Platform

Every project needs several compute profiles at once: classification, generation, vectorization, each with different latency and cost constraints. One model and one shared pool of capacity do not work in an enterprise environment. Tasks compete for resources, and a single provider outage can freeze the whole system — exactly what happened on the transport project before we split workloads across separate instances with automatic fallback.

We split inference into four GPU instance types: reasoning, fast generation, vectorization, and vision. The router distributes requests, flips to a backup model on failure, and enforces quotas and priorities by project.

Latency and error rate do not explain why the agent responded the way it did or how much one outcome actually cost. On the telecom project it was business metrics, not infrastructure metrics, that showed where the agent already beat the operator and where it had to stay out of the loop.

We collect two layers of metrics. The engineering layer traces every call, every tool chain, and token-level cost. The business layer tracks support funnels, automation share, and cost per outcome.

Prompt injection and data leakage are baseline threats, and standard libraries can catch them. But every industry also has its own prohibitions that no generic library understands. In the investment project, the agent began hinting at answers to qualification tests — something the regulator bans outright.

We place filtering on both sides of every model call: data masking, attack detection, and business-specific rules. In banking we built a multi-layer compliance system with prompt constraints, refusal scenarios, checker loops, and audit logs for every answer.

In the investment project, compliance became a multi-layer stack: prompt constraints, refusal paths, checker loops, and audit of every answer. Case →

Quality cannot be checked once and forgotten. Models change, data changes, prompts drift, and answers get worse. On the transport project, a binary “confident / not confident” threshold created too many false escalations: the system sent tickets to operators that it was actually capable of answering safely.

We built a three-pass confidence formula with 30+ parameters calibrated on real requests. It decides when the agent can answer and when a human is still required. Alongside it run LLM judges, production benchmark cases, and synthetic datasets so quality regressions are caught before production, not after a complaint.

In the transport project, the key artifact was a three-pass confidence formula with more than thirty production-calibrated parameters. Case →

Every company has its own regulations, knowledge base, and normative documentation. Standard RAG can retrieve a vaguely similar paragraph, but enterprise tasks are stricter. On the telecom project, tariff questions required exact numbers from tables while vector search kept returning approximate narrative matches.

We built a dual index: one branch for semantic retrieval and another for exact data such as tariff tables, prices, and technical parameters. Pure vector search tends to lose numbers and tables because they vectorize badly.

In the telecom project, a dual index separated semantic search from exact-table retrieval with prices and parameters. Case →

An agent in a demo answers questions. An agent in production must remember context across sessions, call tools, follow a scenario, and escalate to a human at the right boundary. In the finance project, the agent ran a strict sales funnel, remembered previous client conversations without mixing products, and stayed inside compliance limits — effectively a finite-state machine with multiple control loops.

We built orchestration, chat, and memory infrastructure so it does not need to be rebuilt on every project. A dedicated component, Content Digital Twin, is responsible for corporate tone: it took more than sixty iterations before the agent sounded like an actual employee rather than a chatbot.