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  • 01 · INTELLIGENT AGENT ARCHITECTURE

AI Engineering.

From clever demo to regulated production.
  • BRIEF
Most AI projects don’t fail at the model — they fail in the six months after the demo. We build the agents, retrieval systems, and evaluation harnesses, then operate them on infrastructure your security, compliance, and finance teams will all sign off on.
  • CAPABILITIES
  • Custom AI Agents
Multi-step, tool-using agents · planning, memory, guardrails, human-in-the-loop
  • RAG & KNOWLEDGE BASES
Hybrid retrieval over your docs, tickets, code · evaluation harness included
  • LLM INTEGRATION & FINE-TUNING
GPT-class, Claude, Llama · vendor-neutral routing, fine-tuning where it earns its keep
  • GENAI STRATEGY & ADVISORY
What to build, what to buy, what to wait on · 4-week strategy engagements
  • NLP, VISION & FORECASTING
Domain models for document understanding, vision, predictive analytics
  • AI WORKFLOW AUTOMATION
Business processes re-engineered with agents in the loop — not as a chatbot bolt-on
  • AI MVP & RAPID PROTOTYPING
Five-week MVPs · measured against real KPIs, not vibes
  • AI SAFETY & EVALUATION
Red-teaming, eval suites, drift monitoring, prompt-injection defences
  • OUTCOMES

What we've actually moved.

A few rolling averages across active engagements. Quoting them is easy — the work behind them is the engagement.

5 wk.

median time from kickoff to a usable, production-graded AI MVP

86%

agent task-completion rate on the workflows we ship — measured, not estimated

0

incidents of prompt-injection breach on systems we currently operate

Want this calibrated to your stack?

Book a working session
  • Also part of services
  • 02 / 06

AIOps & DevOps

CI/CD, infrastructure-as-code and observability — augmented with intelligent alerting, smart rollback and auto-remediation. Engineers ship on Friday, models page themselves on Saturday.
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  • 03 / 06

Cloud Platform & FinOps

AWS, Azure and GCP architecture, migration and AI-driven cost optimization. We size, harden and right-cost your platform — typically a 30–50% reduction within two quarters.
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  • 04 / 06

Security & Compliance

AI-first SaaS products, LLM-integrated software, and generative UIs. We build production-grade applications that scale seamlessly.
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  • 05 / 06

Custom & Product Development

AI-first SaaS products, LLM-integrated software, and generative UIs. We build production-grade applications that scale seamlessly.
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  • 06 / 06

Data & MLOps

Governed lakehouses, robust MLOps lifecycles, and streaming pipelines. Turn raw telemetry and corporate data into fine-tuning-ready data assets.
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Frequently Asked Questions

Everything you need to know about working with TRIOTECH SYSTEMS.

What is Intelligent Agent Architecture and how does it work?

Intelligent Agent Architecture shifts software from static code execution to autonomous decision-making. Instead of following rigid, hard-coded rules, an intelligent agent utilizes an LLM core as its central reasoning engine, paired with discrete memory modules and specialized tool-calling capabilities. When given an objective, the agent dynamically breaks the task down into sequential steps, calls external APIs or databases to gather context, evaluates its own intermediate outputs, and self-corrects until the target goal is successfully achieved.

Our structured delivery framework typically produces a stable, production-ready AI Minimum Viable Product (MVP) within 8 to 12 weeks. The first 2 weeks focus entirely on scoping, dataset evaluation, and architecture blueprints. By week 4, we deploy an initial proof-of-concept to validate model accuracy and latency baselines. The remaining weeks are spent hardening the application tier—building out data integration contracts, optimizing vector search indexes, implementing strict security guardrails, and embedding the system into your continuous delivery pipeline.

We deploy a multi-layered defense matrix to isolate and protect language models from malicious inputs and data leaks. Rather than relying on basic system prompt instructions, we implement dedicated validation layers—such as defensive input filtering and real-time semantic screening—to detect and block adversarial injection attempts before they hit the LLM. Furthermore, we enforce strict structural output validation to guarantee model responses conform to expected safety schemas, and decouple the agent’s tool execution layer so it can never run arbitrary system commands.

We execute a phased integration strategy that wraps your existing legacy infrastructure in secure, modern data and API abstractions rather than forcing a risky lift-and-shift rewrite. We begin by setting up real-time Change Data Capture (CDC) pipelines to safely stream data out of your legacy databases into an optimized, governed lakehouse architecture. Once this accessible data foundation is live, we deploy microservices and intelligent orchestration layers on top of your existing systems, allowing you to run automated AI workflows and agents without disrupting your core daily business operations.

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