Capabilities
Generative AI
Harness the transformative power of large language models and generative technologies to revolutionize how your organization creates and operates.
Unlock the Full Potential of GenAI
Generative AI is reshaping every industry. From automating complex tasks to creating entirely new products and services, the possibilities are vast—but so are the challenges.
We help organizations navigate this landscape with confidence, implementing GenAI solutions that are secure, scalable, and aligned with your strategic objectives.
Our Approach
- 1Identify high-value GenAI use cases aligned with business goals
- 2Select and customize the right foundation models
- 3Design robust prompt engineering frameworks
- 4Implement security and governance controls
- 5Build scalable production infrastructure
- 6Enable continuous monitoring and improvement
Use Cases
Discover how generative AI can transform your operations across multiple domains.
Conversational AI
Build intelligent chatbots and virtual assistants that understand context and deliver personalized experiences.
Code Generation
Accelerate software development with AI-powered code completion, review, and documentation tools.
Content Creation
Generate marketing copy, reports, and creative content at scale while maintaining brand consistency.
Knowledge Management
Transform enterprise knowledge into searchable, actionable insights with RAG-powered systems.
Generative AI Consulting That Ships, Not Demos
Almost every business has now seen a generative AI demo that looked magical and a production rollout that quietly fizzled. The gap between the two is where most generative AI consulting goes wrong. A large language model that answers cleanly in a sandbox will hallucinate, leak context, or simply annoy users once it meets the messy reality of your data and your customers. Interactive Intel exists to close that gap. We are an operator-led, founder-delivered boutique in Miami, and we treat generative AI not as a novelty to show off but as a tool that has to earn its keep inside a real workflow. The partner who scopes your engagement is the one who ships it, so the incentive is always to build something that works on Monday morning, not something that demos well on a Friday.
Our 10/20/70 methodology shapes every generative AI project. Roughly 10 percent of the outcome depends on the model and the prompts, 20 percent on the surrounding technology and data, and 70 percent on the people and process around adoption. That ratio explains why so many LLM pilots stall: teams pour effort into prompt cleverness and model selection while ignoring whether anyone actually trusts or uses the output. We invert that. We start from the workflow and the humans in it, then bring generative AI to bear on the specific place where it removes friction.
RAG, Grounding, and Trustworthy Output
The single most common request we get is a generative AI assistant that answers questions from a company's own knowledge: policies, product docs, clinical protocols, past tickets, contracts. Done naively, this produces confident nonsense. Done well, it requires retrieval-augmented generation (RAG): we index your source material in a vector store, retrieve the most relevant passages at query time, and ground the model's answer in those passages with citations the user can verify. We build these systems on a proven stack, primarily Anthropic Claude and OpenAI for generation, Supabase for storage and vector search, and Next.js on Vercel for the interface, so you are not locked into a single vendor and can swap models as the frontier moves.
Grounding is also where prompt engineering earns its name. Rather than treating prompts as incantations, we design them as structured, testable artifacts: clear instructions, explicit output formats, refusal behavior for out-of-scope questions, and evaluation harnesses that catch regressions before your users do. For regulated clients, especially healthcare practices working with FHIR and HL7 data, we add guardrails that keep protected information where it belongs and route uncertain answers to a human.
Where Generative AI Pays Off
The use cases that consistently return value for our clients are unglamorous and specific. As illustrative examples: a growth-stage services firm might deploy an internal copilot that drafts proposals from a library of past work, cutting hours off every bid; a clinic might use generative AI to summarize visit notes into structured documentation and draft patient-friendly follow-up messages for staff review; a retail operator might generate consistent, on-brand product descriptions across thousands of SKUs. In each case the win comes from narrowing the scope until the model is reliable, then putting a human in the loop where judgment matters. We resist the temptation to build a single do-everything assistant, because focused tools ship and broad ones drift.
How We Engage
Most generative AI projects begin with our AI Readiness Audit ($5K–$15K), which confirms your data is usable and identifies the single highest-value assistant to build first. From there, our flagship Agentic Workflow Sprint ($25K–$60K) takes that one opportunity from idea to production, instrumented so you can measure the time saved and the quality delivered. Organizations with several generative AI initiatives across regions move into Enterprise engagements starting at $150K. As the agentic-AI practice of Alton Group Worldwide, we stay deliberately small so founders deliver the work. If you want generative AI consulting that ends with a working, grounded, trusted system rather than another impressive demo, that is the entire point of how we operate.