Alternatives to Agnost AI

Heap serves as an all-encompassing digital analytics platform that equips companies with a thorough comprehension of their customers' experiences. By seamlessly tracking every user interaction on both web and mobile interfaces, Heap delivers practical insights aimed at enhancing conversion rates, customer retention, and overall user satisfaction. Utilizing sophisticated data science techniques, the platform pinpoints areas of friction and potential growth, empowering businesses to swiftly make informed, data-driven choices. Featuring robust functionalities such as session replays, heatmaps, and AI-enhanced insights, Heap allows organizations to analyze and refine user behavior throughout each interaction. This holistic approach ensures that businesses can continuously adapt and evolve their strategies to meet customer needs effectively.

Smartlook is a mobile analytics solution that helps websites and apps to understand the whys behind users' actions. It has helped over 300,000 companies of all sizes and industries. You can eliminate the guesswork and find real, actionable reasons. Smartlook offers a unique way to understand user behavior at the micro-level. Smartlook's always-on visitor recordings allow you to see every visitor on your website or app. Automatic event tracking allows you to track how and when your visitors do certain things. To see conversion rates and uncover why people are churning, you can create conversion funnels. Heatmaps for websites provide mass data about where people click, scroll and hover on your pages. Smartlook was ranked among the Top 100 Software Products in 2019 G2 Crowd Awards. It serves customers such as O2, Miele and Hyundai. It can also record Unity-engine games.

CUX in nutshell: ✔ User Behavior Analysis ✔ Experience Metrics ✔ Goal-Oriented Analysis ✔ Conversion Waterfalls ✔ Entire Visits Recording ✔ Heatmaps ✔ Pre-analysis & Alerts ✔ Auto-capture Events ✔ Retroactive Analysis ✔ 100% GDPR-compliant ✔ Data stored in EOG ✔ SSL secured

Kayba empowers AI agents to enhance their performance through experiential learning. By analyzing execution traces, it identifies and rectifies failures while assessing the effectiveness of these corrections. Rather than depending on generic evaluations that fail to clarify the reasons behind an agent's shortcomings, Kayba utilizes the agent's unique traces to identify failure modes and create tailored benchmarks relevant to the user's specific context, enabling teams to gauge improvements against authentic production failure patterns. With a simple one-line setup, Kayba integrates tracing into the agent, continuously monitors its performance, and promptly alerts users when any step ceases to be recorded. Since even effective tracing can degrade as teams implement changes, Kayba actively reviews existing tracing, highlights any broken elements, identifies the specific file requiring attention, and relays the issue to a coding agent via MCP. This coding agent then addresses the problem, after which Kayba confirms that the trace is fully functional again, ensuring ongoing reliability and performance enhancement. Ultimately, this process allows teams to maintain high standards of operational continuity while fostering continual improvement in their AI systems.

Netra serves as a robust platform designed for AI agents to monitor, assess, simulate, and enhance the decisions made by these agents, allowing for confident deployments and proactive identification of regressions prior to user exposure. Built on OpenTelemetry, SOC2 Type II certified, and compliant with GDPR and HIPAA. Key Features 1. Observability: Comprehensive tracing capabilities that capture every step of multi-agent, multi-step, and multi-tool processes, detailing inputs, outputs, timings, and costs for each reasoning step, LLM invocation, and tool use. 2. Evaluation: Automated quality assessment for each agent decision, utilizing integrated scoring rubrics, custom evaluations with LLMs and code reviewers, online assessments using live traffic, and continuous integration gates to prevent regressions. 3. Simulation: Evaluate agents under the stress of thousands of both real and synthetic scenarios before they go live. This includes using varied personas, conducting A/B tests against baseline performances, and quantifying confidence levels prior to any user interaction. 4. Prompt Management: Each prompt is versioned, compared, tracked for lineage, and safeguarded against rollbacks, ensuring that every production response can be traced back to its precise prompt version, thereby enhancing accountability and control. Netra is built on OpenTelemetry, making it compatible with any OTLP-compliant backend and ensuring teams can get started with just 2 to 3 lines of code. It integrates with 14+ LLM providers including OpenAI, Anthropic, Google Gemini, and AWS Bedrock, and 12+ AI frameworks including LangChain, LangGraph, CrewAI, and LlamaIndex. The platform is SOC2 Type II certified and compliant with GDPR and HIPAA, with strict US and EU data residency

Langfuse is a free and open-source LLM engineering platform that helps teams to debug, analyze, and iterate their LLM Applications. Observability: Incorporate Langfuse into your app to start ingesting traces. Langfuse UI : inspect and debug complex logs, user sessions and user sessions Langfuse Prompts: Manage versions, deploy prompts and manage prompts within Langfuse Analytics: Track metrics such as cost, latency and quality (LLM) to gain insights through dashboards & data exports Evals: Calculate and collect scores for your LLM completions Experiments: Track app behavior and test it before deploying new versions Why Langfuse? - Open source - Models and frameworks are agnostic - Built for production - Incrementally adaptable - Start with a single LLM or integration call, then expand to the full tracing for complex chains/agents - Use GET to create downstream use cases and export the data

Maxim is a enterprise-grade stack that enables AI teams to build applications with speed, reliability, and quality. Bring the best practices from traditional software development to your non-deterministic AI work flows. Playground for your rapid engineering needs. Iterate quickly and systematically with your team. Organise and version prompts away from the codebase. Test, iterate and deploy prompts with no code changes. Connect to your data, RAG Pipelines, and prompt tools. Chain prompts, other components and workflows together to create and test workflows. Unified framework for machine- and human-evaluation. Quantify improvements and regressions to deploy with confidence. Visualize the evaluation of large test suites and multiple versions. Simplify and scale human assessment pipelines. Integrate seamlessly into your CI/CD workflows. Monitor AI system usage in real-time and optimize it with speed.

RagMetrics serves as a robust evaluation and trust platform for conversational GenAI, aimed at measuring the performance of AI chatbots, agents, and RAG systems both prior to and following their deployment. It offers ongoing assessments of AI-generated responses, focusing on factors such as accuracy, relevance, hallucination occurrences, reasoning quality, and the behavior of tools utilized in real interactions. The platform seamlessly integrates with current AI infrastructures, enabling it to monitor live conversations without interrupting the user experience. With features like automated scoring, customizable metrics, and in-depth diagnostics, it clarifies the reasons behind any failures in AI responses and provides solutions for improvement. Users can conduct offline evaluations, A/B testing, and regression testing, while also observing performance trends in real-time through comprehensive dashboards and alerts. RagMetrics is versatile, being both model-agnostic and deployment-agnostic, which allows it to support a variety of language models, retrieval systems, and agent frameworks. This adaptability ensures that teams can rely on RagMetrics to enhance the effectiveness of their conversational AI solutions across diverse environments.

SessionStack uses cutting-edge session recording technology empowered by AI to form a Digital Experience Analytics platform. This platform aids e-commerce businesses in pinpointing customer obstacles, drop-off points, and untapped conversion prospects. The platform's insights speed up the optimization of the overall user experience by leveraging data-driven optimization of conversion rates. Our proprietary machine-learning models cater perfectly to e-commerce decision-makers aiming for revenue maximization. SessionStackAI combines qualitative and quantitative user data to fuel a comprehensive overview of website or mobile app interactions. With its auto-capture features and extensive retrospective data, the platform ensures a thorough analysis, detecting any friction points or new conversion possibilities in real-time.

Fumblemap stands out as the leading open-source utility for network discovery and error visualization, catering specifically to the needs of system administrators, software developers, and IT experts. By eliminating uncertainty in diagnostic troubleshooting, Fumblemap enables users to identify issues quickly and effectively. In scenarios where network configurations fail or data packets are lost, this innovative tool allows you to pinpoint the exact failure location via an engaging and intuitive visual map. Key Features Include: * Real-time monitoring of errors and visual representation of data packets * Dynamic mapping of network topology for enhanced visibility * Compatibility across all major operating systems to ensure versatility * Extensive logging capabilities along with various export options * Completely open source and driven by community contributions, fostering continuous improvement and innovation. This collaborative approach ensures that Fumblemap evolves in line with user needs and technological advancements.

Formo is an onchain-native web3 product OS. Use token-gated forms and wallet intelligence to engage and retain web3 customers. Formo's suite helps you to understand and use data on users of web3 to boost your growth. Our mission is to provide web3-native growth infrastructure and analytics for the next generation of Onchain builders. Formo is what we wish existed when we first started out in web3. Formo's platform can sift through fragmented data from web2 and web3 to give you a unified picture of your users, and the health of your product. This allows you to create products that people want. Formo lets you monitor and analyze your end-to-end users journey, from engagement through offchain channels to conversion onchain. It eliminates the need for an internal data team of SQL developers to produce dashboards. This allows teams to focus more on innovation and product developments.

LayerLens serves as an autonomous platform dedicated to evaluating AI models, providing insights into their performance through verified benchmarks, prompt-specific outcomes, agentic comparisons, and audit-ready assessments across different vendors. This platform enables teams to conduct side-by-side comparisons of over 200 AI models, utilizing transparent benchmarks and consistent evaluation techniques focused on accuracy, latency, behavior, and practical application in real-world scenarios. Designed for comprehensive model analysis, LayerLens features Spaces that allow teams to organize benchmarks and evaluations, identify strengths in tasks, and monitor performance trends in relevant contexts. The platform also facilitates ongoing evaluations by continuously assessing model updates, prompt modifications, judge changes, and live traces, thereby empowering teams to identify issues like quality regressions, drift, silent failures, contamination, and policy concerns before they impact production. By prioritizing transparency and collaboration, LayerLens ensures that teams can make informed decisions about their AI model choices.

Every retail establishment faces the challenge of losing sales, but how can you uncover the reasons behind this decline in your store? The answer lies in obtaining insights directly from genuine customers who can pinpoint the issues for you. This valuable information is gathered through observation, a method that is accessible to anyone. Our team of testing agents will explore your store, capturing their screens and narrating their experiences as they go. Say goodbye to uncertainty and isolated trials! Enhance your online store by utilizing data collected from actual users—discover the moments of frustration that lead to lost sales. While their candid feedback may be tough to hear, it offers invaluable insights. These real users will dissect your store and provide you with straightforward answers to your inquiries! You’ll see them experience confusion, frustration, or disorientation, which will highlight the critical points of friction in their purchasing journey. After their exploration, we’ll deliver the recorded video to you, allowing you to grasp their perspective fully. By identifying and addressing these obstacles, you can streamline the buying process, ultimately boosting your conversion rates and increasing profitability. Taking action based on this feedback not only improves the user experience but can significantly transform your bottom line.

Neatlogs serves as a collaborative platform for debugging and enhancing AI reliability, equipping your team with all the necessary tools to effectively identify, comprehend, and resolve issues related to AI agents. Today, you can accomplish the following tasks: - Trace and replay: Observe your agent's actions in a detailed, step-by-step manner. - Detect failures: Automatically identify and flag issues in traces using various conditions, patterns, and classifiers. - Investigate: Utilize Neat AI to analyze runs and convert its insights into actionable fixes. - Evals: Direct traces to either human or AI reviewers to assess quality over time. - Experiment: Create versions of prompts, implement changes, and assess their performance against designated datasets. - Fix: Examine AI-generated solutions and send them to your coding agent for implementation. - Connect your tools: Integrate your applications through Tools and MCPs, allowing Copilot and Neat Agent to operate on your behalf. - Track production: Keep an eye on costs, latency, error rates, tools, and detection trends across all your traces. In contrast to other tools that cater solely to technical users, Neatlogs is designed to be user-friendly and easily comprehensible, ensuring that team members from various backgrounds can effectively engage with the platform. This approach empowers diverse teams to collaborate seamlessly in optimizing their AI systems.

Utilize our automated insights and customizable metrics to assess, enhance, and perpetually refine your GenAI models. Future AGI streamlines the evaluation of AI model outputs by automatically scoring them, which removes the necessity for manual quality assurance assessments. As a result, your QA team can redirect their efforts toward more strategic initiatives, potentially boosting their efficiency and capacity by as much as tenfold. This ensures that your AI-driven customer interactions remain consistently positive and aligned with your brand identity. By optimizing your models, you can highlight the most pertinent and engaging content tailored to each user. Additionally, you can fine-tune your models to produce the most precise summaries for your audience. Future AGI empowers you to establish bespoke metrics that assess your AI model's accuracy according to the specific priorities of your use case. You can articulate your essential metrics in natural language, providing your QA team with greater adaptability and authority to evaluate model performance. This approach guarantees that your assessments are in harmony with your business goals, transcending conventional metrics such as relevance while promoting a more comprehensive evaluation framework. Embracing this method not only enhances model performance but also fosters a culture of continuous improvement within your organization.

You might find it challenging to scrutinize every session replay, but Decibel's AI can effortlessly handle that task. Unlock the potential of DXS® – recognized as the most advanced algorithm designed to enhance digital interactions. By leveraging DXS®, you access an extensive reservoir of knowledge aimed at elevating digital experiences. Gain immediate insights that can dramatically boost online conversion rates, user engagement, and customer fidelity. With years of refinement on heavily-trafficked sites, Decibel’s DXS® evaluates billions of digital interactions monthly, driving an unmatched insight mechanism. Once installed, Decibel's intelligence is swiftly integrated into your website, revealing patterns related to user frustration and engagement, while identifying and highlighting subpar experiences. Dive deeper with comprehensive visualizations that allow you to empathize with your users, recognize their challenges, and prioritize necessary enhancements alongside your team. Furthermore, Decibel enhances the tools you already use by providing valuable data about the caliber of digital experiences. This integration ensures you remain informed and equipped to make data-driven decisions for continuous improvement.

Elevate your product experience by moving past mere analytics and harnessing user insights quickly. Gather valuable feedback from targeted user groups by analyzing their traits and interactions within your product. Utilize the comprehensive Sprig platform to gain a deeper understanding of user perspectives throughout the entire product development lifecycle. Capture video snippets of user sessions coupled with their real-time feedback within the product interface. Proactively address and resolve pain points in crucial user pathways before they escalate into significant issues. Discover the preferences and dislikes of your most engaged users, as well as their suggestions for future features. Recognize behavioral trends that may contribute to user churn and develop effective strategies to mitigate them. By understanding these dynamics, you can create a more tailored experience that keeps users engaged and satisfied.

Oqoqo serves as a comprehensive platform for creating evaluations and tailored benchmarks for practical tasks requiring agency, enabling teams to conduct large-scale experiments in realistic settings utilizing fully managed cloud services. Users have the flexibility to establish private sets of tasks and criteria, evaluate agents on their ability to interact with various products such as skills, MCP servers, CLIs, SDKs, APIs, documentation, and files, while also facilitating the comparison of agents, models, interventions, and levels of effort under consistent conditions. Each individual task operates in its own separate environment, complete with the necessary project state, context, files, tools, and credentials. Oqoqo meticulously records every aspect of each run, documenting commands, tool interactions, errors, files, and the point at which an agent ceased functioning, ultimately providing metrics such as pass or fail results, pass rates, improvements, token utilization, and areas of friction. With these valuable insights, teams are empowered to pinpoint issues within product interfaces, address token inefficiencies, analyze performance variances, rectify failures, and subsequently re-execute the experiments for further refinement and learning. This iterative process fosters a culture of continuous improvement, ensuring that agents are consistently enhanced for optimal performance.

Amplitude is an AI-powered digital analytics platform that enables organizations to understand customer behavior, optimize digital products, and drive business growth through actionable insights. It combines product analytics, web analytics, session replay, heatmaps, experimentation, feature management, guides, surveys, and data governance within one unified platform. The platform's AI features—including AI Agents, AI Feedback, AI Assistant, Amplitude MCP, and Agent Analytics—allow teams to ask questions in natural language, uncover user friction, monitor AI performance, and automate recurring analytical tasks. Amplitude integrates directly with popular AI coding assistants and development tools such as Claude, Cursor, GitHub Copilot, Windsurf, Replit, and Codex, allowing teams to access product insights without leaving their workflows. Product teams can quickly identify drop-off points, analyze customer journeys, and prioritize features based on real usage data. Marketing teams gain behavioral insights that improve acquisition, engagement, and customer lifetime value through personalized experiences and experiments. Engineering teams can safely release features using feature flags while measuring adoption and business impact. Strong data governance, integrations, privacy controls, and security features ensure organizations can trust the accuracy and consistency of their analytics. Amplitude helps businesses continuously analyze, optimize, experiment, and improve every stage of the customer experience with AI-driven decision support.

Organizations, regardless of their scale, utilize Halosight AI and NLP to enhance support performance and interpret help desk data signals. By unlocking vital support indicators, they can simultaneously boost operational efficiency. This approach highlights avenues for innovation, with customers opting for Halosight to achieve favorable support results through previously underutilized service cloud information. The key to innovation lies in maximizing the potential of data that resides within tickets and case histories. Clients of Halosight harness AI and NLP to unearth new possibilities. When agents have access to critical insights, information transforms into actionable solutions. This empowers them to address issues proactively instead of merely responding to problems as they arise. Long lists of case reasons become obsolete thanks to Halosight's automation, which categorizes cases to optimize routing and workflow processes with AI. Additionally, the platform enables the monitoring of emerging support signals that enhance case deflection, knowledge management, and agent productivity. With its integration through the Salesforce App Exchange, Halosight is embedded deeply within Salesforce, ensuring seamless functionality rather than being just another add-on. This comprehensive approach fosters a culture of continuous improvement in customer support.

TierZero Production Agents actively monitor incidents, manage alerts, and autonomously resolve production issues, enabling your engineering teams to release updates more swiftly. When an incident occurs, TierZero immediately engages, conducting a thorough investigation that spans your entire stack, including logs, traces, metrics, deployments, code alterations, and historical incidents. Unlike conventional AI SRE tools that merely handle triage, Production Agents encompass the entire post-merge process, which includes investigation, remediation, support Q&A, and proactive discovery. The Context Engine from TierZero integrates signals from code, infrastructure, discussions, and documentation into a dynamic knowledge graph that evolves and improves with each resolved issue. Installation within your environment can be accomplished in less than an hour, and every AI-driven investigation is fully auditable. This solution is specifically designed for highly regulated industries, such as fintech, healthcare, and cryptocurrency, where maintaining security is imperative. Furthermore, with its continuous learning capabilities, TierZero not only addresses current incidents but also anticipates potential future challenges.

Trusys.ai serves as a comprehensive AI assurance platform designed to assist organizations in assessing, securing, monitoring, and managing artificial intelligence systems throughout their entire lifecycle, from initial testing stages to full-scale production implementation. The platform includes various tools, such as TRU SCOUT, which automates security and compliance checks against international standards and identifies potential adversarial vulnerabilities; TRU EVAL, which conducts thorough evaluations of AI applications—covering text, voice, image, and agent functionalities—focusing on metrics like accuracy, bias, and safety; and TRU PULSE, which monitors production in real-time, providing alerts for issues related to drift, performance drops, policy breaches, and anomalies. By offering complete visibility and tracking of performance, Trusys enables teams to identify unreliable outputs, compliance deficiencies, and operational challenges at an early stage. Additionally, Trusys facilitates model-agnostic evaluations with a user-friendly, no-code interface and incorporates human-in-the-loop assessments along with customizable scoring metrics, effectively marrying expert insights with automated evaluations. This combination ensures that organizations can maintain high standards of performance and compliance in their AI systems.

Trace is an innovative PCB design platform tailored for hardware teams, guiding them from initial concept to a fully manufactured board. It integrates advanced schematic capture and PCB layout tools with smart agents that comprehend the context of hardware design. Users can articulate their project requirements in natural language, enabling Trace to conduct thorough research, produce complex multi-sheet schematics, interpret datasheets, recommend components, verify their availability in real time, arrange parts, route multi-layer boards, optimize matched-length signals, and perform essential checks like ERC and DRC along with design reviews. With a comprehensive library boasting over 30,000 symbols and footprints, the platform sources data from leading electronic distributors. Additionally, Plan Mode delves into intricate tasks, poses clarifying questions, devises a detailed action plan, and initiates execution only after receiving user approval. Moreover, TraceRules ensures that preferences regarding components, layout restrictions, and manufacturing objectives are consistently maintained throughout user interactions, facilitating a smoother design process. This comprehensive approach not only enhances efficiency but also streamlines collaboration within hardware teams.

Humanic streamlines the creation of product onboarding milestones while offering highly accurate segments for each stage. This process employs a combination of user engagement and profile information derived from your product. By generating content tailored to user actions and their reactions to prompts, Humanic continuously adapts to what resonates with your audience at each milestone, automatically updating email content as needed. You can instantly assess the effectiveness of your campaigns against key objectives, such as conversion rates and user retention. By leveraging the comprehensive insights from your data, you can cultivate impactful, customized experiences for your users on a larger scale. Ultimately, this approach ensures that every interaction is relevant and enhances user satisfaction.

Mitzu is an agentic analytics platform that gives every team an AI analyst on top of their data warehouse. Ask any business question in plain language — Mitzu autonomously generates and runs the query on your live Snowflake, BigQuery, Redshift, or Databricks data, then returns an explainable answer with the underlying SQL visible. No data is ever duplicated. Beyond ad-hoc questions, Mitzu runs deep multi-angle analysis and proactively monitors KPIs with email and Slack alerts. Built for product, marketing, growth, and data teams. BYOC and self-hosting available for enterprises with strict compliance needs.

Identify and resolve performance issues and errors within your microservices architecture. Establish connections between root causes by analyzing traces, logs, and metrics. Reduce your costs associated with OpenTelemetry traces through Aspecto's integrated remote sampling feature. The way OTel data is visualized plays a crucial role in enhancing your troubleshooting efficiency. Transition seamlessly from a broad overview to intricate details using top-tier visualization tools. Link logs directly to their corresponding traces effortlessly, maintaining context to expedite issue resolution. Utilize filters, free-text searches, and grouping options to navigate your trace data swiftly and accurately locate the source of the problem. Optimize expenses by sampling only essential data, allowing for trace sampling based on programming languages, libraries, specific routes, and error occurrences. Implement data privacy measures to obscure sensitive information within traces, specific routes, or other critical areas. Moreover, integrate your everyday tools with your operational workflow, including logs, error monitoring, and external event APIs, to create a cohesive and efficient system for managing and troubleshooting issues. This holistic approach not only improves visibility but also empowers teams to tackle problems proactively.

UXCam stands out as the leading provider in app experience analytics, equipping mobile development teams with rapid, contextual, and precise insights. By enabling the recording, analysis, and sharing of sessions and events, UXCam helps teams reveal intricate patterns of app usage. This platform allows for a deep dive into the motivations behind user actions, transitioning from mere quantitative data to rich qualitative insights. Users can replay sessions with personalized events and establish event-driven funnels to analyze specific segments. This comprehensive overview of user interactions and navigation through the app helps pinpoint where users drop off or encounter UX challenges. Additionally, it allows teams to identify and remedy design obstacles within the app. By gaining insight into user behavior across every screen, teams can continuously enhance the user experience, leading to lower churn rates and improved retention. Furthermore, UXCam aids in identifying and addressing app crashes, bugs, and UI freezes. With the ability to export technical logs, teams can share affected sessions with other departments, ensuring seamless and efficient release cycles while fostering collaboration across the organization. Ultimately, UXCam plays a crucial role in optimizing the overall app experience.

The LaunchDarkly feature management platform allows users to dynamically control which application features are accessible to their audience. By leveraging feature management, contemporary development and operations teams can enhance their speed and handle more development cycles effectively. This approach is regarded as a best practice, enabling engineering teams of various sizes to deploy code continuously while giving business teams the authority to manage the user experience. With the LaunchDarkly platform, top teams can mitigate risks and actualize their concepts from the very start. Accelerate your software delivery process by decoupling code deployments from feature launches, allowing for deployment at your discretion and feature releases when you’re fully prepared. By utilizing feature flags, you can minimize the cost of errors when introducing new features or updating systems. Additionally, you can oversee and adjust your features in real-time, ensuring that you test comprehensive functionalities rather than just superficial adjustments. This level of control ultimately leads to a more efficient and responsive development cycle.

OpenAgents serves as an open-source platform and framework aimed at constructing, linking, and deploying networks of AI agents that can collectively identify, communicate, collaborate, and resolve issues, rather than functioning independently. This empowers developers to establish and participate in agent communities that can operate on a large scale while efficiently sharing resources. The platform furnishes an infrastructure for AI agent networks, each functioning as a distinct community with capabilities for peer discovery, message exchange, and synchronized collaboration utilizing adaptable protocols like HTTP, WebSocket, and gRPC. It is crafted to be protocol-independent and is compatible with various prominent large language model providers and agent frameworks, accommodating a wide array of deployment situations. Users are given the flexibility to create their own agents through straightforward configurations or to incorporate personalized logic and tools, allowing them to link their agents to multiple networks and oversee interactions via OpenAgents' standardized interfaces. Ultimately, this framework fosters a collaborative ecosystem where AI agents can work together to achieve complex objectives.

Code Fundi serves as an advanced codebase intelligence platform that equips AI agents, engineering teams, and applications with a comprehensive and searchable map of software repositories. Rather than depending on tools like grep, basic embeddings, or fragmented documentation, it efficiently ingests a repository via a URL or GitHub, systematically maps files, functions, dependencies, and logical flows, eliminates unnecessary boilerplate, and transforms the output into a token-optimized format suitable for AI context windows. Additionally, its Blast-Radius Guard feature identifies which files, services, and downstream behaviors might be impacted prior to any code modifications, aiding teams in avoiding silent regressions and potential production failures. The Universal Search capability enables lightning-fast semantic queries across a single repository or multiple codebases, empowering users to discover implementation patterns, analyze architectural differences, scrutinize authentication or testing methods, and track how code operates across various projects, thus enhancing overall code comprehension and collaboration. This comprehensive toolset makes Code Fundi an invaluable asset for modern software development.

devtodev is a product analytics platform that allows data-driven teams to gain valuable insights and influence user decision making. You can convert users to paying users, improve app economics, predict revenue, customer lifetime value, and predict churn. You can also analyze and influence user behavior.

AvonAI ensures that your AI agents stay aligned with your business objectives by closely monitoring every interaction with customers, managing all communications, and fostering trust in outcomes at scale. While your agents are actively engaged in real-time conversations with actual customers, they require oversight since they can deviate from established scripts, stray from company policies, and struggle to adapt to evolving business needs independently. AvonAI meticulously analyzes each interaction and highlights significant issues such as policy breaches, incorrect information, and other deviations in behavior, enabling teams to identify and address potential risks within hours rather than weeks. This platform empowers operational teams to update agent knowledge and modify behaviors using straightforward language, eliminating the need for coding or developer involvement, and providing a clear preview of changes, which can be validated prior to implementation. Moreover, AvonAI continually evaluates agents against organizational guidelines, ensuring that any alterations in models, prompts, or knowledge bases are promptly assessed, allowing teams to maintain oversight of agent performance and ensure they act as intended. Ultimately, this proactive approach helps maintain the quality and reliability of customer interactions.

Luciq is an advanced mobile observability platform powered by AI, tailored for app developers and enterprises, enabling them to effectively monitor, diagnose, and enhance mobile applications with ease. This comprehensive solution integrates bug reporting, crash analytics, session replay, and performance monitoring within a single SDK that accommodates Android, iOS, web, and hybrid applications. Users can collect extensive device logs, network traces, annotated screenshots, videos, and user feedback, while machine learning automatically correlates events and errors to prioritize issues based on their impact. By offering developers insights into user sessions where problems occurred, they can replicate defects through replay and expedite issue resolution via integrations with tools like JIRA, Slack, Zapier, and Zendesk. Luciq's “Agentic Mobile Observability” methodology not only highlights the most pressing issues but also identifies potential root causes and suggests remediation strategies, empowering teams to boost their efficiency, enhance application stability, and improve the overall user experience. Ultimately, this platform transforms the way teams approach mobile app development and maintenance, ensuring they stay ahead of potential challenges.

Activeloop offers a comprehensive infrastructure for ongoing learning, aimed at teams engaged in software development, agent creation, and data pipeline management. At the heart of their offerings is Deeplake, a GPU-driven database specifically designed for agents, which operates on the principle that if artificial intelligence utilizes GPU technology, then the corresponding data should also be optimized for GPUs. Deeplake facilitates the grounding, versioning, querying, and GPU compatibility of AI agents by integrating both vector and tensor data into a unified storage solution, featuring GPU streaming capabilities for fine-tuning along with a serverless Postgres interface. This product empowers teams with a robust data engine for multimodal AI, enabling them to efficiently store, index, search, and stream data directly to their models and agents. Rather than viewing AI data as fragmented files, embeddings, metadata, and traces scattered across various disjointed systems, Activeloop consolidates these elements into a cohesive infrastructure that supports efficient retrieval, model training, fine-tuning, and memory management for agents. Additionally, the platform includes Hivemind, which transforms agent traces into collective team expertise, thereby allowing solutions developed once to be disseminated throughout the organization via trajectory capture, ultimately enhancing collaborative efficiency and innovation. This seamless integration of data and collaborative tools fosters an environment where teams can thrive in their AI initiatives.

Effortless debugging for microservices allows for immediate identification of the code responsible for service failures, even in cases of elusive errors. Gain insights into each request, outlier, and issue without the need for extra logging or error reproduction efforts. Discover the fundamental causes of every error with comprehensive context derived from logs, metrics, traces, and instances of failed code execution. Benefit from seamless end-to-end tracing supported by automatic instrumentation, enabling a detailed view of logs, metrics, traces, and the history of code execution failures. Experience thorough performance monitoring that aids in swiftly pinpointing and eliminating application bottlenecks. Enjoy real-time topology discovery that provides complete visibility of dependencies across all services involved. Utilize highly adaptable dashboards and notification systems to detect issues before they reach end users. Furthermore, ensure that all failed tests and errors are documented automatically, making it easier to address each failure effectively and facilitating a rapid feedback loop between testing and development teams throughout the entire development process. This approach not only enhances collaboration but also significantly improves overall software quality.

Atla serves as a comprehensive observability and evaluation platform tailored for AI agents, focusing on diagnosing and resolving failures effectively. It enables real-time insights into every decision, tool utilization, and interaction, allowing users to track each agent's execution, comprehend errors at each step, and pinpoint the underlying causes of failures. By intelligently identifying recurring issues across a vast array of traces, Atla eliminates the need for tedious manual log reviews and offers concrete, actionable recommendations for enhancements based on observed error trends. Users can concurrently test different models and prompts to assess their performance, apply suggested improvements, and evaluate the impact of modifications on success rates. Each individual trace is distilled into clear, concise narratives for detailed examination, while aggregated data reveals overarching patterns that highlight systemic challenges rather than mere isolated incidents. Additionally, Atla is designed for seamless integration with existing tools such as OpenAI, LangChain, Autogen AI, Pydantic AI, and several others, ensuring a smooth user experience. This platform not only enhances the efficiency of AI agents but also empowers users with the insights needed to drive continuous improvement and innovation.

LiveSession allows you to analyze users' behavior, improve UX and find bugs. You can also increase conversion rates through session replays and event-based product analysis. Visualization of the most popular sections of your website will allow you to increase the number and frequency of actions. Console logs can be used to identify and fix errors that cause irritation for users. This will also help you lower conversion rates. To better reflect the user's experience, send product-specific events using our Javascript API. To create a single source for truth, enrich user sessions. You can analyze the journeys of your customers to purchase by organizing their paths into funnels.

UserIQ was founded in 2014 to help businesses realize the full benefits of customer success. It provides teams with the product intelligence and customer insights needed to fight churn and grow their account. It also aligns the business around the users' needs. UserIQ empowers CS departments to focus on users' goals and allows them to be a part of all departments. Our platform makes customer success a business mindset and not a function.

It aids in collecting timing information essential for diagnosing latency issues within service architectures. Its functionalities encompass both the gathering and retrieval of this data. When you have a trace ID from a log, you can easily navigate directly to it. If you don't have a trace ID, queries can be made using various parameters such as service names, operation titles, tags, and duration. Additionally, notable data is summarized, including the proportion of time spent on each service and the success or failure of operations. The Zipkin user interface also features a dependency diagram that illustrates the volume of traced requests processed by each application. This visualization can be instrumental in recognizing overall patterns, including error trajectories and interactions with outdated services. Overall, this tool not only simplifies the troubleshooting process but also enhances the understanding of service interactions within complex architectures.

AgentHub serves as a dedicated staging platform designed to emulate, trace, and assess AI agents within a secure and private sandbox, allowing for deployment with assurance, agility, and accuracy. Its straightforward setup enables users to onboard agents in mere minutes, complemented by a strong evaluation framework that offers detailed multi-step trace logging, LLM graders, and customizable assessment options. Users can engage in realistic simulations with adjustable personas to replicate varied behaviors and stress-test scenarios, while dataset enhancement techniques artificially increase test set size for thorough evaluation. The system also supports prompt experimentation, facilitating large-scale dynamic testing across multiple prompts, and includes side-by-side trace analysis for comparing decisions, tool usage, and results from different runs. Additionally, an integrated AI Copilot is available to scrutinize traces, interpret outcomes, and respond to inquiries based on the user's specific code and data, transforming agent executions into clear and actionable insights. Furthermore, the platform offers a combination of human-in-the-loop and automated feedback mechanisms, alongside tailored onboarding and expert guidance to ensure best practices are followed throughout the process. This comprehensive approach empowers users to optimize agent performance effectively.

Apodex is an advanced, self-evolving solver designed for in-depth research, focusing on providing answers that hold up to scrutiny through thorough reasoning rather than mere quick responses. It meticulously tackles complex problems in a step-by-step manner, ensuring that every conclusion is validated before progressing, and each report is accompanied by verified citations. Specifically created for intricate questions that lack straightforward answers, Apodex delves deeply into research, scrutinizes evidence, and confirms each step, allowing users to have confidence in the pathway leading to the final conclusion. Users with accounts have the capability to save their inquiries, revisit them later, continue their investigations at will, search within various threads, branch off from any report, and review the reasoning behind each step in detail. The Apodex-1.0 model emphasizes verification in deep research and can function as a conventional tool through the ReAct agent, while its robust mode operates a team of asynchronous agents that delegate tasks to specialized sub-agents for retrieval and verification, which then share findings through a collective evidence repository, ultimately feeding into a global verification system. This innovative approach not only enhances the reliability of research outcomes but also promotes an interactive and user-friendly experience for those seeking comprehensive answers.

Marketers often struggle to predict the trajectory of a customer journey accurately. However, by analyzing data to uncover trends and behavioral patterns, GrowthSimple can pinpoint your most valuable customers, discover opportunities for cross-selling and upselling, forecast lifetime customer value, and much more. This deep understanding of future customer behaviors empowers marketers to develop more strategic and informed approaches. In an ever-evolving market, having such insights is invaluable for crafting effective marketing strategies. Ultimately, predicting where the customer journey leads is crucial for sustained business growth and success.

Evalgent serves as a platform dedicated to the testing and evaluation of AI voice agents. The common reasons for failures in production are not due to inadequate technology but stem from the fact that demonstrations typically utilize pristine audio and compliant users, which is not reflective of actual user interactions. By identifying potential failures before they can impact production, Evalgent reduces the time needed for iterations and accelerates the path to revenue for voice agents. THE PROCESS 1. Define: establish authentic scenarios and criteria for success. 2. Run: execute tests that mimic realistic human behavior. 3. Measure: identify successful elements, failures, and operational boundaries. 4. Act: obtain clear, actionable insights for necessary adjustments or deployments. KEY FEATURES 1. Scenarios: create and define test cases based on agent directives. 2. Caller Profiles: emulate real user behaviors, including variations in accents, speech speed, and interruption styles. 3. Metrics: utilize custom LLM-related and telemetry scoring to evaluate every interaction. 4. Evaluations: conduct structured testing campaigns that yield pass/fail outcomes along with improvement suggestions. 5. Reviews: incorporate human oversight for corrections, complete with a comprehensive audit trail. This multifaceted approach ensures that voice agents are thoroughly vetted and ready for the complexities of real-world interactions.

Blume serves as a desktop sidecar tailored for AI coding agents, enabling developers to monitor each agent's actions, maintain consistent project context, and identify potential drift before it impacts the codebase. It integrates seamlessly with tools like Cursor, Claude Code, Codex, omp, and Pi, consolidating agent activities, hidden files, skills, hooks, rules, and provider usage into a single interface. The Agents view provides insights into the status of each coding agent, indicating whether they are currently active, have completed their tasks, or are pending approval, while the Setup section reveals the instructions and configuration files that dictate agent behavior. Additionally, usage tracking helps developers keep tabs on remaining plan limits and token usage across various providers like Claude, Codex, and Cursor, thereby minimizing the risk of unexpected disruptions. Blume also securely stores conversation history locally, enabling on-device reviews of rules, skills, and hooks. Its Improve workflow actively identifies recurring points of friction within conversations, groups related issues, and suggests actionable improvements, which may include new rules or the creation of reusable skills, ultimately enhancing the overall coding experience. Furthermore, this continuous feedback loop fosters a more efficient development process, allowing teams to adapt and refine their workflows as needed.

Trace.Space is a platform built on AI principles that streamlines requirements management and traceability, enhancing efficiency in the complex landscape of large-scale product development. It allows teams to seamlessly import requirements, tests, and change logs from various formats and tools, including PDFs, documents, Jira, Git, and APIs, consolidating them into a unified system. By leveraging AI capabilities, it creates trace links, identifies gaps in coverage, and points out inconsistencies among requirements, design artifacts, and testing layers, effectively transforming disparate data into an interconnected, dynamic graph. This trace graph undergoes continuous analysis to unearth potential risks, broken links, and the ramifications of changes, ensuring that teams can proactively address issues before they lead to project delays. Furthermore, Trace.Space fosters real-time collaboration, enabling team members to review, comment on, and approve modifications while preserving comprehensive traceability of decisions and their effects across hardware, software, and systems engineering. This collaborative approach not only improves communication but also enhances the overall quality and reliability of the development process.