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Fast-track your enterprise AI adoption. Your AI worker team builds production software while you focus on the business. No technical skills required.
AI delivery team
PROMI scopes the work, routes handoffs, and keeps progress visible while Cortex preserves the project trail.
Operating proof
Kaidera combines specialist AI workers, PROMI orchestration, Cortex memory, model routing, harness controls, and review evidence into one operating system for AI delivery.
Keith turns plain-language business goals into scoped work with review expectations.
PROMI routes the work, manages handoffs, and keeps progress visible between loops.
Specialist AI workers build, test, document, and package the evidence before review.
Decisions, artifacts, and lessons become reusable project memory for the next task.
Kaidera is for teams that need software outcomes, operational control, and AI adoption momentum without turning every initiative into a long hiring and procurement cycle.
Use Kaidera when you need a real prototype, internal tool, integration, workflow, or product increment without waiting months to assemble a full team.
See the enterprise path →Steering, approval gates, project boundaries, audit evidence, and human review make the work inspectable before anything important is promoted.
Explore trust controls →Cortex preserves decisions, handoffs, artifacts, knowledge links, and boot context so each session starts with the project memory already loaded.
See Cortex →Reasoning, code, vision, voice, embeddings, fine-tuned models, and managed model programs can be routed by business need and provider health.
Understand model routing →Kaidera is not selling another generic AI assistant. It gives customers AI workers with persona, specialisation, platform memory, handoffs, verification, and human approval.
For product, platform, workflow, and integration projects, the Lead worker becomes the CPO role. Keith is the current example: he turns business intent into scope, acceptance criteria, worker handoffs, and review-ready delivery.
For customer-specific operations, the same Lead pattern becomes the role the project needs: CMO for a marketing operation, compliance lead for regulated review, or another accountable business lead. Marlow is the marketing-team example we run ourselves.
Kaidera Infrastructure
Kaidera Infrastructure designs, deploys, and runs enterprise environments across AWS, Google Cloud, Microsoft Azure, and hybrid datacenters. One AI team brings architecture, DevSecOps, reliability, security, service management, and FinOps into an evidence-backed operating model.
Cloud, datacenter, hybrid, and multi-region architecture.
Portable infrastructure as code with controlled rollout.
Monitoring, incidents, patching, backup, and recovery.
Security, service controls, evidence, approvals, and FinOps.
The hard part is not buying access to a model. It is turning AI into a repeatable way to plan, build, verify, remember, and govern work. Organisations that solve the operating model learn faster with every project.
The advantage compounds when every project leaves behind reusable decisions, patterns, artifacts, and review evidence instead of another disconnected chat thread.
The platform now has a clearer story: the team, the operating layer, the control layer, the model layer, the workspace, and the review path all work together.
Specialist workers for product, orchestration, backend, frontend, and knowledge work.
Work planning, handoffs, drift checks, scheduled follow-up, and review gates.
Memory, communication, handoffs, steering context, guardrail evidence, and boot context.
Controlled workspaces with scoped tools, evidence capture, rollback points, and permissions.
Capability-aware routing across reasoning, code, vision, voice, embeddings, and providers.
Fine-tuning, training data, dedicated endpoints, GPU/TPU capacity, and isolated deployments.
Self-hosted Excalidraw diagrams saved in the project vault for shared understanding.
Temporary production-like environments for review, demo, acceptance testing, and promotion.
The homepage should make the operating loop obvious: business intent becomes scoped work, specialist AI workers execute, review evidence is prepared, and the learning stays in the project.
Start with the business goal, constraints, target environment, and what review-ready means.
The goal becomes bounded handoffs with owners, next actions, escalation paths, and evidence expectations.
Specialist AI workers work in their lanes, use approved tools, and verify before claiming completion.
Decisions, lessons, artifacts, and handoffs stay available to future sessions and future AI workers.
Kaidera OS
Kaidera OS is the Kaidera harness as a local app: a project-agnostic control plane for AI workers, Cortex-backed memory, and run state. Free to download for macOS and Linux, with licensing that unlocks the functionality you need.
Book a live demonstration and see how Kaidera turns a business goal into scoped work, AI worker execution, review evidence, and a working dev environment.
Learn more
The website explains the story. The docs explain how users operate Kaidera: getting started, Cortex memory, model capabilities, trust controls, and practical how-to guides.
A practical orientation for evaluating Kaidera and understanding the operating loop.
How project memory, handoffs, evidence, and context continuity work at a user level.
Reasoning, vision, voice, embeddings, routing, and dynamic model catalog boundaries.
Start projects, review work, approve releases, configure models, and inspect evidence.