Sovereign AI Infrastructure.
Zero Token Fees.
Absolute Privacy.
Zero Token Labs deploys production-ready AI systems inside infrastructure you control. They securely read your business data, follow your operating rules, and perform real work across sales, marketing, support, operations, and compliance without exposing sensitive information or charging for every token.
The framework
Private enough to know your business. Governed enough to act on it.
The most valuable AI applications need access to information companies cannot casually upload to a third party: CRM history, customer conversations, contracts, internal policies, support records, financial data, and operating procedures. Zero Token Labs brings the model to that information instead of sending the information away.
Your data stays
Sensitive company information remains inside customer-controlled infrastructure. No shared application database and no unnecessary third-party model exposure.
Your rules decide
Every system follows explicit permissions, confidence thresholds, approval requirements, and business rules. High-risk actions are held for a human instead of guessed.
Every action is accounted for
Important conclusions and actions retain their source evidence, confidence, decision path, previous value, new value, and audit history.
Production systems
Finished products, deployed on infrastructure you control
We do not hand customers an empty agent builder and ask them to assemble the value themselves. Zero Token Labs deploys complete business systems with integrations, interfaces, permissions, guardrails, review queues, and operational support already designed around the job they perform.
Deal Ledger
Your CRM, finally current.
Deal Ledger reads sales calls, emails, and approved Slack conversations, extracts supported deal information, checks every proposed change against your rules, and stages the approved updates to Salesforce with a complete audit trail.
Key capabilities
- Conversation-to-CRM field extraction
- Source sentence and confidence attached to every value
- Field-level permissions and stage-transition rules
- Account, contact, and opportunity resolution
- Sandbox-first write-back
- Human review for ambiguous or high-risk changes
- Visible accepted and rejected actions
Drafting Room
Every message, in context.
Drafting Room reads the prospect's public website and your private Salesforce relationship history, determines whether the situation calls for a first touch, active-deal follow-up, win-back, or expansion message, and prepares the appropriate draft for human approval.
Key capabilities
- Website and account research
- Salesforce relationship-history matching
- First-touch, follow-up, win-back, and expansion postures
- Approved internal context without exposing private CRM notes
- Human review before export
- Compatible with existing outbound sequencing tools
- Read-only, least-privilege CRM access
Promise Track
Every promise, on the record.
Promise Track captures every commitment made in a call, email, or thread in its exact words, links it to the account and the owner, and chases it to fulfillment with reminders, escalations, and a visible audit trail before the customer has to ask where it is.
Key capabilities
- Verbatim commitment extraction with source sentence
- Confidence scoring and two-tap human review
- Classifier separates commitments from suggestions and inbound requests
- Entity resolution to account, project, and owner
- Reminders before deadlines, escalations on silence
- Fulfillment log with reply-detection closeout
- On-premise extraction, ledger in your own Postgres
The platform
One sovereign foundation. Multiple business systems.
Each Zero Token Labs product runs on a shared private application layer. Once the foundation is deployed, additional workflows can use the same identity controls, connectors, evidence store, guardrails, review system, and audit infrastructure.
Private connectors
Salesforce, email, Slack, Teams, document stores, databases, ticketing systems, and approved internal applications.
Permission-aware access
The AI only sees information the requesting user or service account is authorized to access.
Evidence-backed intelligence
Important outputs remain connected to the exact source content that supported them.
Governed execution
Actions are validated against deterministic company rules before they reach production systems.
Human approval
Sensitive fields, low-confidence conclusions, and high-impact actions enter a review queue.
Complete auditability
Every read, extraction, decision, approval, rejection, and system write is logged.
Model flexibility
Use customer-approved open-weight models selected for the workload, hardware, latency, and security requirements.
Dedicated deployment
Customer-specific infrastructure, credentials, storage, models, configurations, and monitoring.
Product roadmap
The private AI operating layer for the entire business
Deal Ledger and Drafting Room are the first deployed systems. The same sovereign architecture can support additional governed applications across revenue, marketing, customer operations, legal, compliance, and internal knowledge.
Account Atlas
A continuously updated, evidence-backed view of every customer and prospect relationship.
Market Echo
Turns sales calls, customer interviews, support conversations, and loss notes into private voice-of-customer intelligence.
Signal Forge
Builds campaign audiences from CRM, customer, product, and engagement signals without exporting the underlying data.
Brand Sentinel
Checks marketing content against approved claims, brand standards, legal requirements, and internal policy before publication.
Renewal Radar
Identifies customer risk, unresolved commitments, upcoming renewals, and expansion opportunities.
Knowledge Mesh
Permission-aware internal search that answers with citations and says when the available evidence is insufficient.
The sovereign foundation
The infrastructure is what makes the applications private, predictable, and yours
Cloud AI applications commonly meter usage, impose platform limits, and require sensitive business context to cross organizational boundaries. Zero Token Labs deploys dedicated inference and application infrastructure under the customer's control, creating predictable economics, stable model behavior, and direct ownership of the operating environment.
Rented cloud AI
Metered, shared, and vendor-owned
Per-token pricing. Data crosses organizational boundaries. You rent access.
- Uncapped API bills
- Sensitive context leaves your perimeter
- Rate limits and throttling
- Vendor lock-in and deprecation risk
- Model behavior can change without notice
Zero Token sovereign deployment
Dedicated, governed, and yours
Customer-controlled infrastructure. Your data, your models, your audit trail.
- No per-token application costs
- Customer-controlled data residency
- Ownership of code, weights, and infrastructure
- Perimeter-contained inference
- Air-gap capable, dedicated deployment
- Cost modelCloudPer-token, uncappedSovereignFlat monthly retainer
- Data residencyCloudVendor cloud + subprocessorsSovereignCustomer-controlled, air-gap capable
- Latency floorCloudNetwork + queue variableSovereignDeterministic local inference
- Scaling ceilingCloudRate-limited by tierSovereignHardware ceiling you control
- Model choiceCloudVendor menu + deprecation riskSovereignAny customer-approved open-weight model
- Ownership at exitCloudCancelled = access revokedSovereignCode, weights, and node stay yours
Engineering programs
Deployment and engineering services
When a standard Zero Token product does not cover the required workflow, our engineering team extends the same sovereign platform with custom integrations, skills, interfaces, rules, and applications.
Sovereign Enterprise AI Swarms
100% On-Premise. Zero Token Overhead. Zero Cloud Leaks.
Autonomous Micro-SaaS Production Studio
Continuous Code-Compile-Debug Iteration Loops at $0 Variable Cost.
Enterprise OpenClaw Skill Engineering
Deep Systems Integration for Legacy ERP, CRM, and DB Core Systems.
Performance Programmatic SEO Engines
Scrape, Research, Outline, and Publish Thousands of Pages with No LLM Billing Penalties.
Sovereign Core Air-Gapped Appliances
Dedicated Physical Iron. Zero Cloud Footprint. Absolute Data Custody.
Infrastructure proof
Proof that the infrastructure can operate continuously under real consequences
Instinct Holdings is Zero Token Labs' internal production environment. It uses the same customer-owned architecture, continuous agent execution, state recovery, monitoring, integration, and fault-tolerance principles applied to client deployments. The case study demonstrates infrastructure reliability rather than the primary market focus of the company.
Overview
Our own internal deployment, on the same sovereign architecture.
Instinct Holdings is an internal production environment built and run by our founder. It uses the same customer-controlled architecture, connectors, evidence store, monitoring, and fault-tolerance principles applied to client deployments: flat economics, no per-token metering, full ownership of the stack, running across four asset classes simultaneously. It is included here as infrastructure proof, not as the primary commercial focus of the company.
The Challenge
Institutional reliability, without an enterprise team
Running real capital across multiple asset classes and brokers requires institutional-grade reliability: crash recovery, accurate position reconciliation, coordinated capital allocation across dozens of concurrent processes, and fault-tolerant execution, without the cost of an enterprise engineering team or the unpredictability of metered cloud AI billing on every decision.
Deployment Scale
24+ autonomous bots running concurrently across four asset classes and three brokers, on infrastructure we own.
Hyperliquid
Crypto Perpetuals
12 tokens · 10x leverage · 15m execution
- 30m and 1h higher-timeframe confirmation gates before entry
- Shared WebSocket price-feed layer serving all bots from a single poller, avoiding per-bot API rate limits
- Fixed risk parameters with trailing exit management per position
Schwab API
0DTE Options
12 tickers across major indices and large-cap equities
- Macro regime classification gate adjusts allocation based on volatility conditions before any trade is sized
- Shared beginning-of-day equity snapshot across all bots prevents position-sizing drift
- Trailing stop exit management rather than fixed profit targets
Interactive Brokers
Forex
6 currency pairs · 5m/15m execution
- Position sizing corrected for non-USD-denominated pairs
- Automatic reconnection with exponential backoff on broker API drops
CME Micros
Futures
6 contracts via automated execution bridge
- Same shared-capital and state-persistence architecture as the other three fleets
System Architecture
Three layers, one coherent fleet
Shared State Layer
File-based state persistence and shared capital pools keep every bot in a fleet working from consistent, up-to-date account and position data.
Fleet Orchestration
A central launcher manages subprocess lifecycle across every bot in a fleet, with crash recovery and orphan position detection running continuously in the background.
Monitoring & Attribution
Web dashboards provide real-time fleet status, and post-session attribution tools break down performance and gate decisions after every trading session.
Engineering Highlights
Reliability and risk architecture
Crash Recovery
Full state persistence means any bot can restart mid-session without losing position awareness.
Orphan Position Detection
Automatic reconciliation catches and resolves positions the system would otherwise silently mismanage.
Atomic State Writes
File-level write safety prevents corrupted state during concurrent updates across dozens of running processes.
Automatic Reconnection
Exponential backoff reconnection logic keeps execution live through broker API interruptions without manual intervention.
Shared Capital Accounting
Beginning-of-day equity snapshots keep position sizing consistent across every bot, even when each queries live account state independently.
AI Trade Approval Gate
A model-based final review layer checks every trade against session context and a confidence threshold before execution, a second set of eyes with no human latency.
Regime-Aware Risk Overlay
A macro conditions classifier adjusts capital allocation dynamically rather than trading fixed size regardless of market conditions.
Realistic Execution Modeling
Custom fill-simulation logic replaced idealized backtest assumptions, correcting for slippage and pricing effects the naive models missed.
Continuous Hardening
Infrastructure reliability is treated as an ongoing discipline, not a one-time build. A full audit cycle across the crypto fleet identified and remediated dozens of issues, ranging from execution edge cases to state-reconciliation gaps, before the current engine version was put into live operation. Every fix is verified against real historical market data before deployment, never assumptions.
38
Issues identified and resolved in a single audit-and-remediation cycle.
Results
What the infrastructure delivers in live operation
Uptime
24/7
Across all four asset classes, with automatic reconnection and crash recovery keeping bots live through broker API interruptions.
Concurrent bots in live operation
24+
Autonomous processes running simultaneously across three brokers and four asset classes.
Time in live operation
6 mo
Live since January 2026, continuous operation across crypto perpetuals, 0DTE options, forex, and futures.
Brokers / venues integrated
3+
Hyperliquid, Schwab, Interactive Brokers, plus CME micro futures execution.
“This isn’t paper trading. Every bot in this fleet runs on real capital, live, around the clock. That means the infrastructure has to be right the first time, every time. I don’t want a cloud vendor’s rate limit deciding whether a position gets reconciled at 3am. That’s why I built this on hardware I own.”
Founder, Instinct Holdings
Who this is for
Built for organizations whose most valuable context cannot become vendor data
Revenue organizations
CRM history, customer conversations, pricing discussions, pipeline data, and account strategy.
Marketing teams
Customer research, campaign history, brand guidelines, approved claims, performance data, and audience information.
Legal and professional services
Privileged or commercially sensitive documents, client history, matters, obligations, and internal analysis.
Healthcare operations
Protected or sensitive operational information requiring tightly controlled access and deployment.
Financial organizations
Customer, portfolio, risk, research, transaction, and strategy data subject to strict security controls.
Industrial and operational businesses
Legacy databases, proprietary workflows, supply-chain records, internal systems, and nonstandard operational software.
Where to begin
Start with one high-value workflow
You do not need to transform the entire company at once. Identify one workflow where sensitive data, repetitive work, cloud AI expense, or operational risk is blocking adoption. Zero Token Labs will design the deployment, connect the required systems, and demonstrate the workflow inside a controlled environment.
Frequently asked
What buyers ask before signing
Straight answers to the questions procurement, security, and finance always raise.
Contact
Schedule a Private AI Assessment
Describe the workflow you want to automate, the systems involved, and why the data or process cannot be handed to a conventional cloud AI product.