# Binary AI Labs

> Binary AI Labs is an AI, quantum, and robotics consulting practice that takes agentic systems from prototype into production. It works with engineering and product teams on architecture, evaluation, and deployment for multi-agent systems, quantum machine learning, and on-device inference. Engagements start with a discovery sprint and are delivered by the engineers who do the work. It was founded in 2025 and operates from Dubai, London, and New Delhi.

Every page below is also available as plain markdown by appending `.md` to its path — for example https://www.binarylabz.com/work/daxa.md.

## Answers

Definitional answers on shipping agentic and quantum systems. Start here for questions of fact.

- [What is an agent eval?](https://www.binarylabz.com/answers/what-is-an-agent-eval): An agent eval is a repeatable test that scores an agent's behaviour on a fixed set of scenarios, run in CI like any other test suite.
- [How many eval cases do you need for an LLM agent?](https://www.binarylabz.com/answers/how-many-eval-cases-do-you-need): Enough that a one-point score move is larger than the noise — usually 200–500 scenarios per agent role, not a handful of spot checks.
- [Is an LLM judge good enough, or do you need human evaluation?](https://www.binarylabz.com/answers/llm-judge-vs-human-eval): An LLM judge is good enough for continuous scoring once it is calibrated against human labels — the humans set the ruler, the model reads it.
- [How do you evaluate a multi-agent system?](https://www.binarylabz.com/answers/how-to-eval-multi-agent-systems): Score each agent against its own role contract, then score the system end to end — most multi-agent failures are handoff failures, not reasoning failures.
- [What metrics should you track for a RAG system?](https://www.binarylabz.com/answers/what-metrics-for-rag): Split retrieval from generation: recall and precision on the retriever, groundedness and answer quality on the generator. A single end-to-end score hides which half broke.
- [When should you start writing evals for an AI feature?](https://www.binarylabz.com/answers/when-to-start-evals): Before the second prompt change. The moment you cannot tell whether an edit helped, you have already needed evals for a while.
- [How do you stop an LLM agent from drifting in production?](https://www.binarylabz.com/answers/stop-llm-agent-drifting-in-production): You cannot prevent drift — you detect it. Pin model versions, score a fixed canary set on every deploy and daily, and alert on the delta rather than the absolute score.
- [What causes model drift in LLM applications?](https://www.binarylabz.com/answers/what-causes-model-drift): Three sources: the provider changing the model, your input distribution moving, and accumulated prompt edits. They look identical in the metrics and need different fixes.
- [How often should you re-evaluate a model in production?](https://www.binarylabz.com/answers/how-often-re-evaluate-production-model): A fast subset on every deploy, the full suite nightly, and a human-reviewed sample weekly. Anything slower and you learn about regressions from users.
- [How do you detect drift when you have no labelled data?](https://www.binarylabz.com/answers/detect-drift-without-labels): Monitor the inputs and the system's own behaviour: embedding distributions, refusal and escalation rates, tool-call mix, and answer length. All are label-free and move before quality does.
- [Should you pin model versions or take automatic upgrades?](https://www.binarylabz.com/answers/pin-model-versions-or-auto-upgrade): Pin, then upgrade deliberately behind an eval run. Automatic upgrades trade a small maintenance saving for an unbounded, unobservable quality risk.
- [When should you run inference on-device instead of in the cloud?](https://www.binarylabz.com/answers/when-to-run-inference-on-device): When latency must be predictable, connectivity is unreliable, or the data should not leave the device. Otherwise the cloud is cheaper and easier to update.
- [How small does a model need to be to run on an NPU?](https://www.binarylabz.com/answers/how-small-does-a-model-need-to-be-for-npu): Small enough to fit the NPU's on-chip memory after quantisation — on microcontroller-class parts that means single-digit megabytes, which is an architecture constraint, not a compression one.
- [How do you split work between an on-device model and a cloud agent?](https://www.binarylabz.com/answers/hybrid-edge-cloud-architecture): On-device handles the perception loop and anything latency-bound; the cloud handles long-horizon reasoning and persistent memory. The split follows the latency budget.
- [What latency is achievable for on-device computer vision?](https://www.binarylabz.com/answers/edge-vision-latency-budget): Sub-10 ms per frame for vision-transformer inference on current mobile and embedded accelerators, which is enough for a real-time interaction loop.
- [Is quantum machine learning useful for real problems yet?](https://www.binarylabz.com/answers/is-quantum-machine-learning-useful-yet): For a narrow class of simulation and optimisation problems, yes — usually via quantum-inspired and physics-informed methods on classical hardware rather than on a quantum computer.
- [What is a physics-informed neural network (PINN)?](https://www.binarylabz.com/answers/what-is-a-physics-informed-neural-network): A neural network trained with the governing physical equations built into its loss function, so it needs far less data than a purely data-driven surrogate.
- [Do quantum ML methods actually train faster than classical ones?](https://www.binarylabz.com/answers/quantum-ml-vs-classical-training-time): On the problems where the structure fits, quantum-inspired methods have cut training from weeks to hours — roughly 97× — but the speedup is problem-specific, not general.
- [Do you need access to a quantum computer to use these methods?](https://www.binarylabz.com/answers/do-you-need-a-quantum-computer): No. The methods that pay off today run on classical hardware — quantum hardware access matters for research, not for the production systems these techniques currently serve.
- [How do you enforce access control in a RAG pipeline?](https://www.binarylabz.com/answers/enforce-access-control-in-rag): Filter at retrieval time against the requesting user's identity, before chunks reach the model. Post-hoc filtering of the model's output is not access control.
- [What is zero-trust AI governance?](https://www.binarylabz.com/answers/what-is-zero-trust-ai-governance): Applying zero-trust principles to AI data flow: every retrieval is authorised against the caller's identity at request time, with no implicit trust granted by being inside the pipeline.
- [What does it take to make an AI system auditable?](https://www.binarylabz.com/answers/make-an-ai-system-auditable): Record the inputs, the retrieved context, the tool calls, the model version, and the output for every request — and be able to replay any one of them.
- [How do you decide what an agent is allowed to do autonomously?](https://www.binarylabz.com/answers/who-is-liable-when-an-agent-acts): Draw the line at reversibility and blast radius: reversible, bounded actions run autonomously; irreversible or outward-facing ones require confirmation.
- [What should an engineering team do now to prepare for AI regulation?](https://www.binarylabz.com/answers/eu-ai-act-what-to-do-now): Build the inventory, the traces, and the evals. Every current regime asks what the system does, what data it touches, and how you know it works — those three artefacts answer all of it.
- [What is a discovery sprint?](https://www.binarylabz.com/answers/what-is-a-discovery-sprint): A short, fixed-scope engagement that turns an ambiguous AI problem into an architecture, an eval plan, and a costed build path you can act on or take elsewhere.
- [What does Binary AI Labs do?](https://www.binarylabz.com/answers/what-does-binary-ai-labs-do): An AI, quantum, and robotics consulting practice that takes agentic systems from prototype into production — architecture, evaluation, and deployment.
- [How does an engagement with Binary AI Labs work?](https://www.binarylabz.com/answers/how-do-engagements-work): Discovery sprint, then build alongside your team, then hand off. Every stage is delivered by the engineers who do the work, not by an account layer.
- [Which industries does Binary AI Labs work in?](https://www.binarylabz.com/answers/what-industries-do-you-work-in): Event technology, AI security, edge and wearable hardware, and financial research — chosen by problem shape rather than by sector.
- [How much does an AI consulting engagement cost?](https://www.binarylabz.com/answers/how-much-does-an-engagement-cost): Priced per engagement rather than per seat or per hour, scoped from the discovery sprint. There is no public rate card because the variance between problems is too wide to be useful.

## Work

Production engagements, each with problem, architecture, and result.

- [Aletheia](https://www.binarylabz.com/work/aletheia): Multi-agent platform for live-event coordination — discovery, scheduling, on-site logistics in real time.
- [Daxa — Zero-Trust AI Governance](https://www.binarylabz.com/work/daxa): TwinGuard architecture enforcing context-aware access control on enterprise data before it reaches an LLM.
- [Brilliant Labs Halo — On-Device AI Wearable](https://www.binarylabz.com/work/brilliant-labs): Open-source AI glasses running on-device NPU inference, paired with a cloud agent with persistent memory.
- [Quasar Markets — Institutional AI Research](https://www.binarylabz.com/work/quasar): AI-powered research workbench fusing quantitative market signals with LLM-driven analysis for the buy side.
- [Starise — Authentic Review Engine](https://www.binarylabz.com/work/starise): Event-driven capture loop that routes satisfied customers into structured review flows across platforms — no incentives, no fake amplification.

## Solutions

Services you can hire and products you can run.

- [AGENTS & RAG](https://www.binarylabz.com/solutions/services/agents): Multi-agent swarms with adaptive retrieval. Hybrid semantic RAG pipelines that scale to millions of daily queries while reducing hallucinations 50–70% across enterprise knowledge bases.
- [QUANTUM ML & PINNS](https://www.binarylabz.com/solutions/services/quantum): QNNs, QSVMs, physics-informed networks. Quantum-classical hybrids and physics-informed neural networks that solve PDE-heavy simulation problems with a fraction of the labelled data.
- [EDGE AI & ROBOTICS](https://www.binarylabz.com/solutions/services/edge): BVLOS drone autonomy, LiDAR swarms, 120 Hz on-device inference. Autonomy stacks for drones and ground robots — perception, planning, and control compiled for embedded NPUs and shipped past regulatory review.
- [COMPUTER VISION](https://www.binarylabz.com/solutions/services/vision): Vision Transformers on the production line. Real-time defect detection, surveillance, and clinical imaging built on ViT and segmentation backbones, optimised for sub-10 ms TensorRT deployment.
- [VOICE AGENTS (VOXEDGE)](https://www.binarylabz.com/solutions/services/voice): Sub-300 ms latency, 50+ languages, on-device or cloud. Real-time voice agents wired through LiveKit and Cartesia, with streaming ASR, TTS, and tool-use stitched into a single duplex turn.
- [DATA ENGINEERING](https://www.binarylabz.com/solutions/services/data): Petabyte lakehouses, real-time CDC, HIPAA/SOC 2 governance. Bronze–silver–gold medallion lakehouses with streaming CDC, schema evolution, lineage, and policy-aware access — designed for AI consumption from day one.
- [AUTOMATED QA](https://www.binarylabz.com/solutions/services/qa): End-to-end test plans, wired into CI. Purpose-built QA frameworks covering unit, integration, E2E, and load testing — from an audit of what exists to a pipeline that blocks the regression before it ships.
- [AI RESCUE](https://www.binarylabz.com/solutions/services/rescue): Stalled builds, taken over and finished. Embedded engineers take over a stuck build, refactor the broken pipeline, and hand back a working system — from codebase audit to production handoff.
- [Aletheia](https://www.binarylabz.com/solutions/products/aletheia): An agentic platform for events — discovery, scheduling, and logistics handled by autonomous agents coordinating in real time.
- [AssureAI](https://www.binarylabz.com/solutions/products/assureai): Automated QA harness for LLM applications. Synthetic conversations, regression evals, drift detection wired into CI.
- [BinaryOS](https://www.binarylabz.com/solutions/products/binaryos): Production runtime for agentic systems — orchestration, observability, and governance for multi-agent deployments.
- [RLForge](https://www.binarylabz.com/solutions/products/rlforge): RLVR / GRPO post-training infrastructure as a service. Verifiable rewards, ~10× cheaper than RLHF.
- [DocScribe](https://www.binarylabz.com/solutions/products/docscribe): AI doctor–patient interaction layer — ambient capture, structured clinical notes, EHR-ready outputs.
- [VoxEdge](https://www.binarylabz.com/solutions/products/voxedge): Real-time voice agents — sub-300 ms duplex, 50+ languages, on-device or cloud.
- [TurboQuant](https://www.binarylabz.com/solutions/products/turboquant): Two-stage extreme quantisation algorithm — 3-bit zero-loss KV-cache compression with no training.
- [QuantumKit](https://www.binarylabz.com/solutions/products/quantumkit): Hybrid quantum-classical ML toolkit — QNNs, QSVMs, and physics-informed neural networks through a Python SDK that runs on real quantum hardware and on simulators.
- [VisionEdge](https://www.binarylabz.com/solutions/products/visionedge): On-device computer vision at sub-10 ms latency. Optimises and deploys Vision Transformer models to constrained hardware — drones, robots, cameras.

## Research

- [Research index](https://www.binarylabz.com/research): Papers, open-source models, and benchmarks.
- [TurboQuant](https://www.binarylabz.com/research/turboquant): Compression research, ICLR 2026, with Google Research.
- [Benchmarks](https://www.binarylabz.com/research/benchmarks): Measured results behind the headline numbers.

## Optional

- [How we work](https://www.binarylabz.com/people): How an engagement runs, stage by stage.
- [Contact](https://www.binarylabz.com/contact): Start a discovery sprint. Direct: hello@binarylabz.com.