Enterprise AI
with a human accountability layer.
Human in the loop. By design, not by accident.
AERIS Lattice sits between your users and any LLM, running every response through an 11-step, 3-layer validation pipeline before delivery. When confidence meets the threshold, it delivers. When it doesn't, it escalates to a qualified human reviewer with full context.
Built for high-stakes environments
Raw LLM output is fundamentally unreliable
in high-stakes environments.
AI systems answer even when they shouldn't
Language models have no native uncertainty signal. Every query receives the same grammatical confidence, whether the answer is accurate or entirely fabricated.
At stake from AI errors in regulated industries
In healthcare, legal and financial services, a confidently wrong AI response is not a UX problem. It is a liability. One misdiagnosis or compliance failure can erase any efficiency gain.
Foundation models ship with a built-in refusal mechanism
No major LLM provider ships a controlled refusal state. When a model is uncertain, it guesses. In high-stakes domains, a confident wrong answer is far more dangerous than no answer at all.
Point of failure in most enterprise AI deployments
Most production pipelines route to a single model with no cross-validation layer. If that model hallucinates on a given query class, nothing stops it from reaching your user.
Every response runs through
a full arbitration system.
AERIS doesn't use a single AI model to decide if a response is safe. It uses three independent consensus layers with 11 validation steps. Any layer can stop a response. The final decision comes from a composite trust score, not a single model's guess.
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Cloud scale and local sovereignty.
Neither is optional.
AERIS Lattice deliberately separates cloud-scale model consensus from a local sovereign layer. The cloud layer maximises coverage. The Sovereign Layer provides offline, tamper-resistant arbitration with no network dependency. Both must agree before delivery.
- OpenAI GPT-4o mini
- Groq LLaMA 3.3
- Mistral Small
- Google Gemini 2.5 Flash
- Risk-tiered routing: 2, 3 or 4 models per query
- Inter-model agreement scoring with contradiction detection
- Each model responds independently, no cross-contamination
- Skeptic: challenges weak evidence chains (weight 1.0)
- Compliance Guardian: regulatory flag detection (weight 1.5)
- Adversarial Challenger: attack-surface scanning (weight 1.2)
- Precision Auditor: factual precision scoring (weight 1.0)
- Silent State Judge: final refusal authority (weight 2.0, veto)
- Runs exclusively on Llama 3.2 locally
- No network dependency. Air-gap capable.
Silent State is not a failure mode.
It is intelligent escalation.
AERIS Lattice was not designed to force 100% AI response coverage. It was designed to guarantee that every response delivered is safe. When that cannot be guaranteed, a qualified human is notified — not a hallucination served.
When AI confidence falls below threshold, the system protects your business. Not its response rate.
Most AI systems are built to always return an answer. AERIS Lattice is built around a harder objective: knowing when not to. Silent State activates only when the validation pipeline — confidence scoring, contradiction detection and multi-agent consensus — cannot reach sufficient agreement. At that point, the system does not guess. It escalates.
No. Silent State activates only when measured confidence falls below the domain risk threshold. The system delivers validated responses the vast majority of the time. Escalation is the exception, not the default.
All three layers evaluate the LLM draft. Confidence is scored, contradictions are checked and the Sovereign agents reach consensus. This happens in milliseconds.
The validated response reaches the user with full audit metadata attached. No human intervention needed. This is the outcome for the large majority of queries.
No response is delivered. The query, draft response and full validation report are logged and routed to a human reviewer, who can see exactly why the system held back.
The qualified reviewer sees the query, the AI draft and the specific failure reason. They approve, revise or override, with every decision logged to the full audit trail.
Triggered by measured insufficient confidence, not arbitrary rules. The system is calibrated per domain so low-risk queries are never over-refused and high-risk queries are never under-protected.
Every escalation includes the original query, the AI draft, per-layer confidence scores, the specific validation failure and a structured resolution interface. Reviewers never work without context.
META-ARBITRATION SCORE 0–100
Reliability defined.
Not assumed.
We don't chase a perfect score. We measure what actually matters: whether a response was safe to deliver, whether an unsafe one was caught, and whether the human reviewer got everything they needed. Production pilot data coming Q3 2026.
High-risk domains — clinical, legal, financial — carry more weight. A medical query needs a higher confidence score to pass than a general one. That asymmetry is intentional.
This is the one number we will not compromise on. Every other tradeoff — speed, escalation rate — is secondary to driving this to zero.
This is a precision metric, not a failure metric. Every escalation should be justified — and give the reviewer everything they need to act.
Pilot programme now open. We are seeking two pilot partners in healthcare or legal to validate these metrics in a live production environment. If your organisation is evaluating AI reliability infrastructure, get in touch at hello@aerislattice.com.
Built for domains where
errors carry real consequences.
Clinical decision support and medical Q&A
Hospitals and health-tech platforms need reliability guarantees that no general-purpose model can provide on its own. AERIS ensures clinical output either meets the confidence threshold or does not reach the practitioner.
Contract analysis and legal research
Legal AI that hallucinates case citations or misrepresents statutory language exposes firms to malpractice risk. AERIS validates factual claims and flags unsupported legal assertions before they reach counsel.
Investment research and compliance automation
In regulated financial contexts, model outputs may be treated as advice. AERIS provides an auditable compliance layer with full per-response logging, refusal records and confidence scores.
Reliability layer for LLM platforms and agents
Teams building on foundation models can wrap AERIS Lattice around any LLM endpoint. Drop-in architecture, open source, with a REST API that mirrors standard completion formats.
Open source. Auditable.
Production-ready.
Built by one person.
With a clear point of view.
Medellín, Colombia
AERIS Lattice was built from a straightforward frustration: AI systems are being deployed in high-stakes environments with no structured way to say "I don't know." The result is confident hallucinations in medical records, legal briefs and financial reports. Not because the models are bad, but because no layer exists to stop them when they should stop.
Most AI reliability strategies optimise for better answers. AERIS optimises for fewer wrong ones. Silent State reframes the problem entirely: instead of pushing accuracy toward 99.999%, we guarantee that nothing below a confidence threshold reaches a user and that every uncertain response reaches a qualified, accountable human instead.
This is not a chatbot. It is not another foundation model. It is a reliability architecture that treats human judgment as a first-class component of any AI system deployed where errors carry real consequences. AERIS Lattice is open source, independently built and actively seeking pilot partners in healthcare and legal to validate the architecture in a live production environment.
What decision-makers ask
before they deploy.
Most AI reliability strategies optimise for better answers. AERIS optimises for fewer wrong ones, routing everything uncertain to a qualified, accountable human instead.
Trust infrastructure
for enterprise AI.
AERIS Lattice is not an AI assistant. It is the reliability layer that makes AI deployable in environments where a wrong answer carries real consequences. Open source, fully auditable and built for human-in-the-loop integration from day one.