GenAI product architecture
Design model, retrieval, memory, tools, orchestration, application, and integration layers.
etelligensAi · Generative AI engineering
Engineer generative AI applications that are grounded, integrated, and production ready.
Build copilots, assistants, content systems, knowledge experiences, and generative workflows with the architecture, evaluation, security, and operational controls required for enterprise use.
Business-value firstSecurity & governance by designProduction engineeringMeasured adoption
Why it matters
Generative AI creates value when the model is only one part of a well-engineered product and workflow.
Etelligens combines foundation models with retrieval, tools, APIs, application logic, permissions, human review, and observability to create reliable business applications.
We select model and deployment patterns based on quality, latency, privacy, cost, context requirements, and portability rather than defaulting to a single provider.
Production readiness includes evaluation datasets, prompt and configuration management, safety controls, auditability, fallback behavior, monitoring, and lifecycle ownership.
Capabilities
What Etelligens delivers.
Our teams can build a new GenAI product, modernize an existing workflow, or integrate generative capabilities into enterprise applications.
Prompt & context engineering
Structure system instructions, context assembly, templates, output formats, and task-specific constraints.
RAG & knowledge systems
Ground outputs in enterprise content with metadata, permissions, ranking, citations, and freshness controls.
Model integration & routing
Use commercial, open, or specialized models with routing based on task, quality, latency, and cost.
Evaluation & safety
Build golden datasets, automated evaluation, red-team scenarios, guardrails, and human review.
LLMOps & observability
Monitor quality, latency, token usage, failure patterns, costs, and configuration changes in production.
Enterprise use cases
Where this capability creates value.
Common programs combine multiple patterns rather than treating generative AI as a standalone chatbot.
Knowledge copilots
Answer complex questions across enterprise knowledge with citations and role-aware access.
Content operations
Draft, transform, classify, review, and personalize content with controlled workflows and approvals.
Software engineering assistance
Support code understanding, documentation, testing, migration, and developer productivity.
Document intelligence
Extract, compare, summarize, validate, and route information from contracts, forms, reports, and policies.
Delivery model
From opportunity to reliable production.
Our multidisciplinary team connects product strategy, data, AI engineering, application integration, security, quality engineering, and change management.
Frame
Define user tasks, output expectations, acceptable error, privacy, and measurable value.
Prototype
Evaluate model choices, prompts, retrieval, workflows, latency, and cost using representative data.
Engineer
Build secure integrations, application experience, evaluation, observability, and operational controls.
Scale
Harden the platform, manage lifecycle changes, optimize cost, and expand use cases based on evidence.