GenAI opportunity portfolio
Prioritize use cases by value, feasibility, risk, data readiness, and workflow fit.
etelligensAi · GenAI strategy
Shape a generative AI program around value, feasibility, governance, and adoption.
Generative AI consulting assesses how content-generating models could support a specific business task and what controls that task needs.
Business-value firstSecurity & governance by designProduction engineeringMeasured adoption
Why it matters
GenAI strategy must balance business ambition with model behavior, enterprise data, security, cost, and operating change.
Compare generation, retrieval, templates, and rules against the same acceptance criteria.
A generated answer should be judged against the user's task, not its fluency alone.
Capabilities
What Etelligens delivers.
Consulting engagements produce decisions, artifacts, and pilots that accelerate responsible execution.
Model & platform strategy
Evaluate provider, open-model, cloud, deployment, routing, and cost options.
Knowledge & RAG readiness
Assess content quality, permissions, metadata, freshness, retrieval, and source traceability.
Risk & governance framework
Define usage policies, evaluation, human oversight, data handling, auditability, and escalation.
Pilot design
Choose representative tasks, users, data, metrics, and guardrails for evidence-based pilots.
Scale roadmap
Plan platform capabilities, operating roles, integration, change management, and portfolio expansion.
Enterprise use cases
Where this capability creates value.
The strongest roadmap starts with a small number of workflows where generative AI can materially improve time, quality, or experience.
Enterprise knowledge
Reduce search and synthesis time across policies, product documentation, research, and operational content.
Customer operations
Improve agent productivity, response quality, case preparation, and self-service.
Content-intensive workflows
Accelerate drafting, review, comparison, summarization, and localization with human control.
Engineering productivity
Support documentation, testing, code understanding, modernization, and technical knowledge access.
Delivery model
From opportunity to reliable production.
Our multidisciplinary team connects product strategy, data, AI engineering, application integration, security, quality engineering, and change management.
Assess
Understand strategy, workflows, data, current experiments, security constraints, and stakeholder expectations.
Prioritize
Build a use-case portfolio and establish target metrics, guardrails, and investment assumptions.
Validate
Run focused pilots to test quality, user value, cost, latency, and integration feasibility.
Roadmap
Define the target platform, governance, operating model, delivery sequence, and scale plan.
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
Build a GenAI roadmap grounded in evidence, controls, and business value.
Talk to our AI team ↗Frequently asked questions
Generative AI Consulting: questions before you start
Practical answers on scope, delivery choices, and acceptance.
Generative AI consulting assesses how content-generating models could support a specific business task and what controls that task needs. The agreed scope can include problem definition, options, ownership, and implementation planning.
Compare generation, retrieval, templates, and rules against the same acceptance criteria. Bring the current workflow, important constraints, and the decision or user outcome you need to improve.
A generated answer should be judged against the user's task, not its fluency alone. 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 problem definition, options, ownership, and implementation planning. Request milestones and assumptions rather than an unsupported fixed-price promise.
Agree acceptance evidence before implementation. Compare generation, retrieval, templates, and rules against the same acceptance criteria. Record known limitations, unresolved risks, ownership after handoff, and the next review point.
Delivery evidence
Connect the scope to real work.
A generated answer should be judged against the user's task, not its fluency alone. Related capabilities include Enterprise Transformation Services.