Decker is an AI-powered deliverable enablement and monetization platform built for consultants, investors, finance professionals, operators, analysts, and knowledge workers who produce high-value business outputs as part of their daily workflows. It helps professionals transform fragmented source material into structured, presentation-ready deliverables while also creating systems to capture, operationalize, and eventually monetize professional expertise over time.
Modern professional workflows are often spread across disconnected documents, spreadsheets, PDFs, transcripts, notes, screenshots, emails, research repositories, and unstructured client inputs. Decker is designed to centralize and operationalize those workflows into one AI-assisted environment where professionals can generate, review, refine, annotate, and manage sophisticated business deliverables using structured workflows rather than generic chat interfaces.
The platform combines specialized AI agents, AI-assisted document generation, analytical sheets, knowledge-base chat, review workflows, source preparation pipelines, transcript extraction, redaction systems, structured annotations, brand templates, practical learning systems, curated tools, community workflows, and expert-assisted operations into one integrated platform. Instead of forcing professionals to move between disconnected tools for drafting, modeling, editing, formatting, analysis, and collaboration, Decker provides a unified environment optimized specifically for professional knowledge work.
Users can create a wide range of high-value deliverables, including MBB-style strategy presentations, investment committee memos, private equity investment memos, diligence summaries, business requirement documents, ERP implementation packs, operational reports, financial analyses, analytical models, DCF models, market maps, executive summaries, review tables, business reports, infographics, research summaries, client presentations, workflow documentation, and structured business analyses. Deliverables are designed to be editable, reviewable, and adaptable to real-world consulting and operational workflows rather than static AI-generated outputs.
Decker supports workflows that begin with messy and fragmented inputs. Users can upload files, transcripts, spreadsheets, recordings, notes, screenshots, templates, data rooms, research repositories, and internal knowledge sources into structured workspaces. The platform helps organize, clean, annotate, redact, classify, and transform those inputs into reusable context for AI-assisted generation and business analysis workflows.
The platform also emphasizes reviewability and professional control. AI-generated outputs can be refined through structured editing systems, annotations, human feedback loops, review tables, comparison workflows, and iterative revisions. Professionals remain in control of final judgment while using AI to accelerate drafting, formatting, analysis, synthesis, and repetitive operational tasks.
Unlike generic productivity AI tools, Decker is designed around professional deliverables and operational outputs. The platform focuses on workflows where quality, structure, formatting, analytical consistency, reasoning, and presentation standards matter. Specialized workflows and domain-oriented generation systems help users create outputs that align more closely with consulting, finance, operations, strategy, and enterprise communication standards.
Decker also includes systems for practical learning and expertise development. Professionals can study examples, reusable frameworks, analytical structures, business templates, and workflow patterns derived from real operational use cases. Community-driven workflows and curated tools help users improve the quality and consistency of deliverables while reducing repetitive work.
A core part of Decker’s long-term vision is enabling professionals to convert expertise into reusable and monetizable intelligence assets. Through opt-in data labeling, annotation, reasoning capture, structured revisions, and workflow tagging systems, users can preserve the reasoning, iterations, examples, edits, and final outputs behind their work. This allows selected deliverables, datasets, annotations, and workflow intelligence to become training-ready assets for future AI systems and operational knowledge repositories.
The platform is designed to support both individual professionals and collaborative enterprise workflows. Teams can standardize templates, operational frameworks, review processes, deliverable structures, and branded reporting systems while maintaining governance and collaboration controls across projects and stakeholders.
Decker is ultimately designed to help professionals produce better outputs today while creating long-term value from the expertise embedded inside their workflows. By combining AI-assisted deliverable generation, analytical systems, structured review workflows, operational tooling, knowledge capture, annotations, reusable frameworks, and expertise monetization infrastructure into one environment, Decker helps transform professional judgment from a temporary service into a reusable, scalable, and operational intelligence asset.
Tuning Engines is a unified AI control, orchestration, and governance platform designed for organizations building production-grade intelligence systems across models, agents, tools, workflows, and fine-tuned AI infrastructure. It provides a centralized operational layer for managing the complete lifecycle of enterprise AI systems, enabling teams to move beyond fragmented AI tooling and isolated experiments into scalable, governed, observable, and production-ready AI operations.
Modern AI systems are no longer limited to single models or isolated chat interfaces. Organizations increasingly operate across multiple providers, custom fine-tuned models, coding agents, inference gateways, retrieval systems, workflow automations, MCP tools, and autonomous agents that interact with internal systems and external APIs. Tuning Engines is built to unify those systems into one extensible platform where intelligence can be routed, governed, monitored, optimized, evaluated, and operationalized consistently across teams and environments.
The platform brings together the core operational components required for modern AI infrastructure, including inference management, model routing, fallback orchestration, fine-tuning pipelines, evaluation systems, datasets, custom models, agent management, MCP servers, reusable skills, guardrails, runtime traces, observability, usage analytics, billing controls, API management, and team governance. Instead of managing disconnected AI providers, gateways, tooling stacks, and evaluation systems separately, organizations can manage AI infrastructure through a single governed control layer.
Tuning Engines supports both hosted and customer-managed AI ecosystems. Teams can connect commercial providers, open-source models, internal inference systems, custom deployments, and future model providers through unified APIs and routing policies. The platform includes OpenAI-compatible APIs, Anthropic-compatible routes, CLI workflows, MCP integrations, coding-agent connectivity, and resource catalogs for models, agents, tools, prompts, and reusable skills. This allows developers to integrate existing AI workflows without rebuilding tooling around proprietary infrastructure patterns.
The platform is designed to work with modern AI developer ecosystems and coding-agent workflows. Teams can connect tools such as Claude Code, OpenCode, Aider, Cline, Roo, Continue.dev, Cursor, VS Code, Windsurf, and other agent-driven development systems into a single governed environment. Tuning Engines acts as the orchestration and governance layer between developers, models, agents, APIs, tools, credentials, and operational policies.
Organizations can manage model routing and execution policies through centralized governance systems. Requests can be routed across multiple providers and models based on cost, latency, quality, availability, capabilities, budgets, or organizational rules. Teams can define fallback policies, traffic distribution logic, provider priorities, model weights, and routing profiles while maintaining observability into every request and execution path.
Tuning Engines also supports fine-tuning and custom model operations. Organizations can manage datasets, launch fine-tuning jobs, import or export models, evaluate outputs, track experiments, and operationalize custom intelligence systems from within the same platform. Evaluation systems support benchmarking, scoring, comparison workflows, runtime analysis, and structured quality measurement for both models and agentic systems.
Governance and operational control are deeply integrated into the platform. Administrators can define role-based access controls, API key permissions, team structures, usage budgets, rate limits, credential sources, guardrails, policy-as-code rules, AGT YAML policies, tenant isolation boundaries, audit systems, and operational governance controls required for enterprise deployments. Every request, inference, tool call, and agent action can be traced, logged, analyzed, and governed through centralized observability systems.
The platform also supports runtime data capture and operational intelligence workflows. Teams can capture successful interactions, prompts, traces, outputs, feedback loops, metadata, and evaluation signals to continuously improve models, workflows, agents, and operational systems over time. This creates a feedback-driven intelligence layer where organizations can optimize quality, reliability, governance, and cost efficiency across AI deployments.
Tuning Engines is designed for organizations building long-term AI capabilities rather than temporary experiments. It helps enterprises standardize how models, agents, workflows, tools, prompts, and operational policies are deployed and governed across teams. By combining inference management, fine-tuning, evaluations, routing, observability, governance, MCP infrastructure, developer tooling, and operational controls into one extensible system, Tuning Engines enables organizations to build secure, scalable, cost-aware, and production-ready AI operating environments capable of supporting the next generation of enterprise intelligence systems.
Pulselake is an AI-native, ontology-driven operating system designed for research operations, assessments, evidence management, evaluations, benchmarking, and insight delivery. It helps organizations centralize the full lifecycle of research, feedback collection, structured intelligence, and operational decision-making into one unified platform. Pulselake is built for research teams, consulting firms, customer experience teams, employee experience programs, product organizations, strategy teams, market research firms, and enterprises that need scalable systems for collecting, organizing, analyzing, governing, and operationalizing human intelligence.
Traditional research platforms often fragment workflows across disconnected survey tools, spreadsheets, reporting software, annotation platforms, dashboards, and collaboration systems. Pulselake replaces fragmented tooling with a unified operational layer that supports structured research workflows from initial study design to final insight delivery. Teams can design studies, launch surveys and assessments, manage respondents and collaborators, collect quantitative and qualitative evidence, analyze structured and unstructured data, generate reports, benchmark results, and deliver insights through dashboards, portals, exports, and automated workflows inside one environment.
Unlike traditional survey and form platforms, Pulselake organizes research around reusable ontologies. Instead of treating every survey or assessment as an isolated artifact, the platform structures intelligence using dimensions, sub-dimensions, benchmarks, scoring frameworks, evidence models, taxonomies, respondent segments, metadata structures, question libraries, and reusable analytical frameworks. This ontology-driven architecture allows organizations to compare research across time periods, departments, customers, markets, teams, products, and studies while maintaining consistency in interpretation and reporting.
Pulselake supports a wide range of research and assessment workflows, including maturity assessments, operational diagnostics, customer experience studies, employee engagement assessments, product research, pricing research, voice-of-customer programs, qualitative interviews, benchmarking exercises, market research, strategic evaluations, onboarding diagnostics, and flexible enterprise research workflows. Teams can also conduct MaxDiff analysis, conjoint analysis, Van Westendorp pricing research, Gabor-Granger studies, scoring-based evaluations, and structured decision-support assessments inside the same operational environment.
The platform combines quantitative analytics with qualitative evidence management. Organizations can collect open-ended responses, interviews, transcripts, screenshots, uploaded files, observations, behavioral metadata, and structured feedback alongside traditional survey data. Pulselake enables teams to analyze qualitative evidence using AI-powered thematic clustering, categorization, tagging, extraction, summarization, and ontology-based structuring while maintaining traceability back to original evidence sources.
Pulselake also functions as a client and collaboration operating system for research-driven organizations. Teams can manage clients, stakeholders, projects, permissions, portals, contributors, reviewers, and collaborators through centralized workspaces designed for operational transparency and governed access. White-labeled portals and dashboards allow organizations to deliver branded research experiences and ongoing insight access to clients, departments, or external stakeholders.
AI is integrated throughout the platform to support automation, analysis, and operational scale. Pulselake includes AI-assisted survey analysis, automated insight generation, report drafting, evidence summarization, response categorization, comparative analysis, trend identification, benchmark interpretation, and narrative generation. Teams can use AI to accelerate research synthesis while preserving human review, traceability, and governance across decision-making workflows.
The platform is also designed for human-in-the-loop AI evaluation and operational intelligence workflows. Organizations can run annotation pipelines, expert reviews, consensus scoring, classification tasks, evaluation studies, and structured review processes using shared ontologies and governed evidence systems. This makes Pulselake suitable not only for traditional research operations, but also for AI evaluation, model assessment, RLHF workflows, and enterprise human intelligence systems.
Pulselake emphasizes reusable knowledge rather than isolated studies. Research data, evidence, insights, and analytical structures become part of a continuously expanding intelligence layer that organizations can query, benchmark, operationalize, and reuse over time. Instead of producing static reports that become disconnected from future workflows, Pulselake helps organizations build continuously evolving research systems that support long-term organizational learning, operational visibility, and decision intelligence.
By combining ontology-driven research infrastructure, AI-assisted analytics, operational workflows, evidence management, collaboration systems, dashboards, benchmarking, and automated reporting into one unified environment, Pulselake enables organizations to move from fragmented research execution toward scalable, repeatable, and production-ready intelligence operations.
HyperLake is built for organizations preparing for a world where AI agents become primary users of infrastructure.
Today, most enterprise infrastructure was designed for humans, dashboards, applications, and scheduled pipelines. AI agents behave differently. They query data, call tools, trigger workflows, generate artifacts, operate across systems, and require continuous access to governed compute, data, policies, and services.
HyperLake provides the command center to deploy, manage, secure, govern, and operate agentic infrastructure at enterprise scale. The first product wedge is Agentic Data Cloud Infrastructure: open-stack data, analytics, semantic, workflow, and agent infrastructure deployed directly inside the customer’s own VPC, private cloud, or on-prem environment.
The broader vision extends beyond a single stack. HyperLake is designed to manage multiple agentic infrastructure stacks simultaneously, including HyperLake-native deployments, customer-owned cloud services, AWS/GCP/Azure-native components, open-source technologies, governed data systems, workflow platforms, MCP tools, and future production-ready agentic applications.
HyperLake combines sovereign AI infrastructure, federated data systems, governance frameworks, and autonomous workflow infrastructure into a unified operational layer for enterprise AI. The platform supports open technologies such as Apache Iceberg, Trino, PostgreSQL, Kubernetes, vector databases, streaming systems, and cloud-native object storage, enabling organizations to avoid vendor lock-in while maintaining deployment flexibility across environments.
Governance is embedded directly into the runtime through RBAC, ABAC, row-level security, policy enforcement, provenance logging, immutable audit trails, and secure access controls for both humans and AI agents. HyperLake also supports governed retrieval pipelines, agentic analytics, real-time streaming, AI-ready APIs, semantic services, and secure data access layers that allow agents to safely reason over enterprise systems without uncontrolled infrastructure access.
The platform is designed to support the next generation of enterprise AI workloads, including autonomous agents, AI copilots, retrieval systems, multi-agent orchestration, governed workflow automation, and real-time operational intelligence. Organizations can unify structured data, vector retrieval, APIs, semantic layers, and agent runtimes into a single governed environment while scaling new AI use cases without rebuilding the operating layer each time.
HyperLake is built for organizations preparing for a world where AI agents become primary users of infrastructure.
Today, most enterprise infrastructure was designed for humans, dashboards, applications, and scheduled pipelines. AI agents behave differently. They query data, call tools, trigger workflows, generate artifacts, operate across systems, and require continuous access to governed compute, data, policies, and services.
HyperLake provides the command center to deploy, manage, secure, govern, and operate agentic infrastructure at enterprise scale. The first product wedge is Agentic Data Cloud Infrastructure: open-stack data, analytics, semantic, workflow, and agent infrastructure deployed directly inside the customer’s own VPC, private cloud, or on-prem environment.
The broader vision extends beyond a single stack. HyperLake is designed to manage multiple agentic infrastructure stacks simultaneously, including HyperLake-native deployments, customer-owned cloud services, AWS/GCP/Azure-native components, open-source technologies, governed data systems, workflow platforms, MCP tools, and future production-ready agentic applications.
HyperLake combines sovereign AI infrastructure, federated data systems, governance frameworks, and autonomous workflow infrastructure into a unified operational layer for enterprise AI. The platform supports open technologies such as Apache Iceberg, Trino, PostgreSQL, Kubernetes, vector databases, streaming systems, and cloud-native object storage, enabling organizations to avoid vendor lock-in while maintaining deployment flexibility across environments.
Governance is embedded directly into the runtime through RBAC, ABAC, row-level security, policy enforcement, provenance logging, immutable audit trails, and secure access controls for both humans and AI agents. HyperLake also supports governed retrieval pipelines, agentic analytics, real-time streaming, AI-ready APIs, semantic services, and secure data access layers that allow agents to safely reason over enterprise systems without uncontrolled infrastructure access.
The platform is designed to support the next generation of enterprise AI workloads, including autonomous agents, AI copilots, retrieval systems, multi-agent orchestration, governed workflow automation, and real-time operational intelligence. Organizations can unify structured data, vector retrieval, APIs, semantic layers, and agent runtimes into a single governed environment while scaling new AI use cases without rebuilding the operating layer each time.
HyperLake also enables enterprises to deploy applications and services closer to where data resides, reducing operational friction, latency, and unnecessary data movement across environments. The platform is designed for enterprises that require secure AI adoption without sacrificing governance, compliance, infrastructure portability, or operational control. It also supports hybrid infrastructure strategies, allowing enterprises to integrate existing systems, cloud-native services, and future AI infrastructure layers into one governed operational environment. By operating entirely inside customer-controlled infrastructure, HyperLake enables organizations to build sovereign, auditable, secure, and production-ready AI systems with full observability, compliance, operational control, infrastructure portability, and long-term architectural flexibility for rapidly evolving AI ecosystems.
