Production agentic AI on Google Cloud. Multi-agent systems on ADK, MCP servers, and the data platforms underneath them. Engineered for production, not demos.
Built on
Built by a team, architected by a
Google Developer Expert
Named engagement architect
Dedicated delivery lead per engagement
Partner capacity for scale
Weekly architecture review
Partners & Certifications
We build production systems for enterprises. That's why our training isn't theory, and why our products aren't demos.
Built in production
Enterprise delivery of agentic AI systems on Google ADK and MCP, plus the data platforms underneath them. Architecture-first, documented, handed over.
Taught from production
TechTrapture Academy and corporate training, built from live deployments rather than slideware. Live cohorts, hands-on labs, real projects.
Productised from the same engineering
The patterns that recur across engagements become products. Capci for on-brand visuals over an API and MCP; TraptureIQ for agent governance.
Live Agent Execution
Goal Received
"Production error spike on checkout service, diagnose and report"
Monitoring Agent
Detected error rate anomaly, Cloud Monitoring
DoneLogs Agent
Correlated logs and recent deploys, Cloud Logging
DoneRCA Agent
Root cause identified, traced to config change
DoneReport Agent
Business-impact alert drafted
DoneOutput Delivered
RCA and stakeholder alert generated, awaiting human approval before fix
Agents That Are
Autonomous & Safe
Our agents don't just complete tasks. They do it securely, efficiently, and within budget. A hierarchical planner dispatches specialist sub-agents that act, self-correct, and report back.
Autonomous Agents
Agents receive a goal and independently plan, tool-call, and execute multi-step tasks without human prompting.
Sub-Agent Hierarchies
A supervisor agent breaks work into subtasks and dispatches specialist sub-agents (Research, Code, Analytics, Writer) in parallel.
Secure Agents & Guardrails
Role-based access, sandboxed tool execution, input/output content filtering, and policy guardrails keep every agent action safe and auditable.
Cost Control & Model Routing
Budget caps, per-task spend limits, and intelligent model routing (Gemini Flash vs Gemini Pro vs Claude Opus) slash LLM costs without sacrificing quality.
Token Optimisation
Prompt compression, semantic caching, context-window management, and chunked retrieval minimise tokens consumed per agent run.
The Frontier Stack
We engineer at the protocol level, not the wrapper level. The same stack runs every agent we put in production.
Models
Multi-Model Routing
Every task routed to the right model by cost, latency, and quality, within a GCP-native architecture.
Agent Protocols
Protocol-Level Agent Engineering
Native tool servers and agent-to-agent interop. Multi-agent systems built on open protocols, not prompt wrappers.
Cloud Infrastructure
Enterprise GCP Foundations
Serverless, autoscaling, IAM-clamped infrastructure. Every deployment is production-grade from day one.
Production Layer
Evals, Guardrails & Observability
Every agent ships with eval suites, policy guardrails, full tracing, and budget enforcement, all powered by TraptureIQ. Meet TraptureIQ
Our Process
A streamlined path from discovery to autonomous deployment
Discovery
Deep-dive into your goals, data landscape, and automation potential.
Architecture
Design AI-native systems: agents, pipelines, and cloud foundations.
Build & Train
Engineer models, orchestrate agents, and wire up integrations.
Deploy & Scale
Launch with full MLOps, agent monitoring, and continuous improvement.
Tell us what you need
in production.
We'll come back with an architecture, an engagement shape, and the case study closest to it.