Asymmetri
Bring us a hard problem

Prescribe Life, Health, Care and Wellbeing

Coaching intelligence that works between sessions.

An AI performance-intelligence platform for leadership coaches. Behavioural science, biometric data and an AI co-pilot flag burnout risk between sessions.

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Following Ami to the

Innovation and R&D

What is next, engineered into what ships.

Applied research in AI and machine learning systems and in mixed reality, plus the products we build and run ourselves. We prototype the frontier, prove it, then transfer it into production.

Research that never ships is a hobby. We prototype the frontier, prove it with evidence and transfer it into production.

Two tracks. One test: does it hold up?

We research where the next decade of products will be decided: in the systems behind AI, and in the spaces where people meet it. Each track runs on open questions, not roadmaps.

02Track A

AI and ML systems

  • Agent runtimes, memory, evaluation and inference economics: the open questions that decide whether AI holds up in production.
  • Agent runtimesHow do long-running agents stay reliable, resumable and observable when one task spans hours and dozens of tools?
  • MemoryWhat should an agent remember, forget and cite, and how does that memory stay correct as the world changes?
  • EvaluationHow do you prove an agent is good enough before it touches the operation, and keep proving it after?
  • Inference economicsWhat does each decision cost, and how far can routing, caching and smaller models cut it without losing quality?

03Track B

Mixed Reality

  • Spatial interfaces that carry intelligence into the physical world, where people work, train and move.
  • Spatial interfacesWhere should information live in a physical space so it helps without getting in the way?
  • Grounded intelligenceHow does an assistant understand what the wearer is looking at, and act on it?
  • Training and guidanceCan spatial guidance shorten the time it takes someone to learn a physical task?
  • Hands-free interactionWhich inputs, voice, gaze or gesture, hold up in noisy, gloved and moving environments?

Most ideas should die cheaply.

Every experiment passes five stations, each with criteria agreed before it starts. Failing early is the point: it costs days, not budgets, and the survivors arrive in production with evidence.

  1. Hypothesis

    A falsifiable claim about value.

  2. Prototype

    The cheapest thing that can be wrong.

  3. Benchmark

    Measured against today's way.

  4. Pilot

    Real people, real workflow, guarded.

  5. Transfer

    Hardened and shipped to production.

05Products from the lab

The best proof of a method is a product that runs on it.

  • Agentic project management

    FinishLine

    Every piece of work, carried to the finish line. Our agentic project management operating spine. People and their agents take work from intent to delivery in one place, and we run our own company on it.

    Read FinishLine

  • Agentic customer conversations

    Elk

    One AI agent for all your customer conversations. Elk learns a business from its website, docs and tools, then answers, books and qualifies across every channel, and hands over to a person when it should. Any model, retrained automatically as the content changes.

    Visit elkagent.com

Your product, incubated in our lab.

FinishLine and Elk are not one-offs. We co-develop new products with partners who bring a market, a dataset or a hard operating problem, and we bring the frontier team that can build it and run it.

  1. Frame it together

    We size the opportunity with you and agree what would make it worth building.

  2. Prove it cheaply

    Prototypes and pilots with real users, judged against criteria set up front.

  3. Build and run it

    We design, engineer and operate the product with you, from launch onwards.

Six disciplines. One lab.

What we actually do in Innovation and R&D, and what you get at the end of each piece of work.

  • Applied AI and ML researchResearch aimed at production questions: agent runtimes, memory, retrieval, fine-tuning and model choice, tested on your data rather than on leaderboards.
  • Mixed reality and spatial UISpatial interfaces for headsets, mobile AR and the web that put guidance, data and agents into the physical spaces where work happens.
  • Rapid prototypingWorking software in front of real users quickly, built to answer one question cheaply and honestly before anyone commits a budget.
  • Benchmarks and evaluationEvaluation suites that measure quality, safety, latency and cost per task against a baseline, so decisions rest on numbers, not demos.
  • New product incubationProducts we build and run ourselves and with partners, from framing the opportunity to a launched and operated product.
  • Research to production transferThe hand-off where most R&D dies. We harden what worked, add observability and governance, and ship it into the real workflow.

Tools of the lab

Representative, not a fixed stack. We pick the instrument for the experiment: AI and ML systems on the first row, mixed reality and spatial on the second.

PyTorch, JAX, Hugging Face, vLLM, Ollama, Weights & Biases, LangGraph, ONNX Runtime

Unity, Unreal Engine, WebXR, Apple visionOS, Meta Quest, OpenXR, Blender, Three.js

Hard problems, shipped.

  • AI platform · Web + mobile

    Prescribe Life

    An AI performance-intelligence platform for leadership coaches. Behavioural science, biometric data and an AI co-pilot flag burnout risk between sessions.

    Read Prescribe Life

  • Research · Multi-agent AI

    Project Protocol

    Asymmetri research into shared machine cognition: can teams of AI agents write what they learn into a shared, inspectable layer that the next agents inherit and build on?

    Read Project Protocol

Questions, answered.

What does R&D with Asymmetri look like?

Applied, time-boxed research aimed at a decision. We start from a question that matters to your operation, such as whether an agent can run a workflow reliably or whether a spatial interface makes training faster. Then we prototype, benchmark against criteria agreed up front and report what we found. Every experiment ends in a clear call: transfer it to production, iterate, or stop.

How do your prototypes reach production?

Transfer is designed in from the first day. Prototypes run on real data paths with evaluation suites and cost measurements, so the evidence carries over. When an experiment passes its pilot, the same team hardens it, adds observability and governance and ships it into the real workflow through our AI and application engineering practices.

Do you build mixed reality experiences?

Yes. Mixed reality is one of Asymmetri's two research tracks, alongside AI and machine learning systems. We design and prototype spatial interfaces that carry intelligence into the physical world, where people work, train and move, using engines and platforms such as Unity, Unreal Engine, WebXR, Apple visionOS and Meta Quest, chosen per problem.

Can we co-develop or incubate a new product with you?

Yes. Alongside client work, Asymmetri designs, builds and operates its own products, including FinishLine, our agentic project management operating spine, and Elk, one AI agent for all of a company's customer conversations. We bring the same model to partners: frame the opportunity together, prove it with prototypes and pilots, then build and run the product with you.

How do we start an R&D engagement?

Bring us the problem, not a specification. A first conversation frames the question and what a good answer would be worth. We then propose a scoped experiment with explicit pass criteria, so you know what will be tested, how it will be measured and what happens if it works. Write to hey@asymmetri.in to begin.

Bring us the hard problem.

We'll find the asymmetric answer.

Get in touch