Ardoq
Ardoq is a dynamic, data-driven Enterprise Architecture (EA) platform that helps organizations connect technology, strategy, and execution in one intelligent environment. Moving beyond static diagrams and outdated spreadsheets, Ardoq enables real-time visibility across your entire IT landscape. It connects live data from applications, teams, and processes to help enterprises make faster, evidence-based decisions. The platform empowers users to map dependencies, crowdsource insights, and identify cost-saving opportunities across systems. With interactive visualizations and automated updates, it keeps architecture continuously accurate and aligned with business strategy. Trusted by global enterprises like ExxonMobil, MUFG, Cisco, and Riot Games, Ardoq transforms enterprise architecture into a strategic growth engine.
Learn more
Acade
Acade is an AI research co-scientist who starts with a research question and turns it into a structured, verifiable research loop. It helps researchers map literature, propose traceable hypotheses, plan experiments, interpret results, and turn the full path into an evidence-backed report while keeping the scientist in control. It is built for human-in-the-loop research, supporting users as they search, compare, critique, and document evidence without replacing scientific judgment. Acade begins with research question intake, capturing the domain, goal, constraints, files, assumptions, and expected decision before the agent starts. It can organize relevant papers, claims, methods, debates, and research gaps into a literature-grounded map while preserving source provenance. It also generates hypothesis cards that compare evidence, counter-evidence, novelty, feasibility, and risk, helping researchers review candidate ideas before execution.
Learn more
Prodely
Prodely is an AI-powered product discovery and insight platform that centralizes scattered customer feedback, research data, and market signals into a structured, searchable knowledge base so product teams can make informed decisions without manual data wrangling. It uses an embedded AI assistant to automatically analyze unstructured input from user interviews, surveys, support tickets, and other sources, distilling trends, patterns, pain points, and strategic themes that help teams identify opportunities and validate assumptions quickly. It generates Opportunity Solution Trees to visualize links between desired outcomes, opportunities, and solutions, supports smart transcription and summarization of audio/video interview data, and streamlines prioritization with frameworks like ICE (Impact, Confidence, Ease) so product teams can align on strategy and roadmap decisions with evidence rather than intuition.
Learn more
SWE-2
SWE-2 is Cognition’s advanced coding model designed to improve software engineering performance while reducing the cost of agentic coding workflows. The model is post-trained from Kimi K3 and uses reinforcement learning to optimize multiple reasoning-effort levels within a single training run. SWE-2 is designed to explore codebases more selectively, begin implementation sooner, and complete tasks with fewer redundant reads and reasoning steps than earlier Cognition models. Its capabilities include code generation, debugging, test creation, verification, repository analysis, and complex terminal-based software engineering tasks. The model also emphasizes stronger engineering judgment, end-to-end test coverage, instruction following, and evidence-based verification of user assumptions. SWE-2 is available through Devin Desktop and Devin CLI, with broader rollout planned across Devin Web and Fusion.
Learn more
