ProtoleapAI INFRASTRUCTURE
Compute, model serving and the runtime around them. Built as one system, from deployment to day-to-day operation.
HOW WE HELP
Expand capacity, connect your systems and improve how work gets done—with clear ownership, practical automation and control over cost.
EXPLORE OUR EXPERTISE
We size capacity from measured behaviour, not hardware counts. We characterise your demand, benchmark compute and inference services under load, and set the concurrency limits, SLOs and runbooks that keep the system inside them. Telemetry from production then feeds back into those limits, so capacity tracks what the workload actually does.
We establish which system owns each record, how it is identified and what a valid change looks like. Integrations enforce those contracts, handle duplicate and out-of-order events, and reconcile missed updates. Where AI extracts or enriches information, we retain the source, model version and review status alongside the result—so a downstream decision can be traced back to the evidence that informed it.
We turn business rules into checks at the point of execution. Each action carries an identity, an approval scope and the record version it was authorised against; the system rechecks those conditions before committing a change. We use idempotent commands where supported and reconcile uncertain outcomes before retrying, so a timeout does not become a duplicate order, payment or inventory adjustment.
Our robotics and vision work starts with the measurement: the defect to detect, the tolerance to hold and the cycle time available. We develop the acquisition setup around optics, lighting and calibration, then use simulation to vary conditions systematically. Independent physical samples establish false-accept and false-reject rates; site trials test operator handoffs and recovery before a design is considered ready for production.
OUR APPROACH
We define what success means, test against real operating conditions, and put explicit controls around changes to your systems.
We connect the records needed for a task, check their structure and freshness, and retain their source and version. Access is limited to authorised data, and missing or conflicting information is flagged before it is used.
SELECTED WORK
Four systems built for Our Babylon LLC, spanning manufacturing execution, procurement, product development and production planning.
We built a manufacturing execution system that connects material staging, weighing, consumption, returns and production status in one operational record. Automated checks surface quantity discrepancies and process exceptions, while a real-time operations cockpit gives management visibility into work in progress and issues requiring intervention. Lot-level traceability and inventory reconciliation connect activity on the production floor with the records used to run the business.
We built procurement agents to identify and engage prospective suppliers, negotiate commercial terms and track purchase orders. The surrounding system connects supplier communications, order documents, approvals and shipment updates, and triggers warehouse receiving workflows as orders progress. This brings sourcing, purchasing and inbound logistics into a coordinated process, giving teams a shared view of supplier commitments, order status and the next action required.
We built a formulation agent supported by a layered, domain-specific knowledge base spanning books, research papers, patents and competitor information. It supports formulation analysis and research synthesis, while a marketing agent develops marketing materials. A custom business-validation system tests assumptions through scenario simulation, including adverse conditions and adversarial behaviour. Together, these tools connect technical research, market positioning and commercial evaluation; formulation proposals remain subject to laboratory testing and expert review.
We built a production-planning optimizer that automatically forms batches and schedules work against equipment availability and product-specific manufacturing requirements. It reserves equipment for planned operations and identifies compatible production activities that can run concurrently. By considering batch grouping and shared-resource constraints together, the system coordinates multiple products within the available capacity, with the objective of improving equipment utilisation and reducing avoidable idle time.
PROTOLEAP / APPLIED INTELLIGENCE
Two research programmes, one principle. In digital systems the model proposes a record; in physical systems it proposes a motion. Neither becomes real until a deterministic gate accepts it, and every accepted change carries its evidence, its author and its permission.
Understand before writing.Verify before committing.
Craton is a system of record built for the moment information enters an organisation. Models read what arrives, connect it to what is already known, and propose how the record should change. Every proposal is checked against evidence and policy before it is accepted, so the record never contains an unverified claim, and every accepted fact carries where it came from and who or what asserted it. The result is institutional memory that can be trusted, queried and audited, without slowing down the people who depend on it.
Perceive before acting.Monitor while moving.
Praxis is a programme in machine vision and robotics for environments where a wrong move is expensive. Perception builds a live model of the workspace, and planning proposes actions against that model rather than against assumptions. Independent controllers and interlocks enforce the limits of safe motion, and sensor feedback watches every action as it happens: detecting deviation, stopping, and recovering. Behaviour is proven in simulation and physical trials before it is trusted with real work.
LET’S BUILD THE FOUNDATION
Tell us about your business, the challenge you’re working through, or an idea you’d like to explore.