Invisible Technologies
Invisible Technologies Inc. · Productivity
Invisible Technologies is an enterprise AI platform that combines managed AI agents, evaluation tooling, and a global expert network to train models and run operational workflows.
About Invisible Technologies
What Is Invisible Technologies
Invisible Technologies is a New York company, founded in 2015, that builds and runs AI systems for large organizations. Its work sits in two places at once. One side trains and tests frontier models with human-reviewed data. The other side puts AI agents into back-office automation: claims review, compliance checks, and order handling.
The company describes its platform as building blocks rather than a single product: a data layer, a process builder, an agent runtime, an evaluation suite, and a network of vetted domain experts. According to Invisible, more than 80% of the world's leading AI models have been trained with data it helped produce. That claim comes from the vendor, so treat it as positioning rather than an audited figure. The client list it publishes includes Cohere and the Charlotte Hornets.
The main limitation is access. There is no self-serve signup, no public price list, and no way to test the platform without talking to sales first. Engagements start with a scoping project, which means weeks of setup before you see a working workflow. Small teams and solo operators are not the audience here. This is enterprise AI infrastructure, priced and paced for organizations with messy data and a budget to fix it.
Getting Started
- Book a demo through the Invisible website and describe the process you want to automate or the model you need to evaluate.
- Run a scoping engagement with Invisible engineers, who map how the work happens today, including the exceptions your current tools skip.
- Connect your systems to the Neuron data layer and configure the relevant components, such as Atomic for process flows or Synapse for evaluations.
- Launch a pilot workflow with expert review in the loop, then promote what passes review into production with versioning and audit trails.
Product Information
A quick look at Invisible Technologies's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Enterprise operations leaders
- AI labs and model teams
- Data and platform engineers
Tasks
- Building supervised fine-tuning and RL environments
- Running AI evaluations
- Automating document-heavy back-office work
- Cleaning and structuring fragmented data
Scenarios
- A support organization wants an agent that handles tier one tickets but hands edge cases to trained specialists
- A company is choosing between two model vendors and needs evidence rather than benchmark scores
- An insurer or bank has a compliance process that changes every quarter
Key features
Axon Agent Runtime
Axon runs AI agents that work through multi-step tasks, call tools, and write code when a step needs it. Each workflow is versioned and separated by customer, so you can test a change on one account before rolling it out. Agents hand finished results back to your systems rather than leaving them in a chat window. Invisible reports a 70% reduction in workflow processing time across clients, which is a vendor figure.
Synapse Evaluations
Synapse answers a narrow question: is this AI good enough for the work we do? You define what a good response looks like, run models through realistic scenarios, and get automated checks plus a side-by-side view of where each one passes or fails. The difference from a public leaderboard is that the criteria come from your domain, not someone else's test set.
Atomic Process Builder
Atomic is a visual map of how work actually moves through your operation. You document the current process, then replace manual steps with AI or human routing one at a time. Every piece of work stays traceable, which matters when an auditor asks who touched a case and when. Invisible puts the average reduction in process complexity at 80% measured by step count.
Neuron Data Platform
Neuron collects messy data from multiple systems, cleans it, and builds a map of how records relate: orders, inventory, customers, processes. That map is what lets an agent know where to look without weeks of manual configuration. For teams stuck with data spread across a CRM, an ERP, and a pile of spreadsheets, this is the layer that makes the rest usable.
Meridial Expert Network
Meridial is a pool of more than 24,000 vetted specialists, screened by skill, language, and domain. They handle the work models still get wrong: labeling ambiguous training data, stress-testing outputs, and catching edge cases during a pilot. You get a human layer without recruiting, contracting, and training a bench of your own.
AI Training Data and RL Environments
Invisible builds training sets for frontier models, including demonstrations, preference data, and reinforcement learning environments with graded tasks. The work is aimed at reasoning and agentic behavior rather than simple classification, so tasks are designed by people who understand the failure modes. Data quality is reviewed before delivery, and the same platform can run the evaluation that follows.
Pros and cons
Pros
- One vendor covers training data, evaluation, agents, and expert staffing, which removes the coordination work of running four separate contracts.
- The expert network is deep and vetted by domain, so niche work like legal review or clinical coding does not stall for lack of qualified people.
- Workflows are versioned per customer, which makes pilots reversible and gives compliance teams something to audit.
- The platform deploys on your existing stack, so you are not migrating data or replacing core systems to start.
- Published client references include model labs and sports analytics teams, a sign the tooling handles both research and commercial work.
Cons
- Pricing is quote based and undisclosed, so budgeting requires a sales cycle before you know whether the platform fits.
- There is no free tier or self-serve trial, which puts it out of reach for small teams and individual builders.
- Onboarding depends on a scoping engagement, so the first working workflow can take weeks rather than an afternoon.
- Most workflow logic lives in Invisible's platform, which creates real switching costs if you later want to move the process in house.
Frequently asked questions
It runs two connected businesses on one platform. It produces AI training data and evaluations for model builders, and it deploys AI agents plus human experts into enterprise back-office processes. The shared platform components are Neuron for data, Atomic for process mapping, Axon for agents, Synapse for evaluations, and Meridial for the expert network.
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