings equally formidable expertise. Mazda is a technology executive and AI innovator with over 30 years of experience building large-scale analytics, cloud, and AI platforms. He co-founded Integrien, where he pioneered patented analytics algorithms for autonomous system intelligence, leading to VMware’s acquisition where he served as VP and CTO of the Cloud Management Business Unit. He holds over 25 patents on mathematical modeling of complex data and is widely recognized for applying AI and automation to operational decision-making at scale.”
“Together, Mazda and I bring complementary strengths — deep enterprise AI and data infrastructure expertise on his side, and a track record of building, scaling, and delivering exceptional investor returns on mine. That combination is the founding DNA of Amberd.”
Formation Of The Company
How did the idea for the company come together? Baghdasarian shared:
“Mazda and I created Amberd to address a recurring problem: Companies struggle with overly complex BI systems that fall short of delivering the insights and information executives need to make decisions. A typical BI implementation often takes 6 to 12 months just to get an intelligent view of structured enterprise data. Even after all that time and investment, the system still struggles to answer the business questions that mattered most, because those questions rely on all of a company’s data, not just the structured slice BI tools are built for.”
“When the dashboards were finally live, executives would ask complex questions in meetings and the dashboard simply couldn’t answer them. Static dashboards are designed for questions you anticipate — not the ones actually asked in a boardroom in real time.”
“That experience raised the question that became Amberd: there has to be a better way — one that works across all company data, structured and unstructured, without the limitations of static dashboards. Combined with the realization that public cloud AI was not viable for regulated enterprises due to privacy and compliance constraints, we had our founding thesis. Amberd was built so enterprises never have to choose between the power of AI and the safety of a locked-down data environment.”
Favorite Memory
What has been your favorite memory working for the company so far? Baghdasarian reflected:
“There are two that stand out. The first is the most foundational. Early on, Mazda and I and our three initial founding team members had a company kickoff barbecue that turned into one of the most unforgettable days any of us can remember — great food, great people, and some of the best brainstorming we’ve ever had. What made it truly special was where it happened: near a fortress called Amberd, which is where our company name comes from. Amberd means “Fortress in the Clouds” — and sitting near that ancient fortress, talking about building a secure private AI platform that lives entirely within an enterprise’s own walls, the name took on a whole new meaning. It felt like the mission and the moment were perfectly aligned. That day cemented not just the name but the culture and spirit of everything we were building together.”
“The second memory that stays with me is the first time an enterprise customer saw the platform work live on their own data. Watching them go from weeks of manual reporting to a real-time, conversational answer drawn from their own secure systems in seconds — that moment validates everything. It confirms we built something that genuinely matters in the real world.”
Core Products
What are the company’s core products and features? Baghdasarian explained:
“Amberd is a private, LLM-native platform that gives executives a single, decision-ready answer from across all their structured and unstructured enterprise data — without sacrificing privacy, governance, or control. It is built on a modular, agentic architecture combining specialized knowledge banks, intelligent information access, and compliance-first workflows that are fully auditable and explainable.”
“In practice, a business leader asks a plain-language question and receives a clear, sourced, auditable answer drawn from their own internal systems — databases, documents, and industry regulations — all in one place. For the first time, multiple databases and unstructured documents can be queried simultaneously in the context of industry-specific laws and regulations, delivering a single decision-ready answer executives can bring directly into the boardroom with confidence.”
“Critically, Amberd connects directly to existing enterprise data sources — databases, CRMs, ERPs, and document repositories — exactly where they already live. There is no requirement to first build a data warehouse or data lake, no ETL pipeline to construct, and no months-long infrastructure project before value is delivered. The platform is deployed entirely within the customer’s own environment, meaning sensitive data never leaves their control — a non-negotiable architectural requirement for regulated industries such as healthcare, financial services, legal, and government.”
Challenges Faced
Have you faced any challenges in your sector of work recently? Baghdasarian acknowledged:
“The biggest challenge in this space is trust. Enterprise leaders recognize the power of AI but struggle to integrate it into real operational workflows. Building AI internally is slow and costly due to legacy systems and limited IT capacity, while off-the-shelf tools fail to integrate deeply with an organization’s full data ecosystem. Leaders also don’t fully trust AI-generated answers when they cannot see the source or the reasoning behind them — and in regulated industries, that lack of auditability is a deal-breaker.”
“We tackled this head-on in three ways. First, we spent over two years doing real-world testing with enterprise customers before our public launch — building reliability before building scale, and ensuring the platform performed consistently across complex, messy, real-world data environments rather than clean demo datasets. Second, we solved the privacy barrier by keeping all data within the customer’s own controlled environment, with full audit trails and explainable, sourced outputs. Third, our 7-phase Solution Architecture onboarding process ensures data is properly structured, documented, and validated before executives ever ask a question, which dramatically reduces the risk of inaccurate or hallucinated responses. The result is a platform executives can trust and bring into the boardroom with confidence.”
Evolution Of The Company’s Technology
How has the company’s technology evolved since launching? Baghdasarian noted:
“Amberd was more than two years in development before we publicly launched, so the evolution began well before the market saw the product. In the early stages, the platform could query single structured databases with reasonable accuracy. Over time, we built out the modular, agentic architecture that now allows simultaneous querying across multiple databases and unstructured document sources, layered with industry-specific regulatory context that makes answers not just accurate but decision-grade.”
“The most significant technical leap was developing our data restructuring methodology — the process we now call Phase 5 of our Solution Architecture onboarding. We discovered early that enterprise databases were simply not built for LLM use, and that no amount of model sophistication could compensate for fragmented or poorly structured data. Building the tooling and methodology to restructure and optimize data in place — without requiring customers to migrate to a warehouse or lake — was the breakthrough that made the platform reliable in production rather than impressive only in demos.”
“We also confronted a deeper architectural challenge: solving the complex, multi-dimensional problem of delivering correct, trustworthy answers across diverse enterprise data environments — while actively avoiding hallucination — cannot be done with a single LLM or a single AI technique. To solve this properly, we built a Holistic AI Architecture that orchestrates multiple LLM models and combines different AI techniques dynamically, selecting the right approach for each specific problem being solved. This multi-model, multi-technique design is what gives Amberd the accuracy and reliability that single-model platforms simply cannot match at enterprise scale.”
Significant Milestones
What have been some of the company’s most significant milestones? Baghdasarian cited:
“Several stand out. First, completing over two years of real-world product development and testing with live enterprise customers before our public launch — that discipline of not rushing to market shaped the reliability and depth of what we ultimately built. Second, our formal launch in March 2026, which marked Amberd’s entry as a production-ready platform available to enterprise customers globally and validated by real-world deployments.”
“Third, landing CDS (Connected Dealer Services) as an early customer and demonstrating that our private AI approach works at the intersection of connected-vehicle data and dealer management systems — a complex, multi-source, real-world deployment that proved the platform outside of controlled conditions. Fourth, the development and codification of our 7-phase Solution Architecture onboarding methodology, which transformed what started as a bespoke client process into a repeatable, scalable system that now forms the foundation of both our competitive moat and our services revenue model.”
Customer Success Stories
Can you share any specific customer success stories? Baghdasarian highlighted:
“We have two compelling early customer stories. The first is CDS (Connected Dealer Services). They came to us because public LLMs were never an option — the sensitivity of their customer and vehicle data made that a non-starter. What Amberd gave them was a secure AI platform integrating directly with both their connected car data and their dealer management systems, two environments that had never been unified before. For the first time, CDS has an immediate unified view across both systems, enabling faster and better-informed decisions for their dealer partners.”
“The second is RL Jones, a customs brokerage and supply chain firm operating in one of the most complex regulatory environments in the world. Trade policy shifts, tariff changes, and refund mechanisms have reached new levels of complexity — most recently with U.S. Customs and Border Protection standing up a new system to process IEEPA tariff refunds within 45 days, leaving importers and brokers scrambling to validate data, reconcile entries, and quantify recovery opportunities.”
“RL Jones turned to Amberd to unify their fragmented data sources and transform tariff management and refund optimization into a competitive advantage rather than a compliance burden. Amberd’s ability to simultaneously query structured trade data and unstructured regulatory documents — in the context of Importer of Record compliance and IEEPA refund calculations — gave RL Jones the intelligence layer to identify recovery opportunities and act on them faster than their competitors. Navigating regulatory chaos is no longer just a cost of doing business for them — it is now a source of differentiation.”
Funding/Revenue
Are you able to discuss funding and/or revenue metrics? Baghdasarian revealed:
“Amberd is fully self-funded by Co-CEOs Mazda Marvasti and myself. That was a deliberate and considered choice. Self-funding allowed us to move with complete focus on building the right product for enterprise customers rather than optimizing for investor timelines or short-term growth metrics that external funding often demands. It also meant we could spend more than two years in real-world testing and refinement before launching publicly — a level of patience and product discipline that external funding pressure rarely permits, and that we believe is a meaningful competitive advantage.”
“We are not disclosing specific revenue metrics at this time. What we can share is that the platform is commercially deployed with paying enterprise customers today, and our revenue model is built on three complementary streams: platform ARR, a one-time Solution Architecture onboarding fee, and ongoing revenue from AI Managed Services — because Amberd is delivered as a fully managed AI service, meaning customers receive continuous value through ongoing management, optimization, and evolution of their AI environment rather than a static software deployment.”
Total Addressable Market (TAM)
What total addressable market (TAM) size is the company pursuing? Baghdasarian assessed:
“Our TAM analysis identifies three converging markets Amberd sits at the intersection of: traditional BI and analytics, decision intelligence, and unstructured data analytics. Combined, these point to a total addressable market of $283-343 billion by 2030 — a number that is larger than most people expect when they first look at this space.”
“This breaks down across four components. The core platform market represents $230-290 billion. The Solution Architecture onboarding services market adds $20 billion, managed services and retainers add $15 billion, and the SI partner and channel ecosystem adds $18 billion — bringing the total to $283-343 billion. Importantly, those last three components — the services revenue streams — are entirely absent from most competitive analyses of enterprise AI platforms.”
“Our Serviceable Addressable Market (SAM) is approximately $95 billion, with a 5-year Serviceable Obtainable Market (SOM) of $9-$14 billion.”
Differentiation From The Competition
What differentiates the company from its competition? Baghdasarian affirmed:
“Our real competition is not other AI startups — it is the traditional BI stack enterprises have relied on for years: Tableau, Power BI, Qlik, Looker, and the data warehouses and lakes that support them. These tools have a fundamental flaw: they require you to know the question before you can answer it. A dashboard can only show what was pre-configured weeks before the meeting where it gets presented. The moment an executive asks something outside that configuration, the answer is a two-to-four-week wait. That is delayed intelligence, not decision intelligence.”
“Beyond static dashboards, traditional BI only works with structured data, requires months of warehouse and data lake infrastructure before any analytics can begin, and takes 6 to 12 months to deploy. Amberd eliminates all of that. We connect directly to existing data sources — no warehouse, no lake, no ETL pipeline required. Amberd is the only platform that delivers real-time, plain-language intelligence across all enterprise data without the infrastructure overhead or the privacy tradeoffs.”
Future Company Goals
What are some of the company’s future goals? Baghdasarian emphasized:
“Our near-term focus is deepening deployment across our highest-value verticals — Automotive, Logistics, and IOT — while growing the enterprise customer base and expanding our Solution Architecture team to support larger and more complex onboardings. Each new vertical we penetrate deepens the regulatory knowledge layer within the platform, which in turn makes Amberd more valuable to every subsequent customer in that industry.”
“On the product side, we are continuing to develop the compliance and regulatory knowledge layers that make Amberd uniquely defensible in regulated industries, and expanding our cross-database integration capabilities to support even more complex multi-source enterprise environments. We are also investing in the tooling that makes our 7-phase Solution Architecture onboarding faster and more consistent across different client data architectures.”
“Long-term, our most significant goal is building a certified System Integrator (SI) partner ecosystem. Our 7-phase onboarding methodology is designed to be certifiable — third-party data consultancies and systems integrators can be trained and certified to deliver it on our behalf. That model extends our geographic reach and customer capacity without proportional internal headcount growth, and unlocks an estimated $18 billion in channel TAM. It is the mechanism that takes Amberd from a strong enterprise platform to a category-defining company at scale.”
Additional Thoughts
Any other topics you would like to discuss? Baghdasarian concluded:
“One thing worth emphasizing is the broader moment we are in. Enterprises are sitting on extraordinary amounts of data but remain paralyzed by fragmentation, infrastructure complexity, and a fundamental lack of trust in AI outputs. The companies that solve the last mile — turning distributed, multi-format data into boardroom-ready decisions while keeping it private, auditable, and explainable — will define the next decade of enterprise software. That is exactly the problem Amberd was built to solve, and we feel a real sense of urgency and responsibility around it.”
“I would also add that Mazda and I built this from a place of deep operator experience. We did not start with a technology looking for a problem. We started with a problem we had each felt personally, repeatedly, and expensively across multiple companies and industries — and we built the technology to solve it the right way, with the discipline to spend two-plus years testing it in the real world before bringing it to market. That grounding shows in the product architecture, in the Solution Architecture methodology, and in the early customer results we are already seeing. We are not building a demo. We are building the intelligence infrastructure layer for how enterprises will make decisions for the next decade.”
