Lumen
OpenAI-compatible control plane for multi-provider routing, policy enforcement, cost guard, drift detection, and cryptographic audit evidence. Drop-in integration with existing applications.
Explore Lumen →Decision Intelligence from Research to Production
QMILabs builds research-grounded AI platforms that make model selection, cost control, quality accountability, and audit evidence visible and enforceable at scale. Two products: Mneme and Lumen.
"Production AI should be measured, not loose."
Research-grounded systems for memory, evaluation, governance, and evidence.
3Research-grounded products for governed AI
2Years of production deployment
4Teams: research, engineering, product, enterprise
∞Providers and local models supported
Platform
One control plane for memory, routing, evaluation, and proof.
Lumen makes every AI request governed, measured, and auditable. Mneme makes memory and context persistent and queryable. Dashboard gives operators and executives visibility over cost, quality, drift, and evidence.
Mneme
Enterprise BI and memory platform that makes business context persistent, queryable, and auditable. Brain-inspired architecture with semantic indexing, dynamic memory consolidation, and multi-domain entity resolution.
Explore Mneme →Customer Stories
From pilots to production governance.
Teams adopt Lumen and Mneme to move from observation (shadow mode) to enforcement (full control) over 2-4 weeks, discovering cost reduction, quality improvement, and compliance evidence in the process.
Research
Foundations in Bayesian inference and memory systems.
Every product is built on research-first principles: probabilistic reasoning, active learning, uncertainty quantification, and human-in-the-loop alignment. These are not bolt-ons; they are architecture.
Bayesian inference
Probabilistic models for uncertainty, active learning, and hypothesis testing in noisy environments.
Memory and consolidation
Brain-inspired architectures for episodic, semantic, and procedural memory with forgetting curves and reactivation.
Evaluation and verdict
Automated LLM-as-judge scoring, drift detection, and quality certification aligned to business outcomes.
Policy and compliance
Formal verification, cryptographic audit chains, and evidence-based decision logging for regulated operations.
About QMILabs
Research meets production engineering.
QMILabs was founded to bridge the gap between research insights and production systems. Most organizations treat AI as stateless request-response. We build systems where memory, evaluation, governance, and evidence are built in from the start.
Differentiation
Three things that set us apart.
Blog
Latest thinking on governed AI.
Drift detection at scale
How Lumen surfaces quality regressions within hours, not weeks, turning incidents into prevention.
Cost explosion and routing
Why model selection is a governance problem, not just a cost-optimization problem.
Memory as a product
Building persistent, queryable context layers that make sense of customer records and conversations.
Team
Research, engineering, and enterprise.
QMILabs teams span machine learning research, product engineering, design, and enterprise partnerships. Everyone moves between first-principles thinking and shippable systems.
Machine learning research
Bayesian inference, reasoning, memory systems, and evaluation methods for robust intelligence.
Product engineering
Production platforms, dashboards, APIs, deployment workflows, and audit-ready user experiences.
Enterprise partners
Collaborative pilots with organisations that need AI to operate within real governance constraints.
Careers
Build the next generation of governed AI systems.
QMILabs is interested in people who can move comfortably between first-principles thinking and shippable systems: ML researchers, product engineers, design engineers, data platform specialists, and enterprise AI operators.
Research
Bayesian ML, uncertainty, evals, memory, reasoning, and human-in-the-loop alignment.
Engineering
Provider-agnostic APIs, dashboards, observability, orchestration, and secure deployment.
Product
Enterprise workflows, crisp UX, demos, documentation, and customer learning loops.
Why QMILabs
The difference is accumulated insight.
Production AI should not be a loose set of prompts and dashboards. It should be a measured system where memory, evaluation, policy, and evidence improve future decisions.
Without QMILabs
- Definitions disappear between sessions.
- LLM costs rise without purpose-level attribution.
- Quality and drift are inspected after users complain.
- Model choices depend on generic benchmarks.
With QMILabs
- Memory carries business context forward.
- Routing and deterministic paths control spend.
- Evaluation signals govern production behaviour.
- Every decision is connected to evidence, policy, and auditability.
Contact
Make AI accountable.
Talk to QMILabs about Mneme, Lumen, dashboards, or research partnerships for governed production intelligence.