AI Technologies

AI Technologies

A structured overview of the technologies we use to build reliable AI products — what they do, why they matter, and when to use them.

Foundations

Data & problem framing
We start from measurable outcomes, define success criteria, and map the data and workflows that the AI system will touch.
Architecture for production
Clear boundaries (UI, API, domain), observability, and safe defaults so the solution is maintainable and extensible.

LLMs & AI agents

LLMs (large language models)
Natural-language reasoning for Q&A, summarization, extraction, and multi-step workflows.
Tool-using agents
Agents that can call tools (CRM, calendars, ticketing) with guardrails and structured inputs/outputs.
Voice and phone agents
Low-latency speech-to-speech experiences with turn-taking, interruption handling, and call analytics.

RAG & knowledge bases

RAG (retrieval augmented generation)
Ground model answers in your documents to reduce hallucinations and keep responses up to date.
Embeddings + vector search
Semantic search over your content to fetch the best context for each question.

Automation & integrations

Workflow automation
Connect systems and automate repetitive tasks while keeping humans in the loop where needed.
API integrations
Robust integrations with authentication, retries, idempotency, and audit logs.

Quality, evaluation & monitoring

Evaluation (offline + online)
Test sets, metrics, and review workflows to track quality over time and across languages.
Observability
Tracing, logs, and analytics to understand failures, latency, costs, and user outcomes.

Deployment & operations

Cloud deployment
Modern deployment with CI/CD, environment separation, and safe releases.
Cost control
Caching, routing, and model selection strategies to keep costs predictable.

Security (at the core)

Data privacy & access control
Least-privilege, encryption, secrets management, and clear data retention policies.
Prompt and tool safety
Input validation, output constraints, and SSRF/PII protections for AI tool-calls.

Robots & automation hardware

Robotics integration
Connect AI systems to robots via safe APIs, telemetry, and command validation.

IoT & edge

IoT data pipelines
Collect, normalize, and analyze sensor data, with alerts and dashboards for operations.
Edge AI
Run parts of the system close to devices when latency, connectivity, or privacy require it.

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