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.
Generative AI development builds applications that generate or transform content within defined product and data boundaries.
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.
Test grounding, output validation, prompt injection exposure, version changes, and human review paths.
Keep evaluation examples separate from demonstrations used to sell the idea.
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.
What clients say about us
Trusted for responsiveness, delivery quality, and ownership.
Feedback from clients who have worked with Etelligens across application development, web platforms, branding, and complex software delivery.
01
Praised the team’s responsiveness, willingness to go beyond the agreed scope, and the quality of the completed application.
Joshua HarrisEtelligens client
02
Highlighted the quality of the website, strong troubleshooting, fast understanding of requirements, and a positive overall delivery experience.
Dean EdelsonEtelligens client
03
Commended the booking-application team for identifying overlooked issues, exceeding expectations, and delivering a polished finished product.
Dr. Matthew MaggioEtelligens client
04
Said the team captured the brand’s identity effectively, communicated promptly across Western time zones, and earned continued work on product and service branding.
Joel LogicEtelligens client
05
Described the team as highly capable and accessible, crediting them with rescuing a difficult software project and consistently going the extra mile to deliver on time.
SargeEtelligens client
06
Highlighted faster-than-expected delivery, close adherence to requirements, and strong communication throughout the web-development project.
Christopher SandsEtelligens client
1 / 2
Move generative AI from prototype to a reliable business capability.
Talk to our AI team ↗Frequently asked questions
Generative AI Development: questions before you start
Practical answers on scope, delivery choices, and acceptance.
Generative AI development builds applications that generate or transform content within defined product and data boundaries. The agreed scope can include AI delivery scope, data readiness, evaluation, and rollout controls.
Test grounding, output validation, prompt injection exposure, version changes, and human review paths. Bring the current workflow, important constraints, and the decision or user outcome you need to improve.
Keep evaluation examples separate from demonstrations used to sell the idea. Ask the delivery team to explain the alternatives, exclusions, and evidence that would change its recommendation.
Scope, integration dependencies, data readiness, access approvals, and acceptance requirements determine the estimate. For this work, plan explicitly for AI delivery scope, data readiness, evaluation, and rollout controls. Request milestones and assumptions rather than an unsupported fixed-price promise.
Agree acceptance evidence before implementation. Test grounding, output validation, prompt injection exposure, version changes, and human review paths. Record known limitations, unresolved risks, ownership after handoff, and the next review point.
Delivery evidence
Connect the scope to real work.
Keep evaluation examples separate from demonstrations used to sell the idea. Related capabilities include AI Governance Consulting.