Engineering
Agentic AI engineered for production
We design and deploy custom agentic AI systems that connect to your products, data, and operations to automate entire workflows end to end. No generic software. No massive migrations.
Production proof
< 2 sec
Voice response on live customer calls
92%
automation on a live customer facing workflow
800–1,000
daily users served by a 9 agent pipeline in production
73%
token reduction through architectural optimization, no quality loss
A few shapes of agentic systems we ship
- Multi-agent orchestration
Specialized agents with single responsibilities, dynamic instruction injection, programmatic validators where determinism matters. Production proof: a 9-agent pipeline serving 800-1,000 daily users.
- Voice & conversational interfaces
Built on Amazon Connect, Lex, or GCP equivalents. Sub-2 second latency. Real customer transactions, not demos. Live API integration with payments, inventory, and identity.
- MCP & RAG knowledge agents
Agents grounded in your real data through MCP-connected knowledge bases, vector stores, and live API access. No hallucination from training data. Full audit trail of what context shaped each answer.
- Internal copilots & workflow automation
Multi-step reasoning, human-in-the-loop, memory across sessions. For due diligence, document processing, customer service, reconciliation, sales enablement, and the long tail of internal work that consumes expert capacity.
Agent ops and evaluation systems
Most agents pass the demo and drift in production. This is the layer that keeps them honest.
Review queues, eval suites, telemetry, and regression tests that make agent behavior measurable instead of anecdotal. Low confidence outputs route to people, drift gets caught before users feel it, and prompts, tools, and policies improve against real usage rather than a static test set.
Every run is logged: what the agent was asked, which tools it called, what it decided, what it returned. When something breaks, the cause traces to the exact step that produced it rather than a hunt through the whole chain.
REGRESSION SUITE ON EVERY RELEASE · ~8% ROUTED TO REVIEW
Applied AI Showcase
AI deployments across multiple clouds
Why Monogram
Applied AI sits on top of years of production engineering, not the other way around.
- 01Engineering depth, not slideware
Our public case studies show architecture, model selection rationale, and production tradeoffs. That is the work, not the wrapper.
- 02Composable foundations since 2017
We've shipped enterprise software for Google, Delta, IBM, Vercel, GitHub, and dozens of teams. Applied AI builds on years of production engineering, not the reverse.
- 03Cloud and model neutrality
We're not selling you our preferred stack. We're picking the right cloud, model, and framework for your workload, your existing infrastructure, and your data sovereignty requirements.
- 04MCP-native and protocol forward
We've been building on the composable thesis since before MCP existed. Now that it does, our agents work from real data through standardized protocols, not custom integrations that break on the next release.
