AI & Agentic Systems
Enterprise AI that earns its place in production
We build LLM applications, retrieval systems and AI agents grounded in your data, tested against the ways they actually fail, and wrapped in the governance your risk and compliance teams expect.
The production gap
Most AI pilots never reach production. The model is rarely the reason.
Gartner expects more than 40% of agentic AI projects to be canceled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls (Gartner, June 2025 (opens in a new tab)). The gap is engineering, not intelligence: data that isn't ready, no agreed way to measure accuracy, brittle integrations and no plan for cost, security or operations. That gap is where we work.
What we build
Generative AI applications and copilots
What it deliversAssistants embedded in the tools your teams already use, with role-based access and source citations
Retrieval-augmented generation (RAG)
What it deliversKnowledge systems that answer from your documents and data, with grounding checks that trace every answer to its source
Agentic systems
What it deliversAgents that plan, call tools and act across systems, with bounded autonomy, approval gates and full traces;integration through APIs and the Model Context Protocol (MCP)
Document intelligence
What it deliversExtraction, classification and routing for contracts, invoices, claims and forms, replacing manual review queues
Voice AI
What it deliversLow-latency voice agents for service, scheduling and field operations, with clean escalation to people
AI in your products
What it deliversAI features for SaaS and customer-facing apps, designed for multi-tenant scale and predictable unit cost
Model integration and platform engineering
What it deliversModel selection, routing and hosting across OpenAI, Anthropic, Hugging Face, Vertex AI, Azure AI and Amazon Bedrock
AI operations (LLMOps)
What it deliversEvaluation in CI, tracing, guardrails, cost and latency budgets, and managed model upgrades after launch
How we engineer AI
Five disciplines every AI system we ship has to pass
- 1
Evaluate before you build. A labeled evaluation set and pass criteria are agreed with your domain experts before development starts, then run on every change.
- 2
Ground everything. Answers cite their sources, and retrieval quality is measured separately from generation quality.
- 3
Design for failure. Fallbacks, confidence thresholds and human review for low-confidence or high-impact decisions.
- 4
Govern by default. Input and output filtering, PII handling, access control, audit logs and model version tracking from day one.
- 5
Budget cost and latency. Targets set early and monitored in production, with model routing and caching to hold them.
Ecosystem
The right model for the job, not the logo
We evaluate candidate models on your task and data, then choose on accuracy, latency, cost and data residency. Systems are built so models can be swapped without a rewrite. TrizenAI is an Anthropic Partner and an OpenAI Select Partner, and we deliver on Google Vertex AI, Azure AI and Amazon Bedrock.
Engagement path
- 1
AI opportunity sprint 2–3 weeks: prioritize use cases by value, feasibility and risk; define success metrics; assess data readiness.
- 2
Pilot to production 8–12 weeks: build against the evaluation set, integrate with your systems, harden security and launch to a controlled group.
- 3
AI operations (ongoing): monitor quality, cost and drift; rerun evaluations on every model or prompt change; expand to new workflows.
Use cases by industry
Financial services
Where AI pays off firstKYC and onboarding document review, analyst copilots, dispute and claims triage
Healthcare
Where AI pays off firstClinical documentation summaries, prior authorization support, patient-communication assistants
Retail and e-commerce
Where AI pays off firstProduct content at catalog scale, conversational commerce, returns and support automation
Media and entertainment
Where AI pays off firstMetadata enrichment, localization workflows, editorial research assistants
Manufacturing
Where AI pays off firstMaintenance copilots over manuals and sensor history, quality-report analysis
Logistics
Where AI pays off firstException-handling agents, document capture for shipping and customs paperwork
FAQs
Will our data be used to train public models?
We design to your data policies: enterprise API agreements, retention settings, and private deployment inside your own cloud account on Bedrock, Azure or Vertex AI where data must stay in your environment.
Which models do you use?
Whichever performs best on your evaluation set within your cost, latency and residency limits. We revisit the choice as models improve.
How do you measure accuracy?
With a test set built alongside your domain experts, scored automatically in CI and reviewed by people for nuanced cases. You see the scores before anything goes live.
How long until we're in production?
It depends on data readiness and integration scope. A focused use case typically reaches a controlled launch in 10–14 weeks.
What happens after launch?
AI operations: quality, cost and drift monitoring, regression testing on every change, and planned model upgrades.
Bring us the workflow, not just the idea
Tell us where AI could change a process. We'll show you what it takes to get it into production safely, and what it will cost to run.
Talk to an AI architect