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WellSaid Labs Raises $10 M in Series A Round
CIO Review
Well Said Labs intends to use the funding to further AI and product development, scale go-to-market operations, and expand the workforce.
FREMONT, CA: Well Said Labs, the artificial intelligence text-to-speech technology company, recently announced a 10 million dollars Series A round led by FUSE, with participation from previous investor Voyager, along with Qualcomm Ventures LLC, and Good Friends. Given the historic year-over-year revenue increase and great client demand, the Series A was oversubscribed with VC interest. Well Said Labs intends to use the funding to further AI and product development, scale go-to-market operations, and expand the workforce.
“We have added AI Voice to the toolkit of thousands of content creators and their teams,” said Matt Hocking, CEO, Well Said Labs. “Our human-parity AI voice can be produced faster than real-time, and updated on-demand. Opening up new and exciting opportunities to ‘add voice’ where never before perceived possible. AI voice easily ensures every production can be created and updated efficiently at scale.”
Well Said Labs empowers content creators and product teams to create engaging voice content for infinite use-cases in streaming services, radio, programmatic advertising, digital marketing, and corporate training content, with a mission to offer businesses and brands the highest quality Text-to-Speech (TTS) service imaginable.
“Plain and simple, Well Said is the future of content creation for voice,” said Cameron Borumand, General Partner at FUSE. “This is why thousands of customers love using the product daily with off-the-charts bottom-up adoption. Matt and Michael have assembled a world-class team and we couldn’t be more thrilled to be a part of the Well Said journey.”
Well Said Labs have redesigned TTS to address the most difficult content development challenges businesses face and provide an easier approach for content creators—big or small—to create all of their desired material in one consistent voice that represents their brand. The Voice Avatar library from Well Said Labs gives anyone access to various read styles and tones that can be utilized in the performances. Brands may also develop their own AI Voice Avatars to their specifications, capturing the likeness, style, and distinctiveness of the voice required to deliver their messages in the most effective way possible.
“Content creators or product experience designers were previously faced with difficult tradeoffs between quality and scalability when using TTS tools or human voiceover. Well Said’s incredible voices, which are accessible through a studio application or a scalable API, removes the need to choose whether you want natural, lifelike speech or infinitely scalable and easily editable voice content. Well Said provides both and delivers it however your team would like to consume it,” said James Newell, Partner at Voyager Capital. “Creative teams have found it to be extremely useful when they need to produce multiple pieces of high-quality content in a consistent voice in hours instead of weeks.”
In the field of AI, creating natural-sounding speech from text is a “grand challenge” that has been a research aim for decades. Well Said Labs has consistently researched and made remarkable advancements in the quality, speed, and reliability of neural text-to-speech systems during the last three years. TTS from Well Said Labs was the first to reach human parity for naturalness across numerous voices on short audio snippets in June 2020.
“Recent developments in TTS technology using generative AI have enabled synthetic voices to sound very human-like, finding exciting new applications for voice including e-learning, advertising and news readers,” said Carlos Kokron, Vice President at Qualcomm Technologies Inc. and Managing Director at Qualcomm Ventures Americas. “Well Said Labs provides an industry leading product that generates highly accurate human-like voices. We look forward to working with Well Said Labs to help fuel the creator economy with human-parity AI voices across mobile and IoT.”
Creating natural-sounding speech from text is considered a “grand challenge” in the field of AI and has been a research goal for decades. Over the last three years, Well Said Labs has consistently researched and developed tremendous breakthroughs in neural text-to-speech systems’ quality, speed, and reliability. In June 2020, Well Said Labs’ TTS became the first to achieve human parity for naturalness on short audio clips across multiple voices.
“WellSaid’s team has applied deep technical expertise to build a platform that enables easy creation and editing of incredibly lifelike audio,” said Dave Gilboa of Good Friends and co-CEO War by Parker. “We see meaningful growth potential in the use of high-quality audio in giving brands the ability to communicate with customers and creators the ability to engage with audiences.”
Well Said Studio removes the complications that standard text-to-speech technologies present to creatives, making voiceover production, updating, and publishing cost-effective and simple. Well Said Labs’ core AI engine is accessible to product developers via real-time APIs, allowing them to power digital experiences with a dependable and scalable speech infrastructure.
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AI agents are exposing a problem that conventional workflow software has rarely solved. Many enterprises run essential work across SaaS platforms, integration tools, local scripts and shared spreadsheets. Agents are then expected to work across all of them, gather enough context and make safe decisions. The difficulty lies in the gap between what an agent can infer and what the business can actually control.
Point-to-point integrations move data but do not preserve the history of a process. iPaaS platforms connect systems, yet long-running work can still end up scattered across queues, callbacks, approvals and exceptions. For buyers, introducing agents is only part of the challenge. They also need a process that can show exactly what happened.
Workflow orchestration can provide that structure when it carries context along with the work instead of simply routing it from one system to another. Agents still need room to exercise judgment, but that judgment needs boundaries. A model might classify an email, interpret intent, retrieve missing context and recommend what should happen next. It should not have to work out the refund procedure or customer verification process from scratch every time a request comes in. Repeatable steps are less expensive to execute through deterministic logic and easier to audit. The agent can then handle the parts that require interpretation while established actions remain within versioned process logic.
“Agents can make decisions where judgment is required while the workflow handles repeatable actions.”
That separation is useful only if the business can see what happened in each workflow. Executives need a way to inspect the process template, runtime history, agent decision and failure path in one place. Once APIs, agents, human reviewers and external events are involved, ordinary system logs do not provide the whole picture. Buyers need to know which action ran, what data moved, what decision was made and what happened when a step timed out or had to be retried. Keeping that information with the process also makes automation easier to improve because performance data remains connected to the work that produced it.
The amount of engineering required to get there matters too. An orchestration platform has limited practical value if a company needs to build a large specialist team before it can put a useful process into production. Existing services and SaaS APIs should be composable into business logic that people can understand and change without rebuilding the entire integration map. A code-first approach is useful when software teams get version control, business reviewers can see the workflow as a visual graph, auditors can trace what happened and agents have a stable process map to work within.
The larger issue is ownership of the process, not simply how many tasks can be automated. Long-running workflows need to retain state, and agent decisions need to remain visible without requiring a model call at every step. Once the process is running, event-driven feedback can show where it needs improvement. The platform also has to work for organizations with different levels of software maturity. One team may be coordinating a large collection of microservices, while another needs custom workflow logic around ERP, CRM, field-service and workforce systems without having to wait for a vendor to add the functionality to its roadmap.
LittleHorse takes this approach with Saddle Command Center and its Business-as-Code model for building workflows across microservices, SaaS platforms, agents and human-in-the-loop steps. Agents can make decisions where judgment is required while the workflow handles repeatable actions. Individual instances remain traceable, and workflow event data can be published to Apache Kafka for analysis. Support for Java, Python, Go and C# also allows engineering teams to maintain the business logic without having to adopt a specialist workflow language. For enterprises working across disconnected SaaS environments or complex microservice estates, LittleHorse provides a practical way to give AI agents room to make decisions while keeping the surrounding process visible and controlled. ...Read more
Sage migration decisions often begin with a contradiction. Finance and IT teams want the subscription feel of SaaS, yet the applications they rely on still carry custom workflows, connected databases, reporting routines and partner-managed changes. A generic cloud host can move the server, but it may leave the business managing every handoff when access breaks or latency appears during a critical task. Month-end close, warehouse workflows, payroll access and reporting cycles leave little room for cloud experiments that behave well only under ideal conditions.
The weak point is usually not migration itself. It is the support chain that follows. Servers sit somewhere, a hosting provider manages the platform, the software publisher owns the application, a Sage consultant handles business logic and the internal team is left to coordinate the room. A single interruption then becomes a routing problem. Executives should favor a hosting model that reduces escalation layers without stripping away control over the ERP.
Control matters because Sage environments rarely behave like standard SaaS tenants. Updates, integrations, VPN links, reporting tools and adjacent applications may need business-specific treatment. Shared resources can look efficient until they limit troubleshooting or change windows. Dedicated virtual environments, network isolation, clear backup design and documented availability standards give leadership a firmer basis for risk decisions. The point is not more infrastructure for its own sake. It is a service model that keeps customization possible while making ownership clearer.
Ransomware risk and phishing exposure have changed the due diligence standard for hosted ERP. Sage access cannot be separated from identity controls, recovery routines, monitoring practices and response authority. A provider that only hosts the application may still leave security teams stitching together evidence after an incident. Before renewal terms are signed, buyers should test how backup frequency, network segmentation, disaster recovery design and incident escalation work in practice.
Cloud economics create a second trap. Public cloud flexibility can turn into variable outlay when workloads are poorly matched to the platform. Licensing shifts and Microsoft choices make architecture a finance issue as much as an IT issue. Lowest monthly price can be misleading when internal staff must manage exceptions or pull multiple suppliers into every problem. A stronger decision weighs contract predictability, application performance, recovery posture and the cost of internal coordination. Sage projects also require a provider that can work alongside ERP partners rather than displace them.
Against that buying logic, Cloud at Work is a premier choice for Sage cloud hosting. It model is built around Sage end users and fewer support handoffs, then extended that base into Azure and managed technology services where the customer environment demands it. Its portfolio spans Virtual Private Cloud, Infrastructure as a Service, Desktop as a Service, Managed Services and Managed Cybersecurity, giving buyers a path from hosted Sage to broader cloud management without changing accountability every time the environment expands. Dedicated resources, virtual firewalls, backup design and Sage-aware support match the pressures that matter most. For leaders who want Sage to feel closer to a managed service while preserving customization, Cloud at Work warrants serious consideration. ...Read more
Digital transformation remains a priority for organizations across Canada, but for many leaders, the challenge is no longer deciding whether to modernize. It is figuring out how to do it without disrupting the systems the business relies on every day.
Many organizations are operating in a mixed environment where old and new technologies must work side by side. Core applications that were implemented years ago still support critical operations. ERP and commercial off-the-shelf platforms have been customized over time to fit unique business processes. Data often lives in multiple systems and cybersecurity concerns continue to grow as organizations expand their use of cloud services, mobile applications and external partners. The result is a level of complexity that can make modernization feel risky, even when change is clearly needed.
This is why successful digital transformation rarely starts with technology. It starts with understanding the business. Leaders need a clear picture of which systems continue to deliver value, where inefficiencies exist and which investments will have the greatest impact. Organizations often spend too much money replacing systems that still serve an important purpose or implementing new solutions before fully understanding the long-term costs. The most effective transformation partners help organizations make informed decisions rather than pushing change for its own sake.
The same practical approach applies to emerging technologies such as artificial intelligence. While AI continues to attract attention, its success depends heavily on the quality of the data behind it. Organizations that struggle with fragmented information, inconsistent processes or weak governance often find it difficult to unlock meaningful value from AI investments. Data modernization, cybersecurity and system modernization are closely connected. Progress in one area often depends on getting the others right.
Security has become another defining factor in successful transformation initiatives. Whether operating in healthcare, education, municipal government or the private sector, Canadian organizations face increasing expectations around privacy, access management and accountability. Security cannot be treated as a separate project that follows modernization efforts. It needs to be built into planning and decision-making from the beginning. Strong governance, clear documentation and defined responsibilities help organizations reduce risk while giving leadership teams confidence that projects remain on track.
Execution is equally important. Many transformation initiatives struggle not because the strategy is wrong but because employees are left behind during the process. New systems, workflows and technologies only create value when people understand how to use them and why the changes matter. Clear communication, realistic timelines and strong change management are often the difference between a successful implementation and an expensive disappointment. For organizations operating across different regions of Canada, bilingual communication and local stakeholder engagement can further influence outcomes.
For organizations looking to modernize in a practical and manageable way, IPSG Technology offers an approach grounded in business realities rather than technology trends. The company combines custom application development, website modernization, cloud services, cybersecurity, data optimization and change enablement to help organizations navigate complex transformation initiatives with confidence.
Its strength lies in helping clients modernize ERP and COTS environments without unnecessary replacement, align AI initiatives with data readiness and incorporate security from the outset. By focusing on clarity, governance and measurable outcomes, IPSG Technology helps organizations move forward without losing sight of operational continuity, budget control and long-term business value. ...Read more
Mid-sized companies often reach a point where data volume has outgrown the reporting habits built around it. Sales systems, finance platforms, customer records and workforce tools accumulate information, yet decision-makers still wait for manually assembled reports or rely on partial views. The buying problem is rarely a shortage of software. It is the cost and coordination burden of connecting systems, preparing reliable data and turning it into useful action without building a large specialist team.
Platform selection should begin with the data foundation. Dashboards and AI models cannot compensate for inconsistent definitions, missing records or poorly governed pipelines. Executives need to know how a platform profiles and cleans data while preserving traceability from source to output. Integration also matters beyond the initial connection. A workable platform must support existing databases and business applications while reducing the amount of custom code required to keep those links current. Migration demands, refresh frequency and access controls deserve scrutiny before implementation begins.
The next pressure is time to proof. Many firms cannot justify a large upfront investment in engineers and data scientists before a use case has shown credible returns. A platform should let a business test a narrow problem and measure model accuracy before committing to broader deployment. Low-code workflow design can shorten that cycle, but ease of configuration must not remove oversight. Buyers should examine how knowledge bases and semantic layers are managed when model outputs affect staff decisions or customer-facing processes.
Access to insight presents a separate test. Static reports remain useful for recurring review, yet business leaders increasingly need answers that were not anticipated when a dashboard was built. Natural-language querying can reduce dependence on report backlogs, provided the platform grounds responses in governed company data and shows enough context for users to judge the result. Predictive functions should be assessed in the same manner. Forecasts are valuable only when teams can understand the inputs and monitor performance before connecting a prediction to a defined next step.
The final buying concern is service depth. Mid-sized firms may adopt a capable platform and still lack the people to design data models or maintain AI workflows. A provider should be able to supply targeted support without turning every change into a consulting project. Subscription or usage-based pricing can lower the entry barrier, though buyers should compare consumption controls and support terms carefully. The strongest fit will combine self-service tools with practical help around implementation and model tuning, backed by ongoing maintenance when internal capacity is limited.
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