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Gradient raises $10M to let companies deploy and fine-tune multiple LLMs

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Gradient, a startup that allows developers to build and customize AI apps in the cloud using large language models (LLMs), today emerged from stealth with $10 million in funding led by Wing VC with participation from Mango Capital, Tokyo Black, The New Normal Fund, Secure Octane and Global Founders Capital.

Chris Change, Gradient’s CEO, co-founded the company alongside Mark Huang and Forrest Moret several months ago while working on AI products at Big Tech firms including Netflix, Splunk, and Google. The trio came to the realization that LLMs like OpenAI’s GPT-4 could be transformative for the enterprise, but believed that getting the most out of LLMs would require creating a reliable way to add private, proprietary data to them.

“Traditionally, teams have focused on improving a single, generalist model — and existing solutions support this model,” Chang told TechCrunch via email. “This is largely because it was too complex to manage multi-model systems. However, relying on a single model is suboptimal because there’s an inevitable tradeoff in task-specific performance.”

Chang, Huang and Moret designed Gradient, then, to make it easier for teams to deploy “specialized” and fine-tuned LLMs at scale. The platform runs in the cloud, allowing an organization to develop and integrate as many as “thousands” of LLMs into a single system, Chang says.

Gradient customers don’t have to train LLMs from scratch. The platform hosts a number of open source LLMs including Meta’s Llama 2, which users can fine-tune to their needs. Gradient also offers models aimed at particular use cases (like data reconciliation, context-gathering and paperwork processing) and industries (like finance and law).

Gradient can host and serve models through an API a la Hugging Face, CoreWeave and other AI infrastructure providers. Or it can deploy AI systems in an organization’s’ public cloud environment, whether Google Cloud Platform, Azure or AWS.

In either case, customers maintain “full ownership” and control over their data and trained models, Chang says.

“The barriers to development are far too high for AI today,” he added. “Building high-performance, custom AI is inaccessible due to the high complexity and cost of setting up the necessary infrastructure and developing new models. We’ve seen that the vast majority of businesses understand the value AI can bring to their business, but struggle to realize the value due to the complexity of adoption. Our platform radically simplifies harnessing AI for a business, which is a tremendous value-add.”

Now, you might ask — like this reporter did — what sets Gradient apart from the other startups engineering tools to pair LLMs with in-house data? And what about the many other companies already customizing LLMs for enterprise clients as a service? It’s a reasonable question.

Take a look at Reka, for example, which recently emerged from stealth to work with companies to build custom-tailored LLM-powered apps. Writer lets customers fine-tune LLMs on their own content and style guides. Contextual AI, Fixie and LlamaIndex, which recently emerged from stealth, are developing tools to allow companies to add their own data to existing LLMs. And Cohere trains LLMs to customers’ specifications.

They’re not the only ones. OpenAI offers a range of model fine-tuning tools, as do incumbents like Google (via Vertex AI), Amazon (via Bedrock) and Microsoft (via the Azure OpenAI Service).

Chang makes that case that Gradient is one of the few platforms that lets companies “productionize” multiple models at once. And, he asserts, it’s affordable — the platform is priced on-demand such that users only pay for the infrastructure they use. (Larger customers have the option of paying for dedicated capacity.)

But even if Gradient isn’t drastically different from its rivals in the LLM dev space, it stands to benefit — and is benefiting — from the massive influx in interest around generative AI, including LLMs. Nearly a fifth of total global VC funding this year has come from the AI sector alone, according to Crunchbase. And PitchBook expects the generative AI market to reach $42.6 billion in 2023.

“Gradient makes it much easier to develop complex AI systems that leverage many ‘expert LLMs,’” he said. “This approach ensures the AI system consistently achieves the highest performance for each task, all in a single platform … Our platform is designed to make it extremely easy for teams to deploy specialized LLMs, purpose-built for their specific problems, more effectively.”

Gradient claims to be working with around 20 enterprise customers at the moment with “thousands” of users combined. Its near-term goal is scaling the cloud backend and growing its team from 17 full-time employees to 25 by the end of the year.

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Bfree, a Nigerian startup enabling lenders recover debt ethically, gets $3M backing

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Bfree, a tech-enabled debt collection startup based in Nigeria, was founded to automate and introduce ethical debt recovery processes after its founders witnessed the use and adverse effects of aggressive retrieval techniques, such as incessant calling and debt-shaming, by predatory digital lenders.

After its launch in 2020, the startup introduced a number of scalable debt recovery methods including a self-service platform, which allows borrowers to set up new payment plans, and conversational AI tools (chatbots and callbots), as part of its collections-as-a-service offering. These tools ensure humane after-sales services for borrowers, and action based on behavioral and financial data.

Over the years, its customer-base has grown to include some of the major banks in Ghana, Kenya and Nigeria, where it plans to continue scaling, backed by the $2.95 million fresh funding it has just secured in a round led by Capria Ventures. Angaza Capital, GreenHouse Capital, Launch Africa, Modus Africa, Axian CVC and a number of angel investors, also participated in the round that brought the total funding raised to $6.5 million, including last year’s undisclosed $1.1 million bridge round.

Julian Flosbach (CEO), who co-founded the startup with Chukwudi Enyi (COO) and Moses Nmor (CPO), told TechCrunch that while Bfree started out with digital lenders, which he says are quick to adopt its products, they currently only work with a handful of them, as their key focus is on banks, which contribute up to 70% in revenues.

“Because of the immense pressure to increase our margins, we essentially had to either increase pricing or let go of a lot of smaller customers,” said Flosbach, adding that it makes business sense to work with banks because of their large loan portfolios compared to digital lenders. The startup currently serves 14 customers, although it has worked with 45 since launch.

Bfree says 92% of its interactions with customers are fully automated, but has maintained a call center, manned by a small team, for when customers call or for follow-ups that require phone calls. It also launched a loan collection management SaaS dubbed Workflow, which targets companies with in-house collection teams or those that are not keen to outsource.

The startup is arguably the only tech-enabled credit recovery company across Africa, where collectors continue to heavily rely on traditional options like call centers to follow-up on settlements.

Bfree to create secondary market  for loans

Its current loan portfolio stands at over $400 million, out of which it has managed to collect 12.5%.

The startup also plans to create a secondary debt market, to allow third-party investors like hedge funds, looking to diversify their investments, to buy non-performing loans (NLPs) from banks in Africa. Debt buyers purchase loans from banks at a fraction of the debt’s face value, and make profits from collection. Banks sell NLPs to minimize their risk, manage loan portfolios and free up funds.

“We collect so much data of borrowers, especially defaulting borrowers. We were able for the first time to actually develop an algorithm that can value these assets. We can predict how much is a loan that has not been paid back, let’s say for 90 days; how likely is it going to be paid back over the next one year. Then we go to banks and buy these assets and take them off their balance sheets, allowing them to offload the risk,” said Flosbach.

He added that they also have an analytics solution for banks to help them gain insights into secondary debt markets.

Commenting on the investment, Susana García-Robles, managing partner at Capria Ventures, a Global South specialist VC firm investing in applied Generative AI, said: “The advent of generative AI provides a pathway for more efficient scaling, enabling the company to expand across the continent at a reduced cost. Bfree is well-positioned to play a crucial role in improving accessibility and mitigating risk in financial services.

“We foresee the growing prominence of credit management and are confident that Bfree will spearhead the creation of a secondary market on the continent for distressed assets. Bfree has secured significant partnerships with top-tier banks and fintechs, affirming the effectiveness of its product and reinforcing our belief in its potential to transform credit collection in Africa,” said García-Robles.

As the startup diversifies its offerings, it has also slowed down its aggressive expansion plans announced two years ago, when venture capital flowed freely and “growth at all costs” was the mantra, to concentrate on its three key markets in Africa. This is upon the awareness of varying market dynamics, and the realization that every market needs different approaches and products.



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MoneyHash raises $4.5M for its payment orchestration platform serving merchants in MENA

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The payment landscape in the Middle East and Africa (MEA) region is marked by significant fragmentation, with numerous payment providers and methods in each country, evolving regulations and diverse customer preferences. This complexity is further compounded by challenges such as payment fraud, low checkout conversion rates and high transaction failure rates.

Although the COVID-19 pandemic accelerated the adoption of digital payments in the region, infrastructure development remains inadequate. Payment failure rates are three times higher in the MEA region than the global average, and fraud rates and cart abandonment exceed those of other regions by more than 20%. This presents a challenge for merchants, who often perceive payments as a cost and risk center rather than a strategic enabler.

Payment orchestration platforms streamline payment processes for merchants through unified payment APIs. Egyptian fintech MoneyHash, one of such in Africa and the Middle East, has raised $4.5 million in seed investment, money it plans to use to further invest in its technology and growth across the region. This comes two years after the startup secured $3.5 million in pre-seed.

Nader Abdelrazik, co-founder and CEO of MoneyHash, highlights that 10% of all payments processed in the MEA region are digital, placing MoneyHash uniquely for a growth phase that the region will inevitably experience over the next decade. However, navigating this burgeoning payments market will demand patience and a commitment to continuous learning.

As merchants or companies launch their platforms, they often start by collaborating with one or two payment processing providers. As their operations grow and expand into multiple regions, they onboard additional payment providers to meet their evolving needs. However, integrating different payment stacks presents significant challenges. Besides the operational inefficiencies and technical complexities, in-house tech teams may take several weeks to complete these integrations. In Africa and the Middle East, these challenges are amplified by variations in payment methods, currencies and the isolation between countries.

MoneyHash payment integration catalogue. Image Credits: MoneyHash

MoneyHash’s product includes a unified API to integrate pay-in and pay-out rails, a fully customizable checkout experience, transaction routing capabilities with fraud and failure rate optimizers and a centralized transaction reporting hub. This is complemented by tools enabling various use cases such as virtual wallets, subscription management and payment links. Fintechs such as Revio, Stitch, Credrails and Recital are similar players in the payment orchestration space.

In an email interview with TechCrunch, Abdelrazik shared insights into MoneyHash’s collaboration with merchants over the last four years. For one, he claims that payment failure rates across the region vary significantly, and relying solely on averages can be misleading. While the typical figures are around three out of 10 payments failing on average, the reality differs widely among businesses, he said. For some, it may be as low as one out of 10, while for others, it could be as high as five or six out of 10. Additionally, these figures do not include customers who abandon the checkout process voluntarily before making a payment. The CEO also noted that most of its customers don’t know much about the complexity of payments and, many times, are not aware that most leakages they have in payments are fixable.

Furthermore, merchants are expanding much faster than their partner payment service providers (PSPs). These PSPs operate under stringent regulations, making the rollout of new products and customizations slower than the merchants’ growth trajectory. As a result, MoneyHash has intensified its collaboration with PSPs, particularly those catering to enterprises and prioritizing customer requirements.

“Businesses appreciate the large network of integration we have not just for coverage but for expertise. When they know that we executed all these integrations in-house, they appreciate the team’s expertise and depth of knowledge and leverage our team to navigate difficult questions in payments. They know that working with us makes them future-proof,” noted Abdelrazik, who founded MoneyHash with Mustafa Eid.

“That means team expertise is key for us. Most of the time, we hire exclusively with payments and/or tech backgrounds, even in non-technical positions. We saw massive effectiveness in building a team where customers trust their knowledge and expertise in something specialized and critical like payments.”

Following a beta launch in 2022, which garnered the participation of key regional players like Foodics, Rain and Tamatem, MoneyHash introduced its enterprise suite last October, targeting large enterprises. Over the past year, the fintech, which integrates with various payment gateways and processors, including Checkout, Stripe, Ayden, Amazon Pay, Tap and ValU, claimed to have expanded its network of integrations, tripled its revenue and increased its processing volume by 3,000%.

At present, MoneyHash boasts 50 active paying customers. It does not offer free tiers; most customers accessing its sandbox without payment are potential clients in the assessment stage, numbering over 100. The payment orchestration platform levies a combination of SaaS and transaction fees, commencing at $500 + 0.4%. SaaS fees increase while transaction fees decrease significantly for large enterprises due to volume, Abdelrazik explained.

MoneyHash’s seed round was co-led by COTU Ventures and Sukna Ventures, with participation from RZM Investment, Dubai Future District Fund, VentureFriends, Tom Preston-Werner (GitHub’s founder and early Stripe investor) and a group of strategic investors and operators.

Speaking on the investment, Amir Farha, general partner at COTU, said his firm believes that the full potential of digital payments in MEA is yet to be realized and MoneyHash’s platform can catalyze the growth of digital payments across the region, enabling both global and local merchants to tap into new revenue streams. “We are thrilled to renew our support to a team that has consistently demonstrated superior execution, not just in securing top mid-market and enterprise customers, but also in expanding value across the entire chain, even under challenging market conditions,” he added.



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Snowflake CEO Frank Slootman stepping down — and Wall St hates it

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Apparently Frank Slootman, the veteran tech executive, was popular with investors, at least judging from their reaction that he will be stepping down as CEO of Snowflake. The company stock price has plunged more than 24% in after-hours trading on the news.

Slootman will retreat into the role of chairman of the board, while Sridhar Ramaswamy, former head of Google Ads, who came to the company when it bought AI search engine Neeva last year, will take over as chief executive.

Lost in today’s executive shuffle was the company’s earnings. It reported revenue of $738 million, up a healthy 33% year over year with guidance for the next quarter of between $745 and $750 million, with growth of 26-27%.

Slootman came on board in 2019, taking over for veteran executive Bob Muglia, and was charged with taking the company public the following year. Over the last year, the stock has done well, up around 50% (the exact amount is hard to tell with this afternoon’s downward spiral), as many tech stocks recovered from 2022 doldrums.

He was famously well-compensated, with a base salary of $375,000 and a rather attractive stock option. In fact, Fortune reported that the chief executive was making an eye-popping $95 million a month at one point.

He raised eyebrows in 2021 when he told a reporter that diversity shouldn’t override merit, and eventually walked back those comments after a negative reaction from industry peers.

Prior to coming to Snowflake, he spent six years as chairman and CEO at ServiceNow. With all that cash, perhaps he’ll retire and enjoy his money.



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