Signal 01
RAG is no longer a background detail
Ingestion queues, vector search, refresh logic, permissions, and quality metrics start affecting the product experience.
AI Platform Engineering
AI Platform Engineering for LLM products moving beyond MVP.
ToolLeap helps AI teams turn working prototypes into production and enterprise-grade platforms: RAG, agent tools, customer-owned code, CI runners, Kubernetes, GPU inference, observability, and private deployments.
When it matters
The MVP worked. The platform is now the product.
Infrastructure becomes strategic when AI workloads start affecting margin, reliability, security, enterprise sales, or customer-owned execution.
Signal 02
Agents need controlled tools
Actions require secrets, audit logs, limits, retries, approvals, and a runtime that does not expose the rest of the platform.
Signal 03
Enterprise asks for isolation
Private deployments, data residency, dedicated runners, and local inference become part of the sales motion.
What we build
A practical stack for AI platforms
RAG
RAG infrastructure
Queues, workers, vector databases, ingestion observability, permissions, refresh paths, and quality metrics.
AGT
Agent tool runtime
Secrets, approvals, audit trails, limits, retries, network boundaries, and safe execution paths for actions.
RUN
CI runner isolation
Runner pools, customer-owned code, build minutes, registry flows, sandboxing, and private network access.
K8S
Kubernetes and private deployment
Cluster design, GitOps, environment isolation, observability, SLOs, policy controls, and data residency.
GPU
Hybrid LLM inference
Routing between external APIs and local models with cost, latency, privacy, fallback, and GPU observability in mind.
OPS
Observability and cost control
Tenant margin, model spend, queue health, latency, error budgets, auditability, and platform dashboards.
Proof point
Browser tooling and controlled execution are already in our hands.
WebTerm is a small public example of the patterns behind agent tools and customer-owned code: browser-native execution, session boundaries, auditability, and operational cleanup.
Open WebTerm
Engagement model
Audit, architecture, build, operate
01 Audit
Map the current platform stage
We review architecture, workloads, data flow, operational pain, security gaps, and business triggers.
02 Roadmap
Choose the next infrastructure move
We sequence what to keep managed, what to build, what to isolate, and what to defer until the business needs it.
03 Build
Ship the platform capability
We implement the runtime, pipeline, runner pool, Kubernetes layer, observability, or deployment path with your team.
04 Operate
Turn it into an operating model
We leave dashboards, playbooks, ownership boundaries, and follow-up improvements for the next growth stage.
Next step
Get a roadmap for the infrastructure your AI product needs next.
In ten business days, ToolLeap maps risks, cost drivers, security gaps, architecture options, and the build sequence.