Agentic AI engineering
Engineer agentic AI that ships to production
Most enterprise AI never ships.
Pilot decks pitch faster cycles, deflected calls, automated workflows. Then the model demo meets the production stack and the project stalls.
The model worked in a notebook. The integration into Connect, Vertex, Salesforce, the data warehouse, the EMR, the IVR, the legacy ERP is where most builds die.
We close that gap. Monogram engineers agentic systems that live inside the infrastructure you already run, with the cloud, framework, and model chosen for the workload rather than the agency.
Production proof
< 4 min
From natural language prompt to working prototype, replacing a manual evaluation that took more than two weeks.
92%
Successful end-to-end bookings by a voice agent running against live APIs on Amazon Connect.
800 to 1,000
Daily users served by a nine agent pipeline in production on GCP Vertex AI.
73%
Less token consumption per request on a production pipeline, with no measured drop in quality.
What we deliver
Freeform voice agents on Amazon Connect, Lex, or GCP equivalents. Real transactions finish on the call: shop, select, pay, book, confirm. See one running inside Amazon Connect.
What separates an AI demo from a system that handles thousands of daily users
How we engineer for production
- Cloud and model agnostic
AWS Bedrock or GCP Vertex AI. Claude, Gemini, GPT, or open source through either cloud, benchmarked for the workload.
- Self-healing pipelines
Multi-layer retry, security auto-fix loops, and fallback to the last good output, so users wait slightly longer instead of seeing errors.
- Model tiering
Reasoning on premium models, classification and validation on faster cheaper ones. On one pipeline this made evaluation about 70% faster.
- Code where determinism matters
Schema validation, documentation lookups, and other zero-ambiguity steps run as code, not model calls, which shrinks the hallucination surface.
- Infrastructure as code
Terraform-managed dev, staging, and production, CI/CD with promotion gates, and telemetry in BigQuery or CloudWatch from day one.
- Local inference when required
When data sovereignty, latency, or cost rule out cloud models, we fine-tune open source models such as Gemma or Llama and run them on your GPUs.
The stack
Clouds: AWS (Bedrock, Connect, Lex, AgentCore, Strands) and GCP (Vertex AI Agent Engine, Cloud Run, Google ADK).
Models: Claude (Haiku, Sonnet, Opus), Gemini (Pro, Flash), GPT-class, and open source through either cloud.
Frameworks: LangChain, LangGraph, Strands, Google ADK, MCP.
Foundation: composable web infrastructure since 2017 across Vercel, Sanity, Contentful, Next.js, and Algolia.
Why teams choose Monogram
- Engineering depth, not slideware
Our public case studies show the architecture, the model selection rationale, and the production tradeoffs. That is the work, not the wrapper.
- Production engineering since 2017
We have shipped software for Google, Delta, IBM, Vercel, and GitHub. Applied AI builds on years of production engineering, not the reverse.
- Cloud and model neutrality
We pick the cloud, model, and framework for your workload, your existing infrastructure, and your data sovereignty requirements, not ours.
Three phases
How we work
- Strategize
We map your stack, find the agent that would pay off first, and produce a build plan with budget, timeline, and risk profile.
- Engineer
Custom-built for your workflows and deployed inside your environment with full observability from the first release.
- Operate
Continuous evaluation, model and prompt upgrades, and integration expansion, so agents improve with traffic instead of decaying.
Common questions
Six to ten weeks is typical. Two to three weeks for a focused proof of concept.
Replace AI pilots with production systems.
Talk to one of our AI systems engineers. We will walk through your stack, identify the agent that would pay off first for your business, and tell you whether a build is worth doing, even if the answer is no.
Talk to an AI systems engineer
