Engineer agentic AI that ships to production
For enterprise teams whose AI pilots stalled at production. We engineer voice agents, multi-agent pipelines, and copilots inside your AWS or GCP stack, and ship them in weeks, not quarters.
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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.
What we deliver
Four shapes of agentic systems, running in production today.
Deflect calls and complete transactions without an agent
Freeform voice agents on Amazon Connect, Lex, or GCP equivalents. Sub-2 second latency. Real customer transactions: shop, select, pay, book, confirm. Not demos. 92% end-to-end success in production.
Compress weeks of expert work into minutes
Specialized agents with single responsibilities, dynamic instruction injection, and programmatic validators where determinism matters. Production proof: a 9-agent pipeline serving 800–1,000 daily users.
Answer questions from your real data, not training data
Agents grounded in your knowledge bases, vector stores, and live APIs through MCP. No hallucination from stale training data. Full audit trail of what context shaped each answer.
Replace the expert manual work that bottlenecks your team
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.
Production proof
< 4 min
from natural language prompt to working prototype, replacing a 2+ week manual evaluation
92%
end-to-end automation on a live customer-facing transaction workflow
800–1,000
daily users served by a 9-agent pipeline in production
73%
token reduction through architectural optimization, no quality loss
Featured deployments
Two flagship deployments across both clouds
Developer platform at scale on GCP Vertex AI
Natural language to working prototype in under 4 minutes, replacing a 2+ week manual evaluation. Serves 800–1,000 daily users on GCP Vertex AI with Gemini 2.5 Pro and Flash. Self-healing pipeline with security auto-fix and three-layer retry. 73% token reduction and 70% faster evaluation cycles through architectural optimization.
Read the case study
Inbound bookings on Amazon Connect
Replaced a rigid IVR with a freeform agent that completes the full shop, select, pay, book, confirm flow inside Amazon Connect. Built on Lex, Bedrock, AgentCore with Claude Haiku and Sonnet. 92% end-to-end transaction success, sub-2 second latency, full payment isolation from the AI layer.
Read the case studyHow we engineer for production
This is what separates AI demos from AI systems that handle thousands of daily users.
- Cloud and model agnostic
AWS Bedrock or GCP Vertex AI. Claude, Gemini, GPT, or open source via either cloud. We benchmark for the workload and pick the right combination.
- Self-healing pipelines
Multi-layer retry, security auto-fix loops, graceful fallback to last known good outputs. Production users wait slightly longer rather than seeing raw errors.
- Model tiering and cost engineering
Reasoning-intensive work on premium tier models, classification and validation on faster cheaper ones. We've achieved 70% latency reductions and 73% token savings on production pipelines through this discipline alone.
- Programmatic where determinism matters
Schema validation, documentation lookups, and other zero-ambiguity tasks run as code, not LLM calls. Smaller hallucination surface, lower operating cost.
- Infrastructure as code
Terraform-managed environments (dev, staging, production), automated CI/CD with promotion gates, BigQuery or CloudWatch telemetry, structured observability from day one.
- Fine-tuned open source models for local inference
When data sovereignty, latency, or cost rule out cloud models, we fine-tune open source models like Gemma or Llama for your domain and run them on your own GPUs. No tokens metered, no third-party in the inference path.
Our stack
- Clouds
- AWS (Bedrock, Connect, Lex, AgentCore, Strands). GCP (Vertex AI Agent Engine, Cloud Run, Google ADK).
- Models
- Claude (Haiku, Sonnet, Opus). Gemini (Pro, Flash). GPT-class. Open source via either cloud.
- Frameworks
- LangChain, LangGraph, Strands, Google ADK, MCP.
- Foundation
- Composable web infrastructure since 2017 across Vercel, Sanity, Contentful, Next.js, Algolia.
Why teams choose Monogram
Applied AI sits on top of years of production engineering, not the other way around.
Engineering depth, not slideware
Our public case studies show architecture, model selection rationale, and production tradeoffs. That is the work, not the wrapper.
Composable 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.
Cloud 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.
MCP-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.
How we work
We map your stack, identify the highest-leverage agent, and produce a build plan with budget, timeline, and risk profile.
Custom-built, tailored to your workflows, deployed inside your environment with full observability.
Continuous evaluation, model and prompt upgrades, integration expansion. Your agents improve with traffic, not decay.
Common questions
- How fast can you ship a production agent?
- Six to ten weeks typical. Two to three for a focused POC.
- Which clouds and models do you support?
- AWS Bedrock and GCP Vertex AI are primary. We work with Claude, Gemini, GPT, and open source models via either cloud. Azure on request.
- Will my data leave my environment?
- No. Agents deploy inside your AWS account, GCP project, or VPC. Customer data stays where it already lives.
- Do you have a POC offer?
- Yes. Two-week Strategize engagement followed by a four-to-six week POC. Fixed scope, fixed price.
- Can you augment my existing AI team?
- Yes. Many engagements are augmentation, with Monogram providing senior agentic engineering capacity alongside an internal team.
Replace AI pilots with production systems.
Talk to one of our AI systems engineers. We'll walk through your stack, identify the highest-leverage agent for your business, and tell you whether a build is worth doing. Even if the answer is no.
Talk to an AI systems engineer