SHARED AI ARCHITECTURE · OVERLIFT

OverLift

Build AI applications with evidence and clear rules.

OverLift is reusable architecture built around the OverLift method. It connects evidence, interpretation, and application rules; each product applies those responsibilities to its own problem, from shipment recovery to document review and game agents.

Explore the architectureOverLift.ai See the evidence
OverLift interactive browser simulator with a globe, guided proof, Evidence Fabric, authority boundary, and Decision Receipt
Interactive simulator The Logistics reference application makes the OverLift architecture visible through a synthetic shipment, its supporting records, approval controls, and decision receipt.

Recorded project scope

Implemented

Browser-based guided and open simulator

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  • Evidence admission, typed state, governance, Decision Receipts, rewind, and replay
  • C17/WebAssembly Truth Core separated from agent proposals
  • Shared R10M intelligence fabric for typed plans, reviewed retrieval, graph traversal, Q-Lens comparison, uncertainty, abstention, receipts, and exact replay

Demonstrated

Synthetic logistics incident with local RAG and multiple recovery paths

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  • Human approval boundary and post-action verification
  • Deterministic replay from the same admitted state
  • 36 reviewed governed-intelligence cases spanning explanation, learning paths, product proof, misconception repair, authority boundaries, clarification, and abstention

Planned

Trusted gateway integration with real enterprise systems

More in this category (2)
  • Tenant-specific connectors, policies, and identity controls
  • Persistent evaluation dashboards for production deployments

Research

Quantum-inspired candidate exploration and uncertainty math

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  • Adaptive execution from T0 through advanced verified tiers
  • Causal and semantic proof across additional domains
MARKETING

Who it helps and why.

Agentic AI can produce a polished answer even when its evidence is stale, its interpretation is uncertain, or the action is outside its permission. In important software, confidence is not the same thing as truth, and a model should not be able to promote its own suggestion into the official record.

What makes it useful: The architecture makes the handoff from language to application behavior visible: relevant records, a typed request, validation, an answer, and a decision record. Models can propose interpretations while application code controls permissions and accepted state.

BUSINESS / EXECUTIVE

Business value.

The portfolio makes this separation inspectable through its learning and Logistics examples. It shows how a visitor can follow an answer back to supporting records and understand which actions remain outside the question interface.

Explore the business outcomes
  • Visible evidence and alternatives
  • Separate proposal and action controls
  • Inspectable decision records
ARCHITECTURE

How the system fits together.

The AI helpers may search, compare, explain, and propose. They cannot rewrite admitted facts, raise their own authority, or directly commit a consequential change. The trusted kernel checks the plan before anything becomes official.

A representative workflow

  1. 01

    Follow the architecture from a question to relevant records and a typed request.

  2. 02

    Inspect how application rules validate the proposed result and keep permissions explicit.

  3. 03

    Open the Logistics example to see these boundaries applied to a synthetic shipment-recovery decision.

Explore the architecture details

I designed and built the shared request-to-result architecture: local retrieval, typed interpretation, evidence and permission checks, a C17/WebAssembly authority core, and inspectable decision records. The Logistics reference application makes these boundaries visible in a synthetic operational workflow.

ENGINEERING

Engineering choices.

I designed the boundary between language interpretation and application authority, then implemented it in the browser. Retrieval finds relevant records, an interpreter proposes a typed request, and application code checks evidence and permissions before returning a result.

Explore the technology and engineering details
Technology stackBrowser-native agents, governed intelligence fabric, local RAG, CATS/Tier-S retrieval, content graphs, Q-Lens, C17/WebAssembly Truth Core, deterministic replay, Decision Receipts
  • Guided and open simulator modes with a shared typed-plan intelligence fabric
  • Reviewed concepts, content graphs, Q-Lens paths, uncertainty, authority, receipts, rewind, and replay
  • A browser-based WebAssembly authority core that remains separate from the agents
OVERLIFT METHOD

How this product applies the OverLift method.

OverLift applies the method end to end: preserve the question, admit the right evidence, translate it into typed state, compare candidate paths, enforce policy and approval, let exact software commit the result, and seal a receipt. The AI can be inventive without becoming the owner of truth.

Explore the shared OverLift architecture

The method is reusable. Each product supplies its own facts, rules, execution, and validation. These tiers describe the shared architecture; the project scope above records which capabilities are implemented here.

Why determinism matters here: OverLift supports consequential decisions. If the same admitted facts and permissions can produce an untraceable official result, the system cannot be governed, audited, or trusted.

  1. T0
    Exact baseline

    Define the authoritative result before adding acceleration, semantics, or agents.

  2. T1
    Measured evidence

    Instrument state, latency, quality, and failure so improvement is observable.

  3. T2
    Bounded ownership

    Move expensive or high-frequency work behind clear worker and memory boundaries.

  4. T3
    Deterministic transport

    Version messages, snapshots, identities, deadlines, and replay inputs.

  5. T4
    Semantic assistance

    Let local retrieval and models propose meaning only inside admitted evidence.

  6. T5
    Candidate paths

    Compare several allowed next actions with explicit risk, cost, fit, and uncertainty.

  7. T6
    Shadow verification

    Challenge the production route without giving the challenger authority.

  8. T7
    Governed production

    Promote only measured, replayable paths with fallback and operational receipts.

  9. Tier-S
    Experimental challenger

    Run advanced evaluators in shadow, compare them with the control, and graduate only repeatable gains.

CATS

Context-Aware Tier Selection

CATS chooses a qualified execution tier for the evidence, device, latency, permissions, and failure conditions of the task.

SEMANTIC BRIDGE

Meaning becomes typed product state

The Semantic Bridge maps a goal to explicit entities, evidence, uncertainty, tools, and permissions. Missing or contradictory information remains visible.

DETERMINISM

Validate the action and retain the result

Models may retrieve, compare, or propose. Exact software checks facts, permissions, state changes, and execution, then records what happened.

OverLift securityAgentic AI Glossary

Read the OverLift MethodOverLift.ai See other products
PROOF

Evidence and limits.

The learning interface and Logistics missions are the portfolio’s working examples of this architecture. Visitors can inspect reviewed concepts, synthetic recovery routes, supporting records, and decision receipts. Question answering is read-only; independent language qualification and real enterprise integrations remain separate work.

Inspect a Logistics mission

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