Your models are smart.
Your infrastructure isn't.
AiMe is a governed AI operating layer for enterprise workflows — not a chatbot wrapper. A deterministic intent engine, a tool-first execution pipeline, and an audit-ready runtime that solves hallucination and compliance risk at the architectural level.
Self-funded · founder-led · built from first principles
Why enterprise AI deployments fail.
Most AI integrations drop a foundation model into a workflow and hope for the best. Three structural flaws make that approach untenable at scale.
Hallucinations in production
Models invent facts, misuse tools, and generate confidently wrong outputs. There is no architectural layer stopping them — just prompt instructions they may or may not follow.
Compliance riskNo audit trail, no routing logic
When something goes wrong, there is no log of why the model made a decision, which tool it called, or what data it was given. Debugging is forensics. Compliance is impossible.
Audit failureModel lock-in & fragile governance
Identity, memory, and workflow logic are entangled with the model. Switch providers, change models, or update APIs — the whole deployment breaks. Governance is bolted-on, not structural.
Infrastructure fragilityHow AiMe solves each one.
Every architectural decision traces back to a single question: if the model gets it wrong, does the infrastructure catch it? AiMe's answer is yes — by design.
Intent arbitration before generation
Routing decisions are made deterministically in Python before the LLM is ever invoked. A 4-resolver consensus engine (semantic, SetFit, spaCy, lexical) classifies every request and assigns it to the right cognitive engine for that task type.
The model never decides what to do. The infrastructure decides. The model is told.
PrefrontalCortex → resolve_lane() → cognitive engine assigned → REGI injectedTool-first fact grounding
Data is fetched by AiMe in Python and handed to the model to read — not hallucinated. The Action Dispatcher runs before the LLM call, retrieves verified results from live systems, and injects them into the prompt as structured facts.
The model sees real data. It cannot invent what it already has.
Action Dispatcher → web search / email / calendar → <<Execution ledger enforcement
Every action, state change, and tool invocation is logged in the Evidence Ledger — an append-only SQLite record that never rewrites history. Every turn is traceable: what was said, what was retrieved, what was executed, and when.
Compliance isn't a feature. It's the default architecture.
Evidence Ledger → append-only · immutable · session-persistent · queryableModel-agnostic swapping
AiMe's REGI (Runtime Execution & Governance Injection) governs model behavior per-turn through a structured specification — not through the model's internal state. Cloud and local models can be interchanged without breaking workflow logic.
Azure GPT today. Anthropic Claude tomorrow. LLaMA offline. Same runtime. Same experience.
routing.json → resolve_lane() → 8 provider adapters · swappable at runtimeSystem Portrait & governance mode
In Work Persona mode, AiMe maintains a System Portrait — a structured runtime model of the governed system, tracking governance commitments, open incident stack, topology map, operational patterns, and a role-keyed Human Field.
The model responds to the authority level of the active role, not the identity of the individual. Open incidents persist until resolved. Governance is architecture, not annotation.
System Portrait → role-keyed authority · incident stack · commitment trackingBuilt for workflows where errors have consequences.
AiMe's architecture is most valuable where hallucination is unacceptable, audit trails are required, and the same system must serve multiple principals with different authority levels.
Legal Tech
Document intake, case summarization, and compliance-bound workflows where every inference must be traceable and auditable. AiMe's execution ledger makes every retrieval and generation step reviewable.
Financial Services
Reporting, audit trails, and risk-bound reasoning where the model must cite its sources, operate within bounded authority levels, and never generate numbers it wasn't given.
Healthcare Administration
Intake triage, scheduling, and billing processing where tool-first grounding is non-negotiable. AiMe retrieves patient data, schedule state, and billing context before the model says a word.
Internal Enterprise Copilot
Email management, task routing, and ticket triage where the system must know who is asking, what authority they hold, and maintain memory of ongoing threads — without being re-briefed each session.
Three layers that run independently of the model.
Governance in AiMe is not a post-processing filter. It is a parallel pipeline that monitors, gates, and enforces constraints before, during, and after every turn — regardless of what the model generates.
Runtime Execution & Governance Injection
A structured per-turn specification injected into every inference cycle. Defines behavioral policy, operational parameters, tool usage rules, and live context — giving the model a "Reggie" every inference cycle.
- Output constraints and tool usage rules
- Task framing and decision boundaries
- Pre-fetched external state (not hallucinated)
- Portrait context injected silently
Governed System Model
A Living Portrait for the governed system — not the user. Tracks governance commitments, open incident stack, topology map, and a role-keyed Human Field mapping roles to authority levels.
- Role-keyed authority registry
- Active incident stack (open until resolved)
- Operational commitments as invariants
- Dual Persona: home + work simultaneously
Parallel Monitoring & Gating
A full governance runtime running alongside every turn: anomaly detection, drift monitoring, stability checks, reinforcement gating, and a Guardian watchdog that enforces system integrity continuously.
- Anomaly Detector — variance, drift, contradiction
- Drift Monitor — intent cluster boundary enforcement
- Reinforcement Gate — pre-commit truth validation
- Guardian — system integrity watchdog (60s pulse)
The system enterprises trust — now personal.
If AiMe succeeds in industry, it becomes a trusted enterprise runtime — a brand associated with reliability, auditability, and deterministic behavior. When it enters the consumer space, it arrives with a different credential.
Not "another AI assistant." The system enterprises trust — now personal.
A user who has worked with AiMe for six months carries a portrait that no new assistant can replicate on day one. The system knows them — not because it was told, but because it has been there.
This is what makes the enterprise path strategically irreversible: governance infrastructure built for industry-grade reliability produces the same compounding continuity when applied to individual relationships.
Same architecture. Same invariants. The stakes just change.
Let's talk about your workflow.
AiMe for Industry is in active development. If you're evaluating governed AI infrastructure for legal, financial, healthcare, or enterprise copilot use cases — reach out.
Request Early Access
What to expect
Direct conversation with the founder. No sales process, no demo deck — a technical conversation about your workflow and whether AiMe's architecture fits.
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Technical alignment callWalk through your workflow, identify where the governance layer adds value, and where it doesn't.
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Architecture reviewDeep dive into how REGI, the System Portrait, and the execution ledger map to your compliance requirements.
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Early partnershipShape the enterprise feature set before general availability. Joint development possible for the right fit.
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No commitment requiredThe goal is a real conversation. If it's not a fit, you'll know quickly.