# Binary AI Labs > Binary AI Labs is a superintelligence company serving the GCC, the UK, and India. It builds AI systems that read, decide, and act on their own — agentic AI, real-time ambient agents, long-running agents, data engineering, robotics, and edge computing — with people kept in control. Clients include a major GCC holding group, a UK compliance-technology firm, a listed Indian lender, and a US AI-security company. Every engagement starts with a two-week Discovery Sprint. It has offices in London, Dubai, Abu Dhabi, and Delhi. Every page below is also available as plain markdown by appending `.md` to its path — for example https://binaryailabs.com/work/secure-enterprise-ai.md. ## Answers Plain-language answers about superintelligence, AI agents, and how we work. Start here for questions of fact. - [What is an agent eval?](https://binaryailabs.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://binaryailabs.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://binaryailabs.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://binaryailabs.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://binaryailabs.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://binaryailabs.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 keep an AI system working well after launch?](https://binaryailabs.com/answers/stop-llm-agent-drifting-in-production): AI can quietly get worse over time. You can’t stop that from happening — you catch it early by testing it on a schedule, and fix it before anyone notices. - [What causes model drift in LLM applications?](https://binaryailabs.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://binaryailabs.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://binaryailabs.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://binaryailabs.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://binaryailabs.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://binaryailabs.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://binaryailabs.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://binaryailabs.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://binaryailabs.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://binaryailabs.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://binaryailabs.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://binaryailabs.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://binaryailabs.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://binaryailabs.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://binaryailabs.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://binaryailabs.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://binaryailabs.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://binaryailabs.com/answers/what-is-a-discovery-sprint): A two-week, fixed-scope, fixed-fee first engagement. You leave with an opportunity map, a working prototype, a scorecard, and a build plan with a fixed quote. - [What does Binary AI Labs do?](https://binaryailabs.com/answers/what-does-binary-ai-labs-do): Binary AI Labs is a superintelligence company. It builds AI that reads, decides, and acts on its own — with people kept in control. - [How does an engagement with Binary AI Labs work?](https://binaryailabs.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://binaryailabs.com/answers/what-industries-do-you-work-in): Binary AI Labs works with holding groups, compliance teams, lenders, AI-security companies, and device makers on AI that makes real decisions. - [How much does working with Binary AI Labs cost?](https://binaryailabs.com/answers/how-much-does-an-engagement-cost): Every engagement starts with a two-week Discovery Sprint at a fixed fee, quoted after a free first conversation. The full build is then quoted at a fixed price too. ## Work Production engagements, each with problem, architecture, and result. - [One view of a 1,300-company group](https://binaryailabs.com/work/gcc-group-intelligence): For one of the GCC’s largest listed holding groups — 1,300+ companies in 40+ countries — we are building one trusted picture of the whole group, kept current by AI agents. - [A compliance co-analyst for regulated firms](https://binaryailabs.com/work/compliance-co-analyst): For a London compliance-technology firm, we are building an AI co-analyst that investigates problems the way a firm’s own team would — and leaves a complete audit trail behind it. - [Faster loan decisions across a national branch network](https://binaryailabs.com/work/lending-intelligence): For a listed Indian lender with ₹59,000 crore+ under management and 245 branches, we are building AI agents that read, check, and prepare every loan file for a credit officer. - [AI that only knows what you’re allowed to know](https://binaryailabs.com/work/secure-enterprise-ai): For a US AI-security company recognised by a leading industry analyst, we built the guard that checks every question against who is asking — before the AI ever sees the data. - [Brilliant Labs Halo — AI glasses that think for themselves](https://binaryailabs.com/work/brilliant-labs): Open-source AI glasses where the AI runs inside the frames — no phone or cloud needed for everyday use — with all-day battery. - [Starise — honest reviews, asked at the right moment](https://binaryailabs.com/work/starise): A system that spots happy customers the moment a sale closes and invites them to leave a review — on the sites buyers actually read, with no incentives. ## Solutions Six practices, offered across the GCC, the UK, and India. - [Agentic AI](https://binaryailabs.com/solutions/services/agents): AI that does the work, not just the talking. We build digital workers that handle whole tasks from start to finish — reading a request, checking your systems, making a decision, and asking a person whenever it matters. They work around the clock and follow your rules every time. - [Real-time ambient agents](https://binaryailabs.com/solutions/services/ambient): Always watching. Always ready. Ambient agents run quietly in the background of your operations — watching data, messages, and systems as things happen — and act the moment something changes. Nobody has to ask them; they notice. They can also listen and speak, answering calls and taking action in real time. - [Long-running agents](https://binaryailabs.com/solutions/services/long-running): Works for weeks, not seconds. Most AI forgets everything the moment a conversation ends. Long-running agents take on projects that last days or months — keeping track of progress, remembering every step, and picking up exactly where they left off. - [Data engineering](https://binaryailabs.com/solutions/services/data): All your data, finally on speaking terms. AI is only as good as the information it can reach. We connect your scattered systems — finance, operations, customers — into one clean, trusted source, kept up to date as things happen and protected so people only see what they are allowed to. - [Robotics](https://binaryailabs.com/solutions/services/robotics): Intelligence with a body. We give drones and robots the ability to see, decide, and move safely on their own — inspecting sites, moving goods, and working in places that are dangerous or remote for people. - [Edge computing](https://binaryailabs.com/solutions/services/edge): AI that works without the cloud. Edge computing means the AI runs on the device itself — a camera, a phone, a vehicle, a machine on the factory floor. Decisions happen instantly, private data never leaves the building, and it keeps working even when the internet does not. ## Research - [Research index](https://binaryailabs.com/research): Papers, benchmarks, and open-source work. - [Papers](https://binaryailabs.com/research/papers): What we are learning, written up openly. - [Benchmarks](https://binaryailabs.com/research/benchmarks): Measured results behind the headline numbers. - [Open source](https://binaryailabs.com/research/oss): Models and tools anyone can use. ## Blog - [How to Prevent LLM Model Drift in Production Environments](https://binaryailabs.com/blog/how-to-prevent-llm-model-drift-in-production): How CTOs and platform teams can detect changing LLM behaviour and limit its impact with evaluations, observability, and controlled releases. - [Enterprise AI Consulting: Scaling Agentic Systems Safely](https://binaryailabs.com/blog/enterprise-ai-consulting-scaling-agentic-systems-safely): An enterprise AI consulting roadmap from a bounded workflow to an observable agentic system, with practical delivery guidance. - [Implementing Zero-Trust Architecture for Corporate LLM Governance](https://binaryailabs.com/blog/zero-trust-architecture-corporate-llm-governance): Apply zero-trust principles to enterprise LLM access, retrieval, and tool execution, with a control map for security architects. ## Optional - [How we work](https://binaryailabs.com/people): How an engagement runs, stage by stage. - [Contact](https://binaryailabs.com/contact): Start a discovery sprint. Direct: growth@binaryailabs.com.