# Distyl — Full Site Content > Distyl builds the intelligence layer for enterprises — systems that encode institutional expertise, execute mission-critical work, and compound in value with every decision. For a structured index of all pages and sections, see: https://www.distyl.ai/llms.txt --- ## Home URL: https://www.distyl.ai ### Hero Headline: Architecting AI-native Enterprises Subheading: Distyl builds the intelligence layer — systems that encode enterprise expertise, execute mission-critical work, and compound with every decision. Backed by: Coatue, OpenAI, Lightspeed, Microsoft, Khosla Ventures ### Philosophy "The enterprises that thrive in the next decade will build their most critical infrastructure around AI systems that understand the business deeply enough to operate it." Distyl closes the gap between frontier intelligence and real-world industry outcomes. We architect the AI-native enterprise — partnering with the most ambitious enterprises to design and operationalize their AI transformations, not by layering AI onto human-designed workflows, but by reimagining operations with AI built at the core. ### Product Suite See dedicated pages below for full content on each product. **Weave** — State-of-the-art AI systems, built autonomously. (https://www.distyl.ai/technology/weave) **Distillery** — The operating system for enterprise AI. (https://www.distyl.ai/technology/distillery) **Context Mesh** — Enterprise knowledge, structured for AI. (https://www.distyl.ai/technology/context-mesh) **Context Views** — Enterprise semantic layer for agent reasoning. (https://www.distyl.ai/technology/context-views) **Journey** — AI voice layer for existing digital experiences. (https://www.distyl.ai/technology/journey) **Personalization** — 1:1 customer engagement at enterprise scale. (https://www.distyl.ai/technology/personalization) **Apprentice** — Tacit knowledge capture and process automation. (https://www.distyl.ai/technology/apprentice) ### Proven in Production - 140M+ consumer interactions powered — one of the largest agentic AI deployments to date - 95% deployment rate — AI systems moved from strategy into production - Weeks or months to production, not years - Deployed across: Telecom, Healthcare, Manufacturing, Insurance, Retail --- ## About URL: https://www.distyl.ai/about ### Built to Ship Enterprise AI has a delivery problem. Spiraling costs, years-long projects, and nothing to show for it. We kept watching the same pattern repeat — and decided to fix it. ### Our Approach Two core beliefs: **Rebuild operations around AI** Real transformation means reimagining systems — not automating broken processes. Distyl designs AI-native operations from the ground up, side by side with the teams that run them. **Experts rebuilding systems together** We don't hand off a platform and walk away. Distyl's forward-deployed engineers and researchers work alongside customer teams to build, deploy, and own AI systems that actually run the business. ### What is Distyl? An Applied Technology Company that Owns the Outcome. - Applied Technology: Rapidly deploy purpose-built AI solutions across the full lifecycle — Discovery → Context Management → Deployment → Continuous Improvement - Relentless Outcome Ownership: Forward-deployed engineers and researchers, high-ROI use case reimagination, EBITDA impact in 6 months ### Team Built by operators and researchers from: Palantir, CIA, OpenAI, Google DeepMind, Tesla, NASA, Stanford, MIT, AWS. --- ## Careers URL: https://www.distyl.ai/careers ### Define how the AI-native enterprise is built Distyl partners with some of the world's most important institutions to transform how they operate with AI. Our engineers, researchers, and strategists build production AI systems that reshape critical business workflows across healthcare, finance, supply chain, insurance, and more. We're not building demos. We're defining how enterprises operate in the age of AI. ### Why work at Distyl? - **Massive Real-World Impact** — Our AI solutions drive $100M+ transformations at Fortune 500s across healthcare, telecom, manufacturing, and retail. This isn't AI for demo day. - **AI Native at the Core** — We're not an "AI-powered" SaaS company. We are AI-native, rethinking how work gets done from the ground up with AI as a full-fledged work partner. - **Speed & Autonomy** — Our most ambitious bets move from concept to production in weeks, not years. No bureaucracy: if you have an idea, you build it; if a decision is needed, you make it. - **A Culture of Builders** — Forward-deployed engineers, researchers, and strategists building alongside the teams that run the business. ### Who thrives here - AI isn't just a tool, it's your mindset — you believe AI-native workflows will reshape entire industries. - You move fast and ship — ideas become prototypes, prototypes become production systems that drive hundreds of millions in impact. - You are relentlessly curious — you ask questions, challenge assumptions, and aren't afraid to say "I don't know, but we will figure it out." ### Benefits - 100% coverage for medical, dental, and vision insurance for employees and dependents - Competitive salary and meaningful equity - 8 weeks parental leave - Retirement plans, life and disability insurance Open roles: https://jobs.ashbyhq.com/Distyl --- ## Context Mesh URL: https://www.distyl.ai/technology/context-mesh ### What It Is Enterprise knowledge structured for AI. Architecting an AI-native enterprise requires reimagining how enterprise knowledge is organized. Context Mesh is the live, governed foundation every agent reads from and writes to — compounding in value with every decision made. ### The Problem Enterprise knowledge lives everywhere — in documents, databases, emails, tribal expertise — and nowhere at once. AI systems that can't access the right context at the right time don't just underperform. They erode trust. Most organizations have tried to solve this with search, RAG, or data lakes. None of these were built for the way agents need to reason — with structure, provenance, and continuous updates. ### Context Mesh — The Knowledge Foundation for AI-Native Enterprises Context Mesh doesn't index documents. It structures enterprise knowledge into a governed, live foundation that every agent reads from and writes to. As decisions are made, the mesh compounds in value — each interaction making the next one more accurate, more contextual, more aligned with how your business actually operates. ### Capabilities 01. Structured Knowledge Ingestion — Every source of enterprise truth, unified into a single governed layer. Context Mesh ingests from documents, databases, APIs, and human expertise — structuring each source with provenance, ownership, and freshness metadata. Agents always know where knowledge came from, how recent it is, and how much to trust it. 02. Live, Compounding Memory — The mesh gets smarter with every decision made. Unlike static knowledge bases, Context Mesh writes back. Every agent action, every human correction, every production outcome feeds into the shared foundation. Over time, the mesh encodes the operational intelligence that makes your business unique. 03. Governed Access for Every Agent — The right context for the right agent. No more, no less. Fine-grained access controls ensure agents only read what they are authorized to see. Governance policies apply across all consumers — whether internal tools, third-party integrations, or autonomous systems — without requiring manual configuration per use case. --- ## Distillery URL: https://www.distyl.ai/technology/distillery ### What It Is The operating system for enterprise AI. 95% of enterprise AI initiatives stall as pilots — Distillery is the end-to-end platform where subject matter experts build AI systems from institutional knowledge, deploy them at production scale, and own them long-term. ### The Problem Enterprise AI projects don't fail because the models aren't good enough. They fail because the path from pilot to production requires engineering depth, institutional knowledge, and operational infrastructure that most organizations can't assemble fast enough. Subject matter experts hold the knowledge that makes AI systems actually work in production. But they have no tools designed for them — so that knowledge never makes it in. ### Distillery — Where Enterprise AI Goes to Production Distillery is the end-to-end platform where subject matter experts build AI systems from institutional knowledge, engineers deploy them at scale, and operators own them long-term. It closes the gap between pilot and production by giving every stakeholder the tools they need — without requiring any of them to become AI engineers. ### Capabilities 01. SME-Led System Building — The people who know the work build the AI that does it. Distillery gives subject matter experts the tools to encode their knowledge directly into AI systems — without writing code. They define what good looks like, annotate edge cases, and validate outputs. The institutional knowledge that usually gets lost in translation becomes the core of the system. 02. Production-Grade Deployment — From pilot to production in weeks, not years. Distillery handles the infrastructure, compliance, and integration complexity that kills AI projects at scale. Systems deploy with monitoring, rollback controls, and audit trails built in — so engineering teams ship with confidence and operators maintain control. 03. Continuous Improvement in the Field — Systems that get better the longer they run. Every production interaction feeds back into Distillery. Failures are surfaced to the right experts, corrections update the system, and performance benchmarks track improvement over time. AI systems don't degrade in production — they compound. --- ## Weave URL: https://www.distyl.ai/technology/weave ### What It Is State-of-the-art AI systems, built autonomously. Building a production AI system takes months of expert engineering and still rarely reaches state-of-the-art performance. Weave produces benchmark-topping systems, from natural language descriptions, in under an hour. ### The Problem Building a state-of-the-art AI system requires specialized engineers, months of architectural iteration, and still most organizations don't get close to what's possible. The models are capable. The techniques exist. But discovering the right architecture for a task requires expertise and iteration cycles that human teams cannot sustain, given the pace businesses now demand. ### Weave — Autonomous AI System Construction Weave doesn't fine-tune models. It discovers the techniques and architectures to build around the best model available — actively pushing for state-of-the-art performance, not just a functional solution. A human engineer explores a handful of compositions. Weave explores dozens, autonomously, and converges on the combination best suited to the task. ### Capabilities 01. Autonomous System Construction — State-of-the-art performance on any task. No prompt engineering. No expert architects. Given a natural language description, Weave autonomously explores architectures and models, iterating through dozens of compositions until performance converges on state-of-the-art. Tested against 75 benchmarks spanning NLP, logical reasoning, medical QA, legal analysis, and agentic tool use — Weave outperformed the published state-of-the-art on all 75, including 15 system-first benchmarks where the competition was purpose-built, expert-engineered systems. 02. Benchmark Generation for Any Task — No test suite? Weave builds one. Most enterprise use cases don't come with a benchmark. Weave generates realistic synthetic evaluation sets from scratch — validated for correctness before they're used for system optimization. Average realism gap versus real ground-truth data: 6%. Correctness auditor accuracy: 87%. Every use case gets a rigorous evaluation foundation from day one. 03. SME Tooling for the Last Mile — The system builds what can be formalized. Your experts refine what can't. Every application Weave builds ships with a UI built for subject matter experts, not engineers. SMEs can inspect benchmark cases, run evaluations, root-cause failures, and edit system behavior through natural language — closing the expert knowledge gap that documentation may miss. --- ## Context Views URL: https://www.distyl.ai/technology/context-views ### What It Is Enterprise semantic layer — enterprise knowledge your agents can actually use. Most enterprise AI fails not because the models are wrong, but because the data they reason from is messy, outdated, and impossible to trust. Context Views fixes that. ### The Problem Most enterprise knowledge is fragmented, stale, contradictory, and encoded in forms agents cannot reliably reason over. Raw RAG answers simple lookups but breaks on multi-document reasoning, policy alignment, temporal context, and contradictions. Agents get the data but not the context. ### Context Views — Enterprise Semantic Layer Context Views sits between your raw data infrastructure and the agents that need organizational context to do work. It transforms files, conversations, code, tickets, and operational data into source-backed business objects, relationships, and task-specific Context Schemas — structured, queryable, and permission-aware. When an agent uses Context Views, it doesn't search. It understands. ### Capabilities 01. Task-Specific Context — The right shape of knowledge for what each agent is trying to accomplish, not a generic retrieval result. Context Views creates representations structured around the task, not around discovery. The result is higher agent reliability on the work that matters: multi-document reasoning, compliance review, policy alignment, personalization, and work routing. 02. Compounding SME Knowledge — Every contradiction resolved becomes shared context for every downstream agent — not a one-off fix. Directed Review gives SMEs a focused way to resolve ambiguity in enterprise knowledge. Those decisions don't stay in a single workflow. They become reusable heuristics, graph updates, and better Context Schemas that sharpen every agent that follows. 03. Provenance and Governance — Every piece of context is traceable, permission-aware, and auditable — by design, not by configuration. Context Views tracks the source of every business object and annotation. Agents always know where their context came from, whether it's current, and whether they're authorized to use it. This is the governance layer that makes enterprise AI trustworthy at scale. --- ## Journey URL: https://www.distyl.ai/technology/journey ### What It Is AI voice layer for existing digital experiences. Digital experiences break down at complexity — Journey gives every user a knowledgeable voice guide, right inside the app, without a rebuild. ### The Problem Most digital UIs are generic, impersonal, and context-blind — built for the average user, not the actual one. When a customer hits a complex moment (comparing plans, evaluating a trade-in, understanding a promotion), the app cannot help them. The complexity that used to live with a knowledgeable agent now lands on the user, and the user walks away. The cost is measurable: lower conversion, higher support volume, and revenue lost on transactions that were already in progress. ### Journey — AI Voice Layer for Digital Experiences Journey is an AI voice assistant that lives inside your existing mobile or web app — no rebuild required. When a user hits a moment of complexity, they talk to Journey. Journey already knows everything: who the user is, what their account looks like, what the app can and cannot do, and the full policy context of your business. That intelligence lives in the Context Mesh, a structured AI-optimized knowledge layer Journey draws on in real time — turning confused users into completed transactions. ### Capabilities 01. Context-Aware Voice Guidance — Users get answers specific to their account, not generic FAQs. Journey knows who the user is, what they're looking at, and what your business can offer them in that moment — so it can guide, explain, recommend, and transact. 02. Zero-Rebuild Deployment — No redesign, no overhaul. Journey embeds as an intelligent overlay on top of your existing mobile or web app, adding a voice-accessible intelligence layer to the flows you already have. 03. Full Knowledge Access via Context Mesh — Deep situational awareness from day one, not a generic script. Journey draws on a structured, AI-optimized layer of institutional knowledge — your products, policies, account data, and business rules — so it arrives with deep situational awareness from day one. --- ## Personalization URL: https://www.distyl.ai/technology/personalization ### What It Is 1:1 customer engagement at enterprise scale. Enterprises still engage customers as cohorts — Personalization gives every customer a concierge that resolves their issues, anticipates their needs, and grows the relationship over time. ### The Problem Until GenAI, enterprises had no choice but to treat customers as cohorts. Marketing speaks to segments, apps render the same flows to everyone, and contact centers route customers through a 20-minute pass-the-parcel — each handoff demanding the customer re-state who they are and why they called. The personalization that does exist is incomplete, mistimed, and indistinguishable from spam. The cost shows up where it always has: lower NPS, higher churn, and a wall of human-only resolution between you and every meaningful moment with a customer. ### Personalization — 1:1 Customer Engagement at Enterprise Scale Personalization gives every customer their own knowledgeable, proactive concierge — across your app, web, chat, and contact center, with no rebuild required. Powered by Context Mesh and Context Views, it carries each customer's full history, intent, and account state across every surface, so the experience is consistent, informed, and genuinely tuned to that individual. Cohort-based marketing becomes a 1:1 relationship, and the human conversations become more human. ### Capabilities 01. Channel-Agnostic Overlay — Without re-platforming what you have. Personalization sits on top of your existing contact center, mobile app, web, and chat as an intelligent layer — not a replacement. Your infrastructure investments stay intact; the experience your customers feel does not. 02. Unified Customer Context, Live — Customers never start over. Neither does the AI. Context Mesh captures the full state of every customer across systems — orders, history, preferences, prior conversations — and Context Views serves each agent the precise slice it needs to act, in real time. When a customer moves from chat to voice to a human rep, the context follows them. 03. Proactive, Hypothesis-Driven Engagement — A concierge that asks, learns, and acts — not a marketing blast. Personalization initiates outreach, gauges interest, and asks follow-up questions to refine each customer's profile. Hypotheses get tested in real conversations; upsell, cross-sell, and partner offers reach the customers most likely to value them — not the cohort they happened to land in. --- ## Apprentice URL: https://www.distyl.ai/technology/apprentice ### What It Is AI-native process mapping and automation. Tacit knowledge capture at scale. Apprentice watches how experts perform their work and turns it into process maps, documentation, and automation in hours, not months. ### The Problem Many processes don't have any documentation today. People in the enterprise have been doing the work for years, so they know how it's done, but that knowledge only lives in the heads of the experts. Capturing this domain knowledge is the first step of reimagining how work gets done. ### Apprentice — Capturing Tacit Knowledge at Scale Inspired by how new hires learn, Apprentice watches how experts perform their work and learns from them. It takes in video recordings, combined with transcripts, and creates the process maps and documentation needed to fully understand how work is done. It captures the happy path, as well as the edge cases, and learns the why in addition to the how. The documentation Apprentice creates allows automation as well as reimagining the process itself. ### Capabilities 01. Process Capture — Process maps of existing workflows in hours, not months. Apprentice uses video recordings of domain experts doing their work to create complete process maps — replacing months of consultant interviews with hours of observation. 02. Workflow Consolidation — Learn a workflow in depth across many experts. By aggregating multiple sessions of experts, Apprentice captures both happy paths and edge cases — building a single canonical understanding of how the work actually gets done. 03. Agentic Execution — Captured context becomes executable workflows. Leverage the context curated by Apprentice to change how work is done — automating routine steps and reimagining the process itself. --- ## Blog All posts: https://www.distyl.ai/blog/all ## Blog — Engineering ### No One Hands You the Right Answer URL: https://www.distyl.ai/blog/engineering/no-one-hands-you-the-right-answer Category: Engineering | Date: 2026-09-03 | Read time: 5 min Authors: Emily Broadhurst Tags: Building at Distyl Inside forward-deployed engineering at Distyl: Emily Broadhurst on questioning the brief, thinking across the system, and owning the outcome. ### Your AI Feedback Loop Is Only as Strong as Your Evaluation Infrastructure URL: https://www.distyl.ai/blog/engineering/your-ai-feedback-loop-is-only-as-strong-as-your-evaluation-infrastructure Category: Engineering | Date: 2026-08-19 | Read time: 8 min Authors: Ravi Bhandia (Product) Tags: feedback loops, evaluation, evals How strong is your AI feedback loop? Measure segmentation coverage, segmentation density, and eval confidence to test whether evals reflect production reality. ### The Validation Gap in Self-Improving AI URL: https://www.distyl.ai/blog/engineering/the-validation-gap-in-self-improving-ai Category: Engineering | Date: 2026-07-27 | Read time: 9 min Authors: Anish Dulla (AI Strategist), Daniar Imanbayev (AI Engineer) Tags: Canary, Self-Improving AI, Validation In most AI deployments, the real world will always find scenarios the test environment missed. The evidence of what to fix isn’t scarce, but what slows teams down is proving a change is safe before it ships. ### How We Do Technical Interviews at Distyl URL: https://www.distyl.ai/blog/engineering/how-we-do-technical-interviews-at-distyl Category: Engineering | Date: 2026-07-03 | Read time: 5 min Authors: Distyl AI (Distyl AI Team) Tags: technical interviews, hiring, engineering, interviews At Distyl, our interviews are designed around the real-world challenges our teams solve. Here’s how our technical interviews work, what we’re evaluating, and how to prepare. ### An Adaptive Harness to Self-Construct State-of-the-Art Systems URL: https://www.distyl.ai/blog/engineering/an-adaptive-harness-to-self-construct-state-of-the-art-systems Category: Engineering | Date: 2026-05-06 | Read time: 5 min Authors: Sandeep Saluru Distyl's adaptive harness achieves state-of-the-art performance on 75 benchmarks. ### Building Brand-Optimized Contact Center Agents: Three High-Risk Vectors & How to Mitigate Them URL: https://www.distyl.ai/blog/engineering/building-brandoptimized-contact-center Category: Engineering | Date: 2025-11-03 | Read time: 12 min Authors: Joey Edell Tags: Contact Center, LLM, Guardrails, Agentic AI Contact centers are among the most impactful real-world applications of AI. At Distyl, we power advanced agentic contact center systems. ### Agent Interoperability: Security Considerations URL: https://www.distyl.ai/blog/engineering/agent-interoperability-security-considerations Category: Engineering | Date: 2025-05-23 | Read time: 8 min Authors: Aryeh Klein Tags: Multi-Agent Systems, Security, AI Architecture In our recent blog post, we outlined four design patterns for multi-agent systems and their associated tradeoffs. ### Agent Interoperability Design Patterns URL: https://www.distyl.ai/blog/engineering/agent-interoperability-design-patterns Category: Engineering | Date: 2025-04-28 | Read time: 10 min Authors: Aryeh Klein Tags: Multi-Agent Systems, AI Architecture, Design Patterns In large enterprises, the adoption of AI-powered agents is growing rapidly, but agent interoperability remains a major challenge. ### Introducing, The Infectious Generosity Guru URL: https://www.distyl.ai/blog/engineering/introducing-the-infectious-generosity Category: Engineering | Date: 2024-02-23 | Read time: 3 min Authors: Distyl AI (Distyl AI Team) Tags: TED, TIGG, Generative AI, Partnership, Infectious Generosity Distyl’s collaboration with TED demonstrates Gen AI’s potential for positive change. --- ## Blog — Research ### VoicEmu: Simulating the Tails of Human Speech URL: https://www.distyl.ai/blog/research/voicemu-simulating-the-tails-of-human-speech Category: Research | Date: 2026-08-07 | Read time: 10 min Authors: Nikhil Gangaram Tags: Conversational Agents, Voice Agent Evaluations, research, voice agents, voice research Frontier models struggle with accents and emotions. VoicEmu pairs speech generation with PCA-whitened embeddings for controllable voice agent stress tests. ### Environment Maps: Giving AI Agents the Context to Navigate the Real World URL: https://www.distyl.ai/blog/research/environment-maps-giving-ai-agents-the-context-to-navigate-the-real-world Category: Research | Date: 2026-06-05 | Read time: 7 min Authors: Yenchia Feng (Applied AI Researcher), Chirag Sharma (AI Platform Engineer), Karime Maamari To bridge the gap between how enterprises actually operate and how agents currently reason, we developed Environment Maps: persistent, multi-modal knowledge graphs that serve as navigational aids for AI agents. ### PrefPO: Optimizing Prompts through Preferences URL: https://www.distyl.ai/blog/research/prefpo-optimizing-prompts-through Category: Research | Date: 2026-03-25 | Read time: 12 min Authors: Rahul Singhal Tags: PrefPO, Prompt Optimization, RLHF, Open Source, LLM Our team built PrefPO, a lightweight, pairwise preference prompt optimization framework, and today we're open-sourcing it! ### Lattice: Building Self-Correcting Guardrails for Conversational Agents URL: https://www.distyl.ai/blog/research/lattice-building-self-correcting Category: Research | Date: 2026-02-20 | Read time: 7 min Authors: Emily Broadhurst Tags: Lattice, Guardrails, AI Safety, Enterprise AI, Conversational Agents Enterprise guardrails are often shipped as static defenses: a handful of regexes, keyword blocks, or LLM judges tied to known failure modes. ### A Systems View of the Space URL: https://www.distyl.ai/blog/research/a-systems-view-of-the-space Category: Research | Date: 2025-11-21 | Read time: 10 min Authors: Karime Maamari Tags: LLM Systems, AI Architecture, Enterprise AI A brief retrospective on the emergence of LLM systems, and a few of the frontier problems we find most compelling. ### OpenAI & Distyl: BIRD Benchmark Leadership URL: https://www.distyl.ai/blog/research/openai-and-distyl-bird-benchmark Category: Research | Date: 2024-08-20 | Read time: 1 min Authors: Distyl AI (Distyl AI Team) Tags: BIRD, NL2SQL, OpenAI, Benchmark, Text-to-SQL We recently shared the news that Distyl AI took the #1 spot in the leading NL2SQL benchmark, BIRD. ### Distyl Takes #1 Spot on BIRD Benchmark (Leading Text-to-SQL Benchmark) URL: https://www.distyl.ai/blog/research/distyl-takes-1-spot-on-bird-benchmark Category: Research | Date: 2024-07-25 | Read time: 5 min Authors: Distyl AI (Distyl AI Team) Tags: BIRD, NL2SQL, Text-to-SQL, GPT-4o, Benchmark This week, the Distyl research team took first place on the leading Text-to-SQL (NL2SQL) benchmark, BIRD. --- ## Blog — Frontier ### Your Superpower Feels Easy. That’s the Point. URL: https://www.distyl.ai/blog/frontier/your-superpower-feels-easy-thats-the-point Category: Frontier | Date: 2026-07-21 | Read time: 5 min Authors: Distyl AI (Distyl AI Team) Tags: AI, culture At Distyl, we’ve found that people create value through four fundamental modes. Most people have a dominant mode. Most people also undervalue it, because the thing that comes easiest tends to feel like it doesn’t count. ### Interviewing with AI at Distyl URL: https://www.distyl.ai/blog/frontier/interviewing-with-ai-at-distyl Category: Frontier | Date: 2026-06-22 | Read time: 5 min Authors: Distyl AI (Distyl AI Team) Tags: interviews, hiring, AI Our engineers are forward-deployed inside some of the world’s most important institutions, using AI every day to build and ship the systems those organizations run on. Here's how AI is integrated into our interview process. ### The Future of Healthcare Is Member-Centric, Not Workflow-Centric URL: https://www.distyl.ai/blog/frontier/the-future-of-healthcare-is-member-centric-not-workflow-centric Category: Frontier | Date: 2026-06-10 | Read time: 4 min Authors: Distyl AI (Distyl AI Team) Tags: healthcare AI is making every healthcare workflow faster, but the member is still the one connecting them. Here's what it takes to build a member-centric future of healthcare with AI. ### The Enterprise of 2030 — Redesigning Work in the Age of AI URL: https://www.distyl.ai/blog/frontier/the-enterprise-of-2030-redesigning Category: Frontier | Date: 2025-08-12 | Read time: 8 min Authors: Distyl AI (Distyl AI Team) Tags: Enterprise AI, Future of Work, Agentic AI By 2030, the Fortune 500 enterprise will be radically restructured–shifting from static hierarchies to adaptive ecosystems of employees and AI agents. ### Women in AI Panel URL: https://www.distyl.ai/blog/frontier/women-in-ai-panel Category: Frontier | Date: 2024-10-04 | Read time: 3 min Authors: Jenn Gamble Tags: Women in AI, Diversity, Panel, Inclusion, Distyl Team Distyl AI, Coatue, and Oasis Collective hosted a Women in AI panel to discuss engineering, product strategy, and the design of AI systems. ### Microsoft for Startups Highlights Distyl AI URL: https://www.distyl.ai/blog/frontier/microsoft-for-startups-highlights Category: Frontier | Date: 2024-08-01 | Read time: 1 min Authors: Distyl AI (Distyl AI Team) Tags: Microsoft, Startups, Partnership, Enterprise AI "The technology is only as valuable as the outcomes it's able to create through products." –Arjun Prakash, CEO of Distyl --- ## Blog — News ### Distyl AI Partners with Google Cloud to Accelerate Enterprise AI Transformation URL: https://www.distyl.ai/blog/news/distyl-ai-partners-with-google-cloud Category: News | Date: 2026-04-22 | Read time: 3 min Authors: Boudhayan Sen Tags: Google Cloud, Partnership, Enterprise AI, Gemini, Fortune 500 Partnership pairs Distyl AI's enterprise AI delivery platform and Forward Deployed Engineer model to Google Cloud infrastructure and Gemini models to deploy enterprise-ready AI agents for Fortune 500 ### Distyl, NVIDIA, and the Reality of Enterprise Agents URL: https://www.distyl.ai/blog/news/nvidia-enterprise-agents Category: News | Date: 2026-03-16 | Read time: 5 min Authors: Jevon Wild, Aryeh Klein Tags: NVIDIA, Enterprise AI, Agents, Distillery, NeMo What production AI actually demands-and the machinery we are building to deliver it. ### Distyl Secures $20M from Lightspeed and Khosla Ventures to Deliver Biggest, Most Impactful Enterprise AI Outcomes URL: https://www.distyl.ai/blog/news/distyl-secures-20m-from-lightspeed Category: News | Date: 2024-11-19 | Read time: 4 min Authors: Jenn Gamble Tags: Funding, Series A, Lightspeed, Khosla, Enterprise AI Fortune 100 companies turn to Distyl’s integrated software and services to power their most ambitious AI initiatives. --- ## Legal & Compliance - Privacy Policy: https://www.distyl.ai/privacy - Terms of Service: https://www.distyl.ai/terms - Distyl is SOC 2 certified and GDPR compliant --- ## Contact - General inquiries: hello@distyl.ai - Press and media: press@distyl.ai - LinkedIn: https://www.linkedin.com/company/distyl-ai - X (Twitter): https://x.com/distylai