AI innovations have long promised productivity at scale, powered by breakthroughs in underlying technologies such as large language models (LLMs), aiding state-of-the-art applications to reason with remarkable fluency. Yet as AI adoption deepens, the constraint is no longer what models can do, but the data science and analytics foundations they depend on. Across industries, the data infrastructure feeding modern AI still resembles digital filing cabinets. Critical information remains scattered across platforms and disconnected tools, while data reaching sophisticated models is often stripped of context before inference begins.

In 2025, that realization created momentum for a new class of companies focused on making enterprise data usable at scale. Unstructured is streamlining fragmented enterprise documents, transforming PDFs, slides, emails, and other unstructured content into context-preserving inputs that AI agents can reliably reason over. In regulated industries, Basil Systems and 3E are reframing compliance and safety as large-scale data unification challenges, aggregating hundreds of millions of records to surface risk signals earlier and with traceable evidence.

Many AI systems still rely on batch data ingestion, introducing delays that limit responsiveness in fast-moving environments. Chalk is collapsing the distance between notebook experimentation and millisecond production inference, while Statsig is code experimentation frameworks directly into the software development lifecycle so that every product change becomes measurable at deployment. Feedzai is transforming risk detection through its data orchestration layer and federated learning that enables banks to collaborate against financial fraud without exposing customer data.

Underneath these shifts, the infrastructure powering data systems is also being re-architected. DataPelago is redesigning execution engines to utilize GPUs across analytics and AI workloads, and Pravāh is modeling electric grids as living graphs that learn continuously from fluctuating renewable inputs. Synchron and Pathway are extending this shift, building neural data interfaces and AI architectures that can learn and adapt continuously after deployment.

1. Unstructured

For standardizing preprocessing of unstructured data for agentic AI

For all the progress in generative AI, most enterprise systems still struggle with a basic mismatch: modern AI models reason fluently, but the data they depend on arrives fragmented, flattened, or stripped of context long before inference begins. Unstructured transforms real-world documents—PDFs, slides, emails, scans, and reports—into data that enterprises can reliably use in retrieval, search, and agentic AI systems. In the past year, the company has expanded beyond document parsing into a full preprocessing layer for generative AI.
Its platform now supports 68 file types, more than 30 enterprise connectors, and layout-aware transformations that preserve structure, hierarchy, and meaning. A new auto-orchestration system dynamically selects the optimal processing strategy on a page-by-page basis, balancing cost, speed, and accuracy without manual tuning. And a new Model Context Protocol server has further embedded Unstructured directly into AI workflows, enabling models and agents to process data using natural-language commands. The platform is now relied on by 82% of the Fortune 1000 and is deployed across commercial and public-sector environments where reliability and compliance matter most. As AI systems become operational dependencies rather than experiments, Unstructured is making data readiness a solved problem.

2. Basil Systems

For transforming fragmented life sciences data into AI-driven intelligence

In life sciences, safety failures don’t arrive as clear warnings. They accumulate quietly across inspections, labeling updates, regulatory filings and more—data streams that are rarely analyzed together. Basil Systems aims to help unify those fragments through its platform, which features more than 600 million regulatory, clinical, and post-market records and applies domain-tuned AI to expose risks earlier and with far greater clarity. The platform has moved beyond search and monitoring into prediction. Its agents automate work that once required weeks of expert review, enabling teams to assess regulatory gaps, compare labels, monitor competitors, and detect post-market risks earlier. Crucially, Basil’s models estimate recall likelihood and probable root causes, linking safety signals to materials, components, manufacturing steps, or patient subgroups while maintaining full traceability to source data.

The company has deployed the platform at four of the five largest global MedTech companies, including Johnson & Johnson, Medtronic, and Baxter, and is in trials at global pharmaceutical firms. Annual revenue is tracking toward $6 million, with sustained double-digit monthly expansion. As regulators and manufacturers push for earlier intervention and stronger evidence, Basil is becoming a shared intelligence layer helping to justify decisions clearly and prevent problems before they reach patients.

3. Pravāh

For building AI-native intelligence to reduce CO2 at scale

Electric grids were never designed to reason, only to follow deterministic rules in a world that behaved predictably. However, renewable intermittency, distributed energy, climate-driven demand spikes, and electrification have turned grid operations into a game of uncertainty—one that utilities are still managing with tools built for another era. Rather than layering analytics onto legacy control systems, Pravāh’s platform treats the grid as a living graph. It uses AI-driven grid digitization and real-time inputs to model how power actually flows across complex energy systems. This creates a continuously learning model of how power actually flows.

In deployments across India and Europe, Pravāh’s topology-aware forecasting improved short-term and day-ahead accuracy by 35 to 40%, helping reduce unintended generator output reduction incidents by 18% and lowering thermal generation by 11% during renewable ramps. Its power-flow simulator and multi-agent reinforcement learning layer has cut congestion by 30%+ in stress tests. All of this converges in Pravāh OS, a graph-native digital twin that unifies diverse data inputs into a single decision layer. Today, Pravāh’s systems span 3.8 million kilometers of grid infrastructure, serving more than 120 million customers.

4. DataPelago

For creating a platform that eliminates AI bottlenecks

As AI models grow more capable, a previously hidden constraint has become impossible to ignore: the data engines feeding them were never designed for an accelerated world. Most enterprise systems are still optimized for CPUs, batch jobs, and static analytics, forcing organizations to bolt GPUs onto software stacks that can’t fully leverage them. This creates a disconnect between what companies want AI to do versus what their infrastructure can support. DataPelago addresses this disparity with Nucleus, a universal data processing engine that unifies batch processing, streaming, analytics, and AI workloads within a single, hardware-aware execution stack. It helps dynamically distribute work across CPUs, GPUs, and other kinds or processors—maximizing utilization while collapsing multiple systems into one. In benchmarks, the approach has reportedly delivered up to 36.8× faster performance on Nvidia GPUs compared with cuDF, an open-source library, producing 3 to 4× workload acceleration and up to 40% lower total cost of ownership in production environments.

A Fortune 100 retailer reported up to 70% cost reductions on petabyte-scale ETL (Extract, Transform, Load) workloads, while other deployments cut job runtimes in half without changing underlying infrastructure. By eliminating redundant data movement and consolidating analytics and AI pipelines, Nucleus is helping to make large-scale AI more economically and operationally viable.

5. Feedzai

For elevating financial safety with an AI platform

Financial fraud and scams are now being developed collaboratively, refined with generative AI, and deployed across borders in hours. Yet, most banks still fight them alone, relying on siloed data and slow, manual review. Feedzai is betting that the future of fraud defense works more like a network than a fortress. The company operates one of the world’s largest AI-native fraud platforms, analyzing over $8 trillion in payments every year to safeguard 1 billion consumers in real-time.

In 2025, Feedzai furthered this mission by acquiring Demyst and introducing a data orchestration layer that integrates data from hundreds of sources worldwide into a single decision-making system. This helped account-opening times fall from days to under 60 seconds, manual operations drop by 65%, and banks approve 23% more legitimate customers without increasing risk.
Feedzai’s most consequential move, however, was launching Feedzai IQ—a federated learning network that allows banks to share fraud intelligence without ever sharing customer data. The system delivers 4× better fraud detection with 50% fewer false positives, creating a privacy-preserving feedback loop that improves as threats evolve. As AI enables financial crime to occur faster and more cohesively, Feedzai is helping risk infrastructure systems keep pace by collaborating in real time and scaling across global payment networks.

6. Chalk

For accelerating machine learning with a platform that compiles code in milliseconds.

Most machine learning models often rely on overnight processing of features,  resulting in stale data usage. Chalk’s data platform eases this pain point by helping to compute features in real time, offering sub-5 millisecond latency without requiring teams to rebuild their systems.

The platform’s core technical breakthrough is a Symbolic Python Interpreter, which automatically transpiles Python code into optimized C++ so that data scientists can deploy notebook code directly to production. Behind the scenes, Chalk’s resolver-based execution engine combines storage, compute, and intelligent query planning to build features on demand, even from signals that didn’t exist seconds earlier.

In 2025, Chalk raised a total of $60 million and reached a $500 million valuation, signaling growing market confidence in its ML infrastructure. Powering roughly one-third of U.S. debit card transactions for real-time fraud detection. It has enabled companies like Whatnot to process more than 300 million signals per second, cutting decision latency from hours to milliseconds while reaching 99% personalization coverage. Chalk is helping to reshape the infrastructure layer, proving that machine learning needs more than better models to meet escalating speed demands.

7. Statsig

For transforming product development with AI

As AI helps write modern software code faster than ever, product teams are often left guessing which changes actually matter. Instead of considering experimentation as a distinct analytics task, Statsig assists in integrating measurement as a part of the software development and deployment process.
The product’s warehouse-native system integrates feature flags, A/B testing, analytics, session replay, and release management to analyze every product change as soon as it is deployed. In 2025, Statsig further bridged the gap between code and causality. The platform now lets teams trace behavior across identities, measure experiment exposure explicitly, and overlay product changes on performance data—reducing noise and revealing what actually drives results. Moreover, the platform’s Metrics Control Protocol lets AI coding assistants automatically create experiments and instrumentation as engineers write code, turning measurement into a default behavior rather than an afterthought.
Today, Statsig processes more than 1 billion events annually for customers, including OpenAI, Notion, and Brex, helping process more than one billion events annually. Teams report cutting A/B decision cycles by seven days and confidently shipping up to 10× faster.

8. Synchron

For aiding people with paralysis by turning brain signals into data

For decades, brain–computer interfaces (BCIs) lived at the edge of science fiction—impressive in labs, fragile in the real world. Synchron is moving them into daily life by treating neural signals not as curiosities, but as data that can be learned from, modeled, and scaled. The company’s minimally invasive Stentrode brain-computer interface is implanted through blood vessels instead of open brain surgery, allowing for long-term real-world use in individuals with severe paralysis.

In 2025, Synchron moved the technology from clinical feasibility to home use validation in the U.S. COMMAND study, creating the largest U.S. dataset ever produced for an implanted BCI. Patients controlled digital devices and communicated through texting and emailing. That dataset now underpins Synchron’s Chiral, the world’s first cognitive AI foundation model trained on large-scale, self-supervised neural data. It learns directly from how the brain encodes intention, allowing the system to improve continuously as more neural data is collected.

Trained on more than 20 patient-years of brain activity, it represents a shift from decoding signals to modeling cognition itself. By pairing scalable neural data with foundation-model learning, Synchron is helping define a new class of AI where thought becomes a computable signal, and data science becomes a pathway to restored independence.

9. 3E

For arming companies with an AI assistant trained on regulatory data

For most global companies, regulatory risk often arrives as thousands of small changes, scattered across jurisdictions, languages, and agencies. 3E aims to help turn that chaos into structured data. The company’s 3E Insight AI assistant applies generative AI to one of the hardest enterprise data problems: interpreting constantly changing chemical and product regulations with precision.

While other models are trained on general data, Insight is trained only on 3E’s proprietary regulatory data sets, which are reviewed and validated by over 160 subject matter experts. This enables an AI solution that provides source-traceable responses to very technical compliance queries, thus cutting regulatory research time by up to 75% for global corporations. This knowledge is now at the forefront of a comprehensive platform that encompasses safety data sheets, regulatory monitoring, supply chain transparency, and sustainability management.

AI-powered horizon scanning (assessing trending opportunities and threats) constantly evaluates thousands of global signals and connects new regulations to a company’s product offerings, enabling teams to prepare for the impact before the regulations take effect. With over 5,000 customers across the globe, including BASF, Dow, Procter & Gamble, Walmart, and Johnson & Johnson, 3E is revolutionizing the concept of compliance as a data science challenge.

10. Pathway

For bridging neuroscience and machine learning to create an AI architecture

Modern AI systems are trained once, then frozen—an odd limitation for technology meant to operate in dynamic, real-world environments. Pathway is tackling that constraint at the architectural level, asking a more fundamental question: What would it take for AI systems to keep learning while they run? The company’s answer is Baby Dragon Hatchling (BDH), a brain-inspired architecture for continuous learning and long-horizon reasoning.

Unlike transformer-based models that must be retrained in costly, energy-intensive cycles, BDH neural units update and adapt incrementally, closer to how biological cognition works. The design also produces sparse, interpretable activations—making reasoning steps easier to inspect, audit, and regulate. Pathway reports that it enables a 50% lower total cost of ownership and 90% lower latency in early deployments, with the architecture already tested in high-stakes environments such as NATO planning systems, Formula 1 telemetry systems, and La Poste logistics systems. The developer community has also embraced Pathway’s open research model, which has attracted 90,000 GitHub stars.

The company is still very early in terms of commercial viability, yet its architecture could fundamentally change the way data systems are built, deployed, and trusted, particularly when adaptability is more important than size.

Explore the full 2026 list of Fast Company’s Most Innovative Companies, 720 honorees that are reshaping industries and culture. We’ve selected the companies making the biggest impact across 59 categories, including advertising, applied AI, biotech, retail, sustainability, and more.