Theories
Stay up-to-date on our team's latest theses and research
Stay up-to-date on our team's latest theses and research
Cryptoassets are becoming an important market, not just for developers but also for large financial players. In 2023, we saw this trend accelerating, germinating a need for financial institutions to have robust software and infrastructure. Today, cryptoassets are worth $2.5t.
Through our research and due diligence with market players like exchanges, ETF providers, prime brokers, auditors, and various financial service companies, we heard time & again the most vital component of the institutional software stack was the data and analytics layer.
Access to accurate & timely data is one of the biggest problems in Web3. As the number of blockchains & transactions increase, the volume of on-chain data is exploding. Because any web3 developer can write their own code via smart contracts, normalizing data across web3 tokens, coins, & swaps is a Gordian knot - and an opportunity for a startup to make sense of it all.
Financial analysts, app developers, and institutions building on these systems require sophisticated, real-time data to make informed decisions, power applications, and manage business reporting/accounting. These users and builders must have insights into wallet activities, smart contract interactions, and cross-chain events to develop high-performance systems, prevent fraud, and more.
Until now, data services have lacked the depth, speed, and reliability that world class institutions and application developers demand.
Allium provides trust and transparency to the blockchain industry. In our market research, we found that Allium consistently surfaced as the most responsive and accurate data provider. Allium provides the most robust data pipeline management solution in Web3. We are thrilled to lead their $16.5 million Series A, with participation from seed investors Kleiner Perkins and Amplify Partners.
Allium offers quick and simple enterprise-grade blockchain data for leading institutions and companies like Visa, Stripe, Uniswap Foundation, and Phantom. Allium's comprehensive suite of data products, covering 40+ blockchains and over 100 schemas, delivers high-quality blockchain data wherever and however customers need it. This is achieved through two powerful APIs:
In addition to these APIs, Allium's robust data infrastructure serves as the backbone for a wide range of use cases:
Institutions want to serve users and consumers wherever they are, independent of the chain or network support. Customers do not want fragmentation of their data sources and vendors. Engineers today are bogged down by time-consuming manual and operational work on new chains, new schemas, new delivery cadences, and new delivery mechanisms. With Allium, you can pick your chain, choose your schema, determine your desired freshness, and finalize the destination. Allium monitors uptime and provides data redundancies, delivering this data seamlessly into any data surface: Postgres, Kafka, BigQuery, etc. Enterprises and protocols can thus integrate their existing intelligence and data systems with on-chain data seamlessly and with a high level of confidence.
About Allium
Ethan Chan and Cheng Han Lee founded Allium with the vision for a new approach to blockchain data management. The co-founders, whose relationship spans over a decade, bring extensive experience in large-scale data systems from their time at companies like Primer, Microsoft, Meta, and Poynt. They’ve built for and sold to Fortune 500 companies, Big Tech, and government entities. To learn more, visit www.allium.so.
In a recent post, we described the new era of composable software.
Most knowledge workers spend their day flipping back and forth between isolated pieces of software. Manual work in emails, spreadsheets, and text documents fill the gaps between them.
Large enterprise platforms like Salesforce require hundreds of hours of professional services to configure, and hundreds more to modify. As a result, they are brittle and can’t easily respond to changes to the business.
We believe that we are at the start of a major shift to composable software platforms. These platforms, enabled by modern infrastructure and AI-based automation, will allow companies to configure applications, workflows, and data to their specific business needs.
But what will composable software actually look like in practice? As we’ve dug deeper, it’s clear the composable software movement will not be monolithic. We think there are four key ways companies will make their software more composable in the coming decade.
In nearly every software category, we expect existing systems of record and vertical software will be swapped out for new products built around composability.
Composable, AI-powered products will create magic-feeling automations that would never be possible with an existing platform. From there, customers can design new workflows and integrate additional software without relying on costly services. Value and stickiness will increase as customers build operations around the platform.
Taking on these incumbents requires substantial upfront effort. Companies need to replicate nearly all of existing products’ functionality to be a viable option, while also designing data models, domain-specific languages, and application components that are extensible.
Rippling has dominated in the Human Resource Information System (HRIS) category with a compound product suite. We believe there is an opportunity – and need – to go even further with configurability in more operationally-intense categories like ERP, CRM, and SCM software. Some companies we’ve seen taking this approach are Doss in manufacturing; Basis in accounting; and Attio, Clarify, Twenty, and Kepler in CRM.
Where composable systems of record and vertical software will target the nodes of today’s software graphs, Robotic Process Automation (RPA) tackles the edges between them – the manual workflows of copying and pasting, writing, and double checking information across applications.
RPA is not a new concept, but until recently technology just wasn’t capable of actually replacing human work. LLM-powered automation is different because it can replicate human thinking/classifications and can arbitrarily transform data.
LLM-powered RPA platforms are different enough from legacy RPA that there is an opportunity for new companies to beat out incumbents who bolt on AI features.
Traditional RPA platforms are designed for static tasks like “when a customer submits a complaint form, copy it into a Jira ticket”. Technical moats are mostly driven by integrations.
LLM-powered RPA platforms must be designed for complex multi-step and branching workflows, like “when a customer submits a complaint form, search for similar issues in our database, add the new feedback, then either auto-reply or forward the message to the right department.” Moats will shift to product experience and automation/agentic orchestration.
RPA platforms overlap somewhat with the previous category: for example, a company might consider building out sales automation workflows using either an extensible CRM or an RPA platform. Some differences are:
Some next-gen RPA platforms include Cassidy, Induced, Manaflow, Orby, Relay, Respell, and incumbents like N8n, UiPath, and Zapier.
The most direct reason why today’s software products don’t work together is that it’s too complex to integrate them. The platform might have a sprawling data model that’s hard to parse; it could be a legacy platform without modern APIs. Key information might only be accessible in a generated PDF, or manually typed into a comment field.
Fortunately, one of the most powerful capabilities of LLMs is that they can transform arbitrary data. Whether the input is unstructured (e.g. emails), semi-structured (e.g. PDFs), or structured (e.g. CSVs), LLMs can turn them into reliable structured outputs that can be built into applications.
While LLM outputs are non-deterministic, transformation tasks can typically be done with high accuracy, and are well-suited to fine-tuning. LLMs can also handle variance in input data that would break a deterministic transformation.
As models continue to get smaller, faster, and cheaper, LLM transformations are viable for more real-time and high-volume use cases, like powering transactional marketplaces based on email inputs.
This infrastructure is a key enabler of composable software platforms: as the cost of building and maintaining integrations drops dramatically, it will be possible to couple together more and more software platforms to build workflows around them. They will be especially critical infrastructure in the domains that rely on legacy systems – like finance, healthcare, supply chain/logistics, manufacturing, retail, and real estate.
Some companies building AI-powered transformation infrastructure include Bem, Extend, Klarity, Narrative, Roe, Sensible, and Trellis.
The vast majority of businesses don’t have substantial software engineering capabilities in-house.
These companies will realize many benefits of AI by purchasing AI-enabled software. For example, sales teams will become more efficient with automated prospecting and outreach tools.
They’ll also implement new AI-powered RPA workflows. Customer success teams can use a next-gen RPA tool to ingest customer feedback, classify it by feature or problem type, and route it to the right team for resolution.
But many domain or company-specific use cases require deeper integrations than out-of-the-box software can provide, or more complex workflows than you can build in a UI. Imagine a utilities company that wants to ingest customer outage reports and coordinate repair teams. This could require more complex mapping UIs, routing algorithms, and integration with their ERP and operational software. It probably exceeds what you can buy out-of-the box or configure with an RPA platform or app like Retool.
As The New York Times reported, large consulting firms like McKinsey and BCG have seen appreciable portions of their revenue driven by AI projects. Over time, we expect projects will shift from AI strategy to implementation. This will drive a majority of work to systems integrator firms designed to configure and implement software to address these more complex use cases.
If AI dramatically improves software development efficiency, the market for custom applications will explode. Most companies hire development services firms to configure their ERP or CRM, but wouldn’t ask them to build custom applications – it would be too slow and costly, as these firms are incentivized to sell more hours of work. We expect there will be a set of new AI-enabled development services firms designed to rapidly build and ship new applications on a fee-for-service basis. These will open up a market that is not well-served by the large existing system integrators.
Some new AI-enabled development services firms include Agnetic, Cogna, and Isoform.
Composable software will come in bits and pieces. Walled-garden applications will be replaced with extensible platforms, business users will automate cross-application workflows, and AI will power integrations. It will be easy and cheap to commission fully-custom applications built by AI-enabled services firms.
While their implementation looks different, all these types of composable software work towards the same objectives: better integration of data, and application logic that is tailored for each business versus one-size-fits-all. The result will be massive operational efficiencies – software will automate busy work and let employees focus on what’s most important.
Here’s a (non-exhaustive) map of startups helping drive the composable software revolution:
If you’re building in this space, we’d love to chat at info@theory.ventures!
