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CargoLink is a transportation, shipping and route management tool designed by transportation professionals. Our system allows to automate and integrate your freight management processes. The program has been designed to be as flexible as possible. Thanks to that, it’s capable of fully reproducing processes within the TSL industry. - Fast transport and forwarding orders (creating orders is just a few clicks away) - Transparent invoices and payments (invoicing orders and full cost control) - Verified database of contractors and contacts (CargoLink helps to organize contacts and verify the customer base) - Clear reports and analyses (simpler planning of the company's budget thanks to reports in the system) - Structured communication with the client (communication and sending documents directly from the system to the client) - Full control of fleet management (vehicle and driver management) - Full control of loads and orders (control of load statuses and execution of orders) - Effective debt collection and reminders (automatic payment reminders supporting the settlement of orders)

Wyzer Detective analyzes engineering specification documents and surfaces requirement quality scores with suggested rewrites, logical contradictions ranked by severity, coverage gaps by engineering domain, compliance gaps against standards (AUTOSAR COM today, ISO 26262 and ASPICE on the roadmap), and a dependency graph showing the full web of requirement relationships. Built for systems engineers, spec authors, and program/procurement leads working with dense technical specifications. A requirements defect caught during review costs roughly 29× less to fix than one caught during system testing (IBM Systems Sciences Institute, cited in Boehm & Basili, IEEE Computer 2001) Wyzer Detective moves that catch to before sign-off. Most teams report 70-90% less time on first-pass manual review after adopting it. Under the hood is SHERLOCK, Wyzer's deterministic analysis engine, not a black-box LLM guess, but reproducible, explainable analysis your team can trust in a compliance-sensitive workflow.

Guardamos is a third-party content audit API for AI fitness and wellness products. AI-generated training plans often sound authoritative, but nothing verifies them against actual sports-medicine literature. Guardamos sits between your LLM and your users: send it a generated plan, and it returns a verdict (pass, flagged, or rejected) along with the specific guideline rules the plan was evaluated against. The rule engine is deterministic. Rules are extracted directly from published position statements — ACSM, NSCA, and ECSS — with each rule traced to a verbatim quote in the source document. No LLM sits in the judgment path, so verdicts are reproducible rather than probabilistic. Because a pass verdict returns the evidence it was checked against, teams get a defensible audit record, not just an absence of alerts — increasingly relevant as AI regulation moves toward documented risk management requirements. The deterministic rule engine is open source (MIT). Self-host it, or use the hosted API for genuinely independent third-party verification.

CostMate is estimating software built for New Zealand residential builders — solo operators and two-person crews doing renovations, bathrooms, decks and additions. Labour is calculated using phase-based production rates adjusted for real site conditions (access, height, existing-building constraints, crew size) rather than a flat square-metre rate. BRANZ wastage percentages are applied to material quantities automatically. NZ Building Code requirements — external moisture, wet areas, energy efficiency, timber framing — are built into every estimate by default, not left as a checklist. A quote-checking step reviews the estimate and flags scope commonly missed on residential quotes before it's sent to the client. CostMate runs on a rules engine, not a language model — every quantity, wastage figure and code item is deterministic and reproducible, and can be traced back to why it's there. It is not a quantity surveying package, a project management platform, or a job scheduling tool.

Conovo is an embedded contract generation API for vertical SaaS platforms. It turns data your product already stores into executed contracts, without sending users into a separate document tool. A business uploads the agreement it already sends, in DOCX or PDF. AI proposes which parts vary from deal to deal, and a person confirms every proposal before anything is saved. Those fields bind to your own data model. After setup, no model touches the send path. Resolution, money and date arithmetic, and formatting are deterministic code, so identical inputs always produce an identical document. DOCX filling happens on the original file, preserving clause numbering and formatting. Validation gates every send: an empty required field or inconsistent arithmetic holds a contract as a draft. Each contract pins the template version and values used, so it stays reproducible. Signing runs on your own pages with no vendor branding, and platforms are billed per contract sent.

Sciloop is an AI-powered platform designed to revolutionize scientific research by automating the entire research process, from hypothesis generation to experiment execution and paper drafting. Founded by MIT alumni and International Physics Olympiad medalists, Sciloop aims to accelerate scientific discovery by reducing the manual workload traditionally associated with research. Key Features and Functionality: - Automated Research Workflow: Sciloop handles the entire research lifecycle, allowing researchers to focus on core concepts while the platform manages the rest. - Autonomous Experimentation: The platform runs experiments and iterates based on results, streamlining the research process and saving valuable time. - Cloud-Native Research Agent: Sciloop Lab v_0 is a cloud-native research agent designed to autonomously conduct machine learning research. Primary Value and User Solutions: By automating the complexities of machine learning workflows, Sciloop enables researchers to focus on innovation and discovery. The platform removes the burden of infrastructure and experimentation, leading to faster and more reproducible research outcomes. This approach empowers researchers to accelerate discovery and push the boundaries of scientific knowledge.

Prancer delivers autonomous penetration testing and continuous attack validation for enterprises, MSPs and public-sector teams. Its SwarmHack engine is a swarm of specialized AI agents with 100+ capabilities across 85 plugins. It plans its own attack path, exploits web applications, REST and GraphQL APIs, networks, Active Directory, cloud, containers and CI/CD, then moves laterally, escalates privileges and captures crown jewels. Unlike scanners and breach-and-attack simulation, Prancer validates rather than predicts. Critical severity is reserved for proven exploitation, and every result ships as an evidence-graded report inside a signed authorization envelope. Prancer also tests whether deployed defenses, such as segmentation, Zero Trust, SASE/SSE and identity controls, actually stop the attack. It covers new surfaces such as AI agents, non-human identities, IoT and OT. A deterministic core runs reproducibly and air-gapped, with optional frontier or sovereign models. Compliance evidence maps to SOC 2, ISO 27001, PCI DSS 4.0, HIPAA, DORA and NIS2.

Uptimo tells you whether customers can actually buy, not just whether the server responds. It walks the paths you earn from in a real browser: login, shopping cart, delivery details and the payment gateway. Steps are built from a list or described in one sentence, with no code to write and nothing to install on your side. When a step fails, Uptimo leaves evidence instead of a message: a recording of the run, a screenshot of every step and a HAR file, so a developer can start fixing rather than reproducing. Nine check types cover the rest: HTTP, HTTPS, full page with assets and HAR analysis, DNS, ping, TCP port and SSL certificate expiry. Alerts reach email, Slack, Microsoft Teams, SMS or webhooks, with escalation paths and maintenance windows. SLA reports measure availability to the second and go out weekly or monthly. Four roles and service groups keep agency clients separate. Data sits on a server in Germany. Free plan available; the trial needs no card.

CorpStage ESG 360 is an ESG platform that governs sustainability data from collection to assurance-ready disclosure. It is built on a simple principle: methodology and controls are first-class parts of the system, not afterthoughts, so the numbers in your report can be traced, defended, and audited. Structure ESG data across entities, facilities, and value chains. Document methodology so calculations are reproducible. Build the internal controls, approvals, and audit trail that make ESG data auditable rather than merely reportable. Calculate greenhouse gas emissions across Scope 1, 2, and 3, and map disclosures to ISSB S1/S2, CSRD and ESRS, GRI, TCFD, and TNFD from one data environment. Produce lender packs, investor summaries, board reports, and regulatory submissions that hold up under scrutiny. Built for ESG and sustainability leads, finance teams, and internal audit and risk functions in mid-market companies across APAC, the GCC, and Europe. The output is disclosure backed by verifiable evidence, ready for limited or reasonable assurance.

DataFlow helps researchers, survey agencies, universities, and NGOs detect and resolve data quality issues while fieldwork is still underway. The desktop application connects to ODK Central, SurveyCTO, and SurveyLoopr, and supports importing CSV and Excel datasets. Key capabilities: - Automated checks for duplicates, outliers, interview duration, missing values, logic inconsistencies, GPS issues, and back-check differences. - Local AI assistance without sending survey records to external AI services. - Collaborative project review for research teams. - Gamified quality-control workflows that make resolving issues more engaging. - Traceable, reversible corrections and Stata do-file exports for reproducibility. - Unlimited projects across paid plans. DataFlow is built around research-grade data quality workflows developed through ARCED Foundation's experience conducting field surveys. A sample study containing 600 interviews is available to explore without creating an account. Pricing: $19 per user/month, with a limited launch offer of $99/year for five team members. Website: https://letdataflow.com

Nygen empowers leading experimental biologists to generate deep, actionable insights from their single-cell omics data. Our product suite is designed to help researchers unlock the full potential of single-cell data, driving innovation in clinical trials, diagnostics, and therapeutic decision-making with robust, scalable solutions. Nygen Analytics Our flagship platform for single-cell multi-omics analysis combines no-code pipelines, reproducibility, traceability, and a highly scalable infrastructure. It also supports seamless data hosting and result publishing to streamline your research workflow. Nygen Database An open-access, curated single-cell database that includes datasets spanning hundreds of cell types. Nygen Database enables the exploration and integration of millions of cells, simplifying full-cycle data management and fostering collaboration. Nygen Insights Featuring LLM-augmented knowledge distillation, Nygen Insights automates cell annotations and delivers disease-specific insights. These powerful capabilities accelerate drug discovery and therapeutic advancements by providing researchers with actionable results tailored to their data. Nygen is your complete solution for turning single-cell data into groundbreaking discoveries.

Shieldtag - Smart Hologram Sticker is a product authentication solution for brands that sell through marketplaces, resellers and distributors, where counterfeit and unofficial stock reach buyers who then blame the brand. Every unit carries a smart hologram sticker with its own serialized QR code. A buyer scans it with a smartphone and sees right away whether that unit was issued by the brand. Brand owners manage their codes in Shieldtag’s online dashboard and can see where and how often their products are being verified, which helps them spot suspicious sales channels and back their official sellers. Partners will have access to live dashboard to review distribution and customer interactions. The hologram layer is hard to reproduce with ordinary printing. Because one sticker carries both the brand’s label and its verification, there is no separate label printer and authentication vendor to coordinate. Shieldtag serves more than 400 brands in Indonesia across cosmetics and skincare, food and beverage, pharmaceuticals, agriculture inputs and lubricants, from first production runs to full product lines.

Auraa is Covasant's Databricks-native, agent-driven data platform which is the fastest path to AI-ready data on Databricks. With its conversational AI capabilities in natural language, enterprises can use agents to discover various data sources, build pipelines, enforce data quality, and register everything in Unity Catalog autonomously, from day one. Auraa eliminates pipeline code, engineering backlog, and months of manual configuration. While creation of a datalake on Databricks usually takes more than 18-24 months, Auraa onboards the first source in under 15 minutes, deploys the first use case within hours, and shrinks the overall deployment timeframe to about 8-10 weeks, and reduces the overall implementation cost by upto 70%. Auraa treats every data engineering decision as structured, versioned, governed metadata rather than fragile hand-coded pipelines. Auraa ensures Databricks lakehouse is reproducible, auditable, and continuously improvable by agents The data foundation platfom and services comply with ISO 42001, 27001, 27701, and other data security and privacy requirements such as HIPAA and GDPR in agentic solutions built by the company

Intrudify is an autonomous penetration testing platform that tests web applications and APIs the way a human pentester would, by logging in, mapping the application, and attempting real exploits against every endpoint and parameter it discovers. The workflow runs in four stages. Discover authenticates into the target application and inventories every endpoint, parameter, and authentication flow, including single-page app crawling, MFA, OAuth, and SAML. Attack generates test cases from that inventory and executes exploit attempts rather than matching vulnerability signatures, with AI agents reasoning through business logic to catch issues like IDOR, stored XSS, account takeover chains, and mass assignment. Validate holds every high and critical finding to a reproducible proof-of-concept requirement before it appears in a report. Monitor reruns the full workflow on every code push or a client-set schedule. Coverage spans 100% of discovered endpoints and parameters across 80+ vulnerability classes. Tenant data stays isolated behind AES-256 encryption and per-tenant KMS, with tamper-evident, append-only audit logs retained for 7 years and SIEM export available.

CubeVue is an advanced software platform developed by CurveBeam AI, designed to enhance the analysis and visualization of weight-bearing CT scans, particularly for orthopedic applications. By integrating artificial intelligence and deep learning technologies, CubeVue streamlines the diagnostic process, offering precise and efficient assessments of bone structures. Key Features and Functionality: - SimX : Generates synthesized X-ray views from original CT scans, eliminating the need for additional radiographic exams and reducing patient repositioning time. - Autometrics: Utilizes AI to automatically segment and identify individual bones, calculating key biometrics of the foot and ankle without manual input. - Cloud Integration: Provides a HIPAA-compliant, web-based platform for secure DICOM dataset storage, enabling seamless data access, sharing, and processing across multiple locations. Primary Value and User Solutions: CubeVue addresses the need for efficient, accurate, and reproducible imaging assessments in orthopedic practices. By automating complex measurements and offering instant X-ray views, it significantly reduces operator time and enhances throughput in busy clinical settings. The platform's AI-driven capabilities ensure consistent and precise evaluations, improving patient outcomes and facilitating better-informed treatment decisions.

Setup Studio is a native Windows application for creating setup packages with a clear, script-based workflow. It helps you define your installer project, configure application details, choose installation settings, and build setup packages for your applications — all in one lightweight desktop tool. With Setup Studio, you can manage important installer options such as app information, output paths, installation folders, shortcuts, and package behavior in an easy-to-edit project script. This makes it simple to create reproducible installer builds and maintain your setup configuration over time. Features Create installer projects with a script-based workflow Configure application information and publisher details Define installation paths and output locations Set up shortcuts and installer behavior Manage project settings in a clear text-based format Build native Windows setup packages for your applications Lightweight native desktop experience Why Setup Studio? Setup Studio is designed for developers and software creators who want a straightforward way to prepare Windows setup packages without unnecessary complexity. Its project-based approach makes installer creation easier to organize, edit, and reuse.

KitOps is a packaging, versioning, and sharing system for AI/ML projects that uses open standards so it works with the AI/ML, development, and DevOps tools you are already using, and can be stored in your enterprise container registry. It's AI/ML platform engineering teams' preferred solution for securely packaging and versioning assets. KitOps creates a ModelKit for your AI/ML project which includes everything you need to reproduce it locally or deploy it into production. You can even selectively unpack a ModelKit so different team members can save time and storage space by only grabbing what they need for a task. Because ModelKits are immutable, signable, and live in your existing container registry they're easy for organizations to track, control, and audit. ModelKits simplify the handoffs between data scientists, application developers, and SREs working with LLMs and other AI/ML models. Teams and enterprises use KitOps as a secure storage throughout the AI/ML project lifecycle. Use KitOps to speed up and de-risk all types of AI/ML projects: Predictive models Large language models Computer vision models Multi-modal models Audio models etc...

Wardkey is an offensive security firm. We find the vulnerabilities in AI products and multi-tenant SaaS that automated scanners and checklist audits miss, and we help engineering teams close them before a customer, an auditor or an attacker finds them first. AI applications and agents: LLM-powered features, retrieval pipelines, autonomous agents and MCP integrations. We assess how untrusted input can steer a model into real actions, how data can cross tenant boundaries through retrieval and memory, and whether an agent's permissions match the user it acts for. Voice AI: phone agents that book, change or disclose information on a caller's behalf, tested over real calls for caller verification, access to recordings, tool authorization and abuse of telephony features. Multi-tenant SaaS: authorization and tenant isolation in Next.js, Supabase and Firebase applications, including row-level security, roles, SSO and integrations, the controls enterprise buyers and SOC 2 auditors ask about first. Every engagement is led by a certified tester. Findings are reproduced and evidenced, remediation is delivered as pull requests, and every fix is independently retested. Reports map to OWASP, MITRE ATLAS, SOC 2 and ISO 27001 and close with a signed attestation letter.

SeamUI is an AI image and video generation workspace built for creators, marketers, agencies, and teams that need a faster and more organized way to work with multiple generative AI models. Instead of generating assets one by one through separate AI chat interfaces, SeamUI lets users create image batches in parallel, generate and edit videos, organize outputs into projects, save prompts and model settings as reusable recipes, and keep complete generation histories in one workspace. SeamUI supports leading image and video models from providers including OpenAI, Google, Black Forest Labs, and xAI. Users connect their own API keys, so model usage is billed directly by the provider with no markup from SeamUI. Key features include batch image generation, text-to-video and image-to-video workflows, reusable prompt recipes, project-based asset management, version history, CSV batch generation, team roles, and encrypted API key storage. SeamUI is designed to replace repetitive AI generation workflows with a centralized, scalable workspace where teams can generate, manage, reproduce, and hand off AI creative assets efficiently.

Velo Workspaces is a fully native Mac app — built directly on Apple's own Virtualization.framework, not a QEMU wrapper like most cross-platform VM tools — for running disposable Linux and macOS environments on Apple Silicon. Clone a workspace from a base image in seconds, use it, close it — it's gone, disk included, nothing to maintain. It also has AI Bridge, a VM can reach a model server (like Ollama) running on the Mac's own GPU via Metal, as a plain local network endpoint — so AI agents running inside an isolated Linux VM get local inference with no cloud dependency and no code changes on their end, just a normal chat-completions URL. Velo Workspaces solves problems engineers hit daily. QA engineers reset a clean environment in seconds instead of managing snapshots or re-imaging machines, so destructive testing costs nothing. DevOps teams reproduce a target OS locally before touching CI. AI researchers run models against real data through AI Bridge without uploading anything to a cloud API, keeping proprietary code and datasets on-device while still getting near-native inference speed.