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Invovia

Technologies

Technology Practice

The Technology Foundations Invovia Builds On

A working reference across every layer we operate in, from raw LLM APIs through production observability. These aren’t tools we read about in a briefing deck, they’re the stack our FDEs ship with, and it’s re-certified quarterly as the ecosystem moves.

Stack reference · 3 tiers / 8 layers
AI Core AI Engineering AI Infrastructure
AI Core
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LLMs, APIs & Tokenization

Token economics, context-window behavior, rate limits, and failure modes across every major model, including the cost modeling enterprises need to budget inference at scale. We benchmark new releases against real client workloads before recommending a migration, not after.

Claude GPT-4o / o-series Gemini Llama 3 Mistral
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Agent Frameworks & Orchestration

LangChain, LangGraph, LlamaIndex, Semantic Kernel, and the multi-agent coordination patterns that keep long-running agents reliable: state management, tool-call retries, human-in-the-loop checkpoints, and guardrails against runaway loops.

LangChain LangGraph LlamaIndex Semantic Kernel CrewAI
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AI SDLC Tools

Copilot, Cursor, CodeWhisperer, and the testing and evaluation tooling woven into the dev cycle behind Accelerated AI Delivery, from AI-assisted code review to automated regression suites that catch what a fast-moving team would otherwise miss.

GitHub Copilot Cursor CodeWhisperer Codeium
AI Engineering
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AI Native Engineering Stack

Production agent design, enterprise RAG, and model-integration engineering: retrieval pipelines tuned for accuracy over demo-ware, evaluation harnesses that catch regressions before users do, and integration patterns built to survive a model version bump.

RAG pipelines Fine-tuning Prompt evaluation Vector search
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AI for AMS Stack

Observability synthesis, predictive alerting, and automated remediation tooling that turn application-management-services work from reactive ticket triage into a proactive, largely self-healing operation.

Anomaly detection Auto-remediation Log synthesis
AI Infrastructure
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Cloud & DevOps

AWS Bedrock/SageMaker/EKS, Azure OpenAI/AI Studio/AKS, GCP Vertex AI/GKE/TPU, plus specialized low-latency inference through Groq, Together AI, and Modal for workloads where every millisecond of latency has a business cost.

AWS Bedrock Azure AI Studio GCP Vertex AI Groq Modal
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Data & Vector Stores

Pinecone, Weaviate, pgvector, dbt, MLflow, and Weights & Biases, wired together into pipelines that keep embeddings fresh, experiments reproducible, and model performance tracked release over release.

Pinecone Weaviate pgvector dbt MLflow
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Security & Compliance

NeMo Guardrails, LLM Guard, Lakera Guard, and Rebuff, built into the infrastructure layer rather than bolted on afterward, with prompt-injection defense, PII redaction, and output filtering wired in before an agent ever reaches production. SOC 2, HIPAA, PCI-DSS, and FedRAMP by design.

NeMo Guardrails Lakera Guard LLM Guard Rebuff

Our technology practice re-certifies the stack quarterly: every framework, model, and tool above is something an active FDE has shipped to production in the last two quarters, not a slide from a vendor briefing.