# Clearstone — full public context > Clearstone designs and builds private AI systems, controlled AI agents, and workflow software, and is developing an Agentic Adversary Simulation research pilot starting with digital economies. Canonical site: https://clearstonedigital.com Legal entity: Clearstone Digital Services LLC Jurisdiction: Registered in Wyoming, United States Registered address: 1621 Central Ave, Cheyenne, WY 82001, USA Language: en-US Last reviewed: 2026-08-01 ## Positioning Turn expensive recurring work into dependable systems that teams can operate. Senior-led, specialist-supported architecture and implementation with written boundaries, acceptance evidence, and handover. ## Audiences - Operations leaders: Reduce manual document handling, spreadsheet approvals, disconnected systems, and recurring operational exceptions. - Product leaders: Turn a defined AI or software capability into a reliable production workflow with tests, controls, and ownership. - AI leaders: Improve retrieval, agent reliability, evaluations, permissions, approvals, and deployment boundaries. - Digital-product leaders: Understand how a goal-directed autonomous adversary might pursue value, access, protected data, authority, compute, entitlements, or automated actions across the product's rules and connected systems. - Non-marketing consultancies: Add senior AI and software delivery capacity while retaining the client relationship and commercial ownership. ## Typical reasons to contact Clearstone - Manual document intake or review consumes recurring staff time. - Work moves between email, spreadsheets, and disconnected systems. - Existing automations are fragile, difficult to audit, or hard to maintain. - AI outputs are unreliable, poorly sourced, or missing approval controls. - Executives or specialists repeat work that should be routed through a dependable system. - A product controls value, access, protected data, authority, compute, entitlements, or automated actions through rules a motivated autonomous adversary could try to game. ## Systems Clearstone builds ### Private AI systems Use company knowledge without a public chat tool. Search, retrieve, compare, and draft from approved internal sources with access rules and hosting chosen for the workload. Best when: Your team needs to use internal knowledge with a documented data boundary. Outputs: - Data and trust-boundary design - Source-backed retrieval - Access controls and evaluations ### Reliable AI agents Agents that complete a defined job across your systems. Retrieve records, prepare drafts, update approved tools, and route exceptions using named permissions and human approval. Best when: A recurring task crosses systems and needs tools, approvals, and a run history. Outputs: - Tool and permission registry - Approval and failure paths - Evaluation and audit evidence ### Workflow software Replace handoffs and spreadsheet work with one operating workflow. Applications and integrations for document intake, routing, approvals, exceptions, and recurring operational work. Best when: Documents, approvals, and exceptions are still moving through email or spreadsheets. Outputs: - Defined workflow application - System integrations - Acceptance tests and handover ## Current implementation capabilities ### Agent harnesses & long-running agents Agents that can work beyond a single chat turn. Multi-step agents with resumable state, tool limits, checkpoints, budgets, evaluations, and human escalation for work that may run for minutes, hours, or on a schedule. Examples: - Research and report workflows - Scheduled monitoring and follow-up - Multi-agent or evaluator loops ### MCP servers & integrations Give AI controlled access to the systems where work happens. Typed tools and integration services for company data, APIs, files, and business software—with authentication, permissions, tests, and traceable actions. Examples: - Custom MCP servers - API and data connectors - Tool registries and access control ### Chat & voice agents Conversations that complete a defined job. Customer or internal assistants that answer from approved knowledge, collect structured information, trigger permitted actions, and hand off cleanly to a person. Examples: - Intake and qualification - Scheduling and guided support - Internal workflow assistants ### Private knowledge systems Answers grounded in the sources your team trusts. Access-controlled retrieval, RAG, source-backed drafting, and internal search with the hosting and data boundary chosen for the workload. Examples: - Knowledge assistants - Source-backed research - Document comparison and drafting ### Document & workflow software Focused software for recurring operational work. Applications and services for intake, extraction, routing, review, approvals, exceptions, and handover across document-heavy workflows. Examples: - Document AI pipelines - Approval and exception flows - Operations dashboards and APIs ## Agentic Adversary Simulation ### Agentic Adversary Simulation See how an adversary would game your product. Bring a product you can authorize and one outcome an attacker might pursue. The agent looks for a route. We reproduce what it finds and show your team what to fix first. Status: Design-partner conversations open Disclosure: Design-partner conversations and the founding-pilot waitlist are open. Clearstone is not accepting payment or start dates until the harness, dry run, legal, insurance, specialist review, containment, and dossier gates are complete. No Clearstone client results are claimed yet. Audience: Product, security, and engineering teams operating digital economies. Products where value moves through gift cards, credits, loyalty, subscriptions, virtual goods, refunds, marketplace balances, or entitlements. Selected products where an adversary could pursue access, protected data, authority, compute, or an automated action across product rules and connected systems. Flagship pattern: Synthetic Economy Adversary. A campaign built around one attacker question: can a user make the product issue, preserve, move, or redeem something they should not have? Autonomy: We set the objective, budget, and hard limits. Inside them, the agent chooses its own route. Clearstone operator role: Clearstone is the strategic adversary operator. We set the mission, monitor containment, and decide between rounds whether to redirect or stop. The agent chooses its own ordinary steps inside the approved boundary. Campaign modes: - Autonomous rounds: The standard founding format is three measured-autonomy rounds over ten business days. Clearstone reviews the record after each round, logs every intervention, and decides whether to redirect the next one. Environment: Runs in a sandbox, staging environment, or an approved live product limited to observation and safe proof. - Continuous campaign: Available only by exception under a separate proposal. The agent runs without between-round review while Clearstone monitors the limits and can stop it. Environment: It runs only in an independently isolated sandbox or replica, never on a live product. Agent campaign loop: 01. Set the mission: Clearstone fixes the objective, runtime, compute allowance, environment, and hard boundary. 02. Learn the product: The agent discovers the reachable product surface and builds a working view of roles, states, routes, and trust boundaries. 03. Pursue the route: It forms hypotheses, chooses tools, attempts bounded actions, observes results, and adapts without tactical prompting. 04. Return the evidence: The run stops on its budget, objective, anomaly, or safety condition and returns its decision trail and retained evidence. 05. Decide the next round: Clearstone reviews the changed situation and either redirects, approves more budget, verifies remediation, or stops. Minimum client brief: - An authorized starting target and proof of authority - Test access when the approved environment requires it - Prohibited effects, fragile workflows, and emergency contacts - A sandbox, staging environment, or separately approved live surface - Optional crown jewels or known abuse concerns; a complete target map is not required Campaign budget: The signed proposal fixes the campaign budget, runtime, approved tools, and stop conditions before kickoff. Published starting price: From $3,250 USD. The $3,250–$4,000 example applies to the smallest qualifying design-partner campaign. Larger or custom scopes receive a higher fixed proposal. The entry example combines a $2,500 base with a prepaid, itemized $750–$1,500 tooling allowance that is reconciled afterward. Standard founding campaign: 10 business days and 3 measured-autonomy rounds. Continuous operation is available only by exception under a separate proposal. Paid-campaign condition: This is a paid research campaign. The anonymized research summary is a participation condition, not a substitute for the campaign fee. Extensions: A deeper round or remediation check requires a separately approved extension with its own runtime and compute budget. Authorization boundary: The signed campaign names the authorized starting target, reachable boundary, approved environments, prohibited effects, emergency contacts, budget, and stop conditions. Live boundary: On a live product, the campaign is observe-and-prove only. It cannot change customer-visible state or create third-party, economic, destructive, persistent, or irreversible effects. Continuous boundary: Continuous autonomy runs only in an independently isolated sandbox or staging replica. It uses synthetic identities, no reusable production credentials, allowlisted egress, blocked cloud metadata, complete event capture, and a network-level kill path. It never runs on a live product. Full state-changing execution: Any test that could change customer, authorization, data, resources, automated actions, or economic state runs in an isolated replica with synthetic accounts, canary data, and non-redeemable test value. Stop controls: - Target, actions, network access, runtime, and compute are limited outside the model - Only approved network destinations are reachable; cloud metadata is blocked, and credentials cannot be reused in production - Every action and result is recorded - Clearstone holds kill authority, with a named client contact for emergencies - The run stops immediately if scope becomes unclear, containment is lost, the target becomes unstable, or a real-world effect appears Validated campaign dossier: - What the agent pursued: The initial mission, objectives the agent derived, and any crown jewels your team named. - What the agent reached: The roles, routes, states, tenant boundaries, integrations, and product relationships it actually found. - The route it chose: Its hypotheses, tools, attempted actions, observations, adaptations, and retained evidence. - What worked and what failed: Verified progress, failed paths, false hypotheses, and the conditions that changed the result. - Runtime and controls: Runtime, model and tool compute, analyst-validation effort, escape and persistence attempts, blocked actions, state recovery, detection and kill timing, stop events, and containment status. - Findings that held up: Human-reproduced issues with impact, evidence, limitations, and remediation priorities. - What to do next: A recommendation to stop, redirect, deepen the work, or verify remediation under a new budget. Research publication requirement: Every founding pilot includes a contractually bounded anonymized research summary. We agree the publication boundary before work begins. Client review: The client reviews the draft for confidentiality and factual accuracy before publication. Publication exclusions: - Target identity and identifying product details - Raw exploit instructions, credentials, source code, and confidential architecture - Customer, payment, personal, or other production data - Any material outside the publication boundary agreed before kickoff Evidence status: Clearstone has not delivered this campaign to a client. The harness and paired benchmark remain in build. Evidence disclosure: The sources below are external precedent. They are not a Clearstone result, client delivery, partnership, or endorsement. External precedents: - Anthropic GTG-1002 investigation (Provider-reported malicious campaign): Anthropic reported that an agentic system performed much of the tactical work in multi-stage intrusions while people kept strategic control. Source: https://www.anthropic.com/news/disrupting-AI-espionage - HackerOne AI hackbots update (Authorized field precedent): HackerOne reported that autonomous systems produced findings accepted in authorized programs. Source: https://www.hackerone.com/blog/ai-hackbots-security-testing-update - Hugging Face agent intrusion timeline (Affected-party forensic report): Hugging Face described a 4.5-day campaign with roughly 17,600 actions. The agent escaped an evaluation environment, rebuilt tools, crossed systems, and pursued protected benchmark answers. Source: https://huggingface.co/blog/agent-intrusion-technical-timeline - OpenAI Hugging Face incident disclosure (Operator corroboration): OpenAI confirmed the main sequence reported by Hugging Face. Its investigation was preliminary, and the models were operating with reduced cyber safeguards. Source: https://openai.com/index/hugging-face-model-evaluation-security-incident/ High-level application question: What could an adversary gain or change? - Games, virtual currencies, or digital goods - Gift cards, credits, loyalty, or rewards - Commerce, marketplaces, refunds, or balances - Subscriptions, usage, plans, or entitlements - Accounts, roles, permissions, or tenant boundaries - Data, content, model assets, or proprietary results - Approvals, workflows, integrations, or delegated actions - Another product objective - Not sure yet Limitations: - Denial-of-service, stress, or uncontrolled load - Social engineering, persistence, destructive changes, or real-data exfiltration - Third-party infrastructure without the asset owner's written authorization - Customer-visible, real-money, or irreversible live effects - Incident response, continuous monitoring, certification, or guaranteed outcomes - Claims of exhaustive coverage, complete security, or guaranteed zero-day discovery ## Private AI deployment language - Customer-hosted — choose when "Nothing can leave our infrastructure.": Open-weight models such as Llama or Mistral run on your own servers or VPC—model, search, and application included. Stack: Open-weight models, Your hardware, Zero egress - Customer-owned cloud — choose when "Our own cloud account is fine.": Azure OpenAI, AWS Bedrock, or Google Vertex through private endpoints with zero-retention terms—inside your network. Stack: Private endpoints, Zero retention, Your cloud - Controlled hybrid — choose when "We want the best models available.": Documents and search stay with you; only the minimum context for each request reaches GPT or Claude. Stack: Frontier models, Minimal context out ## Engagements and pricing All starting prices are USD. They are minimum engagement sizes, not fixed quotes. The signed proposal sets final scope, schedule, acceptance terms, and fees before work begins. ### Technical Retainer — From $2,000 USD / month Canonical offer page: https://clearstonedigital.com/offers/technical-retainer Duration: Three-month initial term Eight to twelve senior hours each month for difficult AI and software decisions, with every decision and hour written down. Billing: Billed monthly for the agreed capacity and term. Includes: - Senior Hours — eight to twelve per month, scheduled and tracked in writing - Decision Records — recommendation, evidence, trade-offs, and what was rejected - Technical Working Sessions — model selection, training and evaluation, hosting, agents, and workflow architecture - Capacity Ledger — what was used, what remains, and what carries to next month Use this when: - Your internal team already builds - The decisions are costly to reverse - You want a written decision trail Client inputs: - One agenda owner - Evidence before each session - A team that implements Scope boundaries: - Eight to twelve hours per month over a three-month initial term - Scheduled office hours plus two-business-day async response on agreed questions - Not fractional staffing, emergency response, or on-call at 2 a.m. - An agent, model, MCP, private-cloud, or system-support lane is included only when written into the proposal, bounded by monthly capacity, and governed by a pre-approved external-cost cap - At-cost pilots require a case-study boundary agreed before work begins; client data, examples, prompts, credentials, weights, and confidential results stay outside it - Production rollout is outside the retainer; we hand over at the pilot boundary unless the client asks for a separate scope ## Delivery process 1. Map the workflow: Define the trigger, people, systems, data, exceptions, current cost, and desired result for one recurring workflow. 2. Set evidence and boundaries: Choose the deployment boundary, permissions, approval points, evaluation method, and written acceptance tests. 3. Build and test: Implement the defined software, integrations, retrieval, or agents and test it against the agreed evidence. 4. Handover and measure: Document operation, ownership, failure handling, maintenance, and the measures used after release. ## Industry patterns ### Real-estate operations Property matching, document intake, and approvals in one system. Match client requirements to inventory, process incoming documents automatically, and route every task to the right person for a decision. Examples: - Property and inventory search - Client-requirement matching - Document intake and task routing Boundary: No lead generation, property marketing, automated lending or tenant decisions, investment advice, or professional valuation judgment. ### Legal and matter workflows Matter search, chronology, and drafting with source links. Search, summarize, and draft from matter documents—every output linked to the sources it came from, with review routed to the right lawyer. Examples: - Matter-aware retrieval - Chronology and evidence extraction - Draft and review workflows Boundary: No legal advice, final legal judgment, autonomous filings, or unsupervised external submissions. ## Documented professional work ### TaskTime — 2018–2026 Eight years from product-team engineer to long-term technical consultant. The work began inside TaskTime’s product team in January 2018, grew into senior engineering responsibility, and continued from July 2021 through software delivery, integrations, automation, consultancy, and AI-related assignments. Role: Software Development Engineer → Consultant & Development Specialist Scope: - Full-time product development, progressing into senior responsibility - Complex software delivery and technical consulting - System integrations and workflow automation - AI-related services during the consulting period Verification: TaskTime reference · signed 24 June 2026 · ongoing at issue date ### DataAnnotation — 2020–2022 Expert evaluation for production AI systems. A scoped technical-contributor engagement focused on testing and improving AI outputs through coding, reasoning, prompt, and response-quality work. Role: Technical contributor · AI evaluation Scope: - Coding-response evaluation and error identification - Prompt and response-quality review - Instruction-following and edge-case analysis - Clear written rationales for model feedback Verification: Engagement context available after an initial conversation ### Helm & Nagel / Konfuzio — 2022–2024 Senior engineering inside an enterprise Document AI product team. Contract senior engineering with Helm & Nagel covered enterprise document-processing products from Python and Django backend services through model integration, SDK work, testing, CI, and RAG-based product development. Role: Senior Software Engineer / Developer · contract Scope: - Python and Django services, REST APIs, data models, and asynchronous processing - AI-model and SDK lifecycle work, testing, version management, and CI - NLP, computer vision, and enterprise document automation - Backend services taken from prototype through production release Verification: Helm & Nagel reference · signed 28 June 2026 ### KorrAI — 2022–2024 Product engineering for traceable risk intelligence. Technical consulting around a platform that brings documents, geospatial evidence, and AI-assisted reasoning together for site-risk and engineering review. Role: Technical consultant · product systems Scope: - Document and structured-data workflow design - Geospatial evidence and site-risk interfaces - Traceable reporting, citations, and review paths - Production reliability and technical delivery Verification: Engagement context available after an initial conversation ### Boundless — 2024–2026 Technical leadership for a document-heavy immigration platform. Hands-on product and engineering support for software that guides immigration workflows, checks application documents, and keeps requirements and case status visible. Role: Technical lead & consultant · workflow software Scope: - Eligibility, checklist, and document-intake workflows - Application review and exception handling - Authentication and service integrations - Delivery, testing, and production handover Verification: Engagement context available after an initial conversation ## Workflow case studies The case-study classifications distinguish internal R&D, public reference implementations, and documented professional work. Internal examples are not client outcomes. ### One workspace for projects, scenes, versions, and precise edits. Classification: Internal R&D system Job: Keep a visual story or product narrative coherent while the team develops scenes, compares versions, and requests bounded changes. Workflow: Project → Story → Scene → Annotated change → Approved version Evidence: A working Django application for project, character, story, and scene management with variants, region notes, and video handoff. Boundary: Internal R&D software shown through its real interface. It is not presented as a client deployment or a standalone commercial product. ### Turn visual references into repeatable production rules. Classification: Internal R&D workflow Job: Translate a reference into composition, lighting, camera, materials, and continuity decisions that can be reviewed before generation. Workflow: Reference → Structured direction → Controlled variants → Visual review → Selected scene Evidence: Implemented reference analysis, typed scene layers, variant generation, visual critique, and retained run history. Boundary: The workflow demonstrates production control. Reference rights, creative approval, factual accuracy, and final release remain with the client. ### Build the story around timed beats before rendering every frame. Classification: Internal R&D system Job: Keep scripts, timed beats, visual direction, references, generated media, B-roll, and export packages attached to one story record. Workflow: Script → Timed beats → Visual plan → Media generation → Export package Evidence: Implemented story records, beat planning, reference inputs, generated-media tracking, B-roll management, and export flows. Boundary: Internal production tooling. It demonstrates workflow design and implementation, not audience, campaign, or commercial performance. ### Give agents small tools and clear limits. Classification: Public reference implementation Job: Expose bounded search, calculations, drafting, and system actions through named tools, permissions, tests, and review points. Workflow: Approved request → Named tool → Scoped action → Human review → Run record Evidence: Inspectable real-estate and legal workflow repositories with structured tools, deterministic logic, access boundaries, and tests. Boundary: Reference implementations only; not client outcomes, commercial adoption claims, professional advice, or autonomous decision systems. ### Document workflows engineered for production. Classification: Documented professional work Job: Build backend services and model integrations for enterprise document intake, processing, retrieval, and product use cases. Workflow: Document intake → Asynchronous processing → Model integration → API or SDK → Tested release Evidence: Signed professional evidence covers Python and Django services, REST APIs, asynchronous pipelines, SDK work, testing, model integration, and RAG. Boundary: The public record describes verified engineering scope. It does not claim client metrics, endorsement, or results beyond the signed evidence. ## Generative-media experiments These are labelled internal experiments. They demonstrate working production methods, not client outcomes or audience performance. ### Storyboard first. Animate the frames that work. Classification: Internal motion experiment Job: Turn a brief into structured scenes, compare visual options, approve keyframes, and move only the selected direction into video. Workflow: Brief → Scene plan → Image variants → Selected keyframe → Motion test Evidence: Implemented scene planning, variant generation, selection, image-to-video handoff, and frame extraction. Boundary: AI-generated internal R&D. It demonstrates the production method, not a client result or audience outcome. ### KOZU: one character, twenty connected scenes. Classification: Internal character-world experiment Job: Carry a final character design through a contained workshop story while props, camera positions, and the crystal evolve from scene to scene. Workflow: Character canon → Story phases → Scene layers → Continuity review → Final sequence Evidence: A twenty-scene generated sequence with retained story structure, scene-specific direction, and progressive visual state. Boundary: AI-generated internal R&D. The sequence is shown as a continuity experiment, not client work or a commercial IP claim. ### One approved identity, carried across a brand world. Classification: Internal identity-and-brand experiment Job: Turn a first-party image set into a repeatable production system for portraits, product and brand scenes, connected stories, and short motion. Workflow: Consent & source set → Train & compare → Identity review → Scene system → Motion handoff Evidence: Versioned model experiments, selected stills across distinct environments, longer narrative sequences, and an image-to-video test. Boundary: Internal R&D using a first-party likeness with permission. Not client work, not an audience-performance claim, and not a service for replicating people without consent. ### Train, compare, refine, repeat. Classification: Internal R&D workflow Job: Curate a visual dataset, run controlled generations, compare models and prompts, refine weak assets, and keep the processing history attached. Workflow: Curate → Train or condition → Generate → Compare & refine → Handoff Evidence: Implemented dataset preparation, model comparison, multi-angle generation, selective refinement, and asset provenance. Boundary: A working internal workflow. Results depend on the source material, model, review process, and release criteria agreed for the project. ### Carry a visual world into motion. Classification: Internal motion experiment Job: Use a selected scene as the starting frame, add bounded movement and camera direction, inspect the transition, and extend only when it holds together. Workflow: Selected frame → Motion direction → Short render → Review → Next scene Evidence: Short image-to-video experiments tied back to selected visual frames and reusable scene direction. Boundary: AI-generated internal R&D. The clips demonstrate motion handoff and review, not a finished film or client deliverable. ## Reusable tooling capabilities These capabilities are assembled around a defined workflow and are not sold as a standalone software catalogue. - Plan — Structured brief builder: Product data or references → reusable production brief. Control: Named inputs and reviewable scene layers. - Generate — Coordinated image sets: Approved brief → views, variants, flat lays, and scenes. Control: Batch and version tracking. - Compare — Output evaluation: Candidate outputs → side-by-side selection. Control: Retained inputs and chosen version. - Edit — Targeted visual changes: Selected image + bounded instruction → revised version. Control: Region notes and source lineage. - Enhance — Repair and upscaling: Approved image → corrected delivery asset. Control: Original retained for comparison. - Sequence — Story and scene continuity: Story record → timed beats and connected scenes. Control: Scene-level overrides and review. - Motion — Image-to-video handoff: Approved keyframe → motion job and extracted frames. Control: Start-frame and prompt lineage. - Operate — Job history and retries: Generation events → visible run history. Control: Limits, status, selection, and usage records. ## Evidence boundaries - Selected engagement context is kept concise on the public site and can be discussed after an initial conversation. - Public reference implementations demonstrate engineering structure and are not presented as client adoption or commercial outcomes. - Decorative brand and offer imagery is photorealistic AI/CG conceptual visualization, not photography of physical installations and not evidence of client delivery. - Case-study, studio, generative-media, identity, portrait, logo, and map imagery retains its source-specific role and disclosure; it is not part of the decorative concept-image system. ## Explicit exclusions - Marketing, advertising, media buying, campaign automation, attribution, and ad-tech - Lead generation, publishing, distribution, and audience-growth delivery - Open-ended staff augmentation and 24/7 support - Regulated legal, financial, medical, lending, valuation, or other professional judgment - Autonomous external submissions or production actions without agreed controls - Agentic adversary campaigns without the asset owner's written authorization and signed campaign boundary - Unapproved denial-of-service, persistence, social engineering, destructive effects, or real-data exfiltration ## Common questions ### What does Clearstone build? Agent harnesses, long-running agents, custom MCP servers, chat and voice agents, private knowledge systems, Document AI, workflow software, integrations, and selected generative-media pipelines for defined recurring work. Source: https://clearstonedigital.com/services ### Who is Clearstone for? Operations, product, and AI leaders with expensive recurring workflows, plus non-marketing consultancies that need senior technical delivery capacity. Source: https://clearstonedigital.com/ ### How does an engagement begin? Describe the workflow or application through the contact path. Clearstone reviews fit and, when there is a match, proposes the right engagement—often the Technical Retainer for ongoing senior review. Source: https://clearstonedigital.com/contact ### What is Clearstone's Agentic Adversary Simulation? It is a design-partner research offer for authorized, objective-led product-abuse campaigns. The standard founding format is ten business days and three measured-autonomy rounds. Agents explore permitted routes, Clearstone logs intervention and verifies candidate findings in a separate human reproduction step, and the client receives the route, evidence, limitations, and remediation priorities. No result is guaranteed, and no payment is accepted until every readiness gate passes. Source: https://clearstonedigital.com/offers/agentic-adversary-simulation ### Can the Agentic Adversary Simulation touch a live product? Only with explicit approval and only to observe and prove. Live work cannot create customer-visible, third-party, economic, destructive, persistent, or irreversible effects. Tests that could change customer, authorization, data, resource, automated-action, or economic state use isolated replicas, synthetic identities and accounts, canary data, and non-redeemable value. Source: https://clearstonedigital.com/offers/agentic-adversary-simulation ### What does private AI mean? The privacy claim depends on the selected hosting, model, retrieval, identity, networking, retention, and tool boundary. Customer-hosted, customer-owned cloud, and controlled hybrid patterns are described separately. Source: https://clearstonedigital.com/services ### Does Clearstone provide legal or financial judgment? No. Systems can support research, extraction, drafting, routing, and review, but regulated professional judgment and final decisions remain with qualified people. Source: https://clearstonedigital.com/services ### What do the case studies demonstrate? They show how structured inputs move through creative-production software, visual review, agent tooling, and document workflows. Each study is labelled as internal R&D, a public reference implementation, or documented professional work. Source: https://clearstonedigital.com/case-studies ### What does the generative-media work demonstrate? It shows storyboard-first video planning, character and scene continuity, consent-based identity and brand production, controlled visual-model workflows, human review, and image-to-video handoff through labelled internal experiments. Source: https://clearstonedigital.com/generative-media ### What proof is available? The Work page presents bounded professional-work facts, public reference implementations, and a path to request original professional references after a first conversation. Source: https://clearstonedigital.com/work ### How can a prospect contact Clearstone? Send a short message through the contact page or email directly; a brief intro call is scheduled after review when there is a match. Credentials, target details, vulnerability evidence, client-owned production material, and regulated information should not be submitted through the form. Source: https://clearstonedigital.com/contact ## Public routes - [Clearstone — AI, Software & Agentic Adversary Research](https://clearstonedigital.com/): Clearstone builds private AI systems, controlled agents, and workflow software, and is developing an Agentic Adversary Simulation research pilot starting with digital economies. - [AI, Software & Agentic Adversary Services — Clearstone](https://clearstonedigital.com/services): Private AI systems, controlled agent harnesses, workflow software, integrations, and an active Agentic Adversary Simulation research pilot. - [Agentic Adversary Simulation | Clearstone](https://clearstonedigital.com/offers/agentic-adversary-simulation): Authorized, research-stage adversary campaigns starting with digital economies and selected products where access, data, authority, compute, entitlements, or automated actions are at stake. - [Technical Retainer — AI & Software Systems — Clearstone](https://clearstonedigital.com/offers/technical-retainer): A monthly AI systems retainer for senior review, agent systems, private cloud AI, MCP and system support, and optional open-weight model training—with written capacity, cost caps, and handover. - [AI Agents, MCP & Product Engineering Work — Clearstone](https://clearstonedigital.com/work): Documented AI and software work plus agent harnesses, long-running agents, MCP servers, chat and voice agents, private knowledge systems, Document AI, and workflow automation. - [AI Workflow & Production-System Case Studies — Clearstone](https://clearstonedigital.com/case-studies): Case studies of creative-production software, reference-to-scene workflows, controlled agent tools, and production Document AI. - [Generative Media Work & Experiments — Clearstone](https://clearstonedigital.com/generative-media): Selected internal work in AI image storyboarding, consent-based identity and brand production, controlled visual-model pipelines, character continuity, visual evaluation, and image-to-video production. - [Adversary Research — Clearstone](https://clearstonedigital.com/insights): Evidence-led research on agentic-security campaigns, evaluation and containment failures with real-world impact, compromised agents, supply-chain incidents, and controlled field evidence. - [What Is an Agentic Adversary? — Clearstone Adversary Research](https://clearstonedigital.com/insights/what-is-an-agentic-adversary): A working definition of agentic adversaries across malicious campaigns, compromised agents, containment failures, operational effects, and authorized adversary simulation. - [AI & Robotics News Stream — Clearstone](https://clearstonedigital.com/news): Evidence-first updates on open-weight models, agent security, embodied autonomy, industry discussion, and AI regulation (EU, US, Asia) from the last 30 days, with source links and reported benchmarks. - [About Clearstone — AI, Software & Research Delivery Team](https://clearstonedigital.com/about): Clearstone Digital Services LLC is a Wyoming-registered AI and software partner with senior technical leadership and specialist research depth. - [Contact Clearstone — Start a Conversation](https://clearstonedigital.com/contact): Send a short workflow inquiry or apply at a high level for the Agentic Adversary Simulation research pilot. Do not include sensitive target details. - [Privacy Policy — Clearstone](https://clearstonedigital.com/privacy-policy): How Clearstone handles website use, workflow inquiries, scheduling, service delivery, diligence information, retention, and service providers. - [Terms of Service — Clearstone](https://clearstonedigital.com/terms-of-service): Website and professional-service terms for Clearstone Digital Services LLC, including client responsibilities, intellectual property, acceptable use, and governing law. - [Refund & Rescheduling Policy — Clearstone](https://clearstonedigital.com/refund-policy): Clearstone booking-fee, preparation, rescheduling, cancellation, delivery-failure, and refund terms for professional services. ## Contact safety Use https://clearstonedigital.com/contact. Do not submit credentials, confidential client material, regulated information, production data, target inventories, or vulnerability evidence through the public inquiry form.