Generative AI development services that make it to production.
Hoop Interactive builds custom LLM applications, RAG knowledge assistants, voice AI agents and in-product copilots that answer from your data, plug into your systems and are tested before they reach a single customer.
Most generative AI stops at the demo.
A chatbot that impresses in a meeting is easy. One that answers correctly at 2am, keeps customer data private and costs less than the person it helps is a different build. We close four gaps on every project: answers that make things up, no measured accuracy, surprise bills, and an AI that can't actually do anything.
Our approach: grounded answers with sources, an evaluation suite, a cost forecast per request and real integrations from week one.
- Grounded, not guessed
- Answers sourced from your documents, not model memory.
- Measured, not assumed
- An evaluation set before every release, not a vibe check.
- Forecast, not surprised
- Cost per request estimated before you approve the build.
- Connected, not isolated
- Function calls into your CRM, phone system and APIs.
What we build with generative AI.
Custom generative AI solutions, from a single feature in your product to a full AI application.
Custom LLM applications
Web and mobile apps built around a large language model, with your UI, logic and data.
RAG knowledge assistants
Ask questions across policies, manuals and tickets, with every answer linked to its source.
Voice AI agents
Real-time phone agents on OpenAI's Realtime API that qualify leads and handle callbacks.
AI copilots in your product
In-app assistants that draft, summarise and take actions for your SaaS users.
Generative AI integration
OpenAI, Claude or Gemini APIs added to your CRM, helpdesk, telephony or website.
Document & content generation
Proposals, reports, product descriptions and emails drafted on-brand from your templates.
Fine-tuning & evaluation
Fine-tuned or smaller open-source models when style, speed or cost matters most.
Generative AI consulting
A two-week discovery sprint: use cases ranked by value, data readiness and a costed plan.
Inside a production generative AI system.
The model is one layer of six. The other five decide whether the system is accurate, safe and affordable.
PineconeHow we keep confidential data away from the model.
Sending customer or company data to an LLM is the first worry in every discovery call. These controls are built in from the first prototype, not added before launch.
No training on your data
Enterprise API settings with zero data retention where the provider offers it.
PII redacted before it leaves
Names, emails, IDs and card numbers masked before any prompt is sent.
Permission-aware retrieval
The AI only retrieves documents the signed-in user is already allowed to open.
Private deployment options
Azure OpenAI, AWS Bedrock or open-source models hosted on your own cloud.
Audit logs and retention rules
Every prompt and response logged, with deletion schedules that match your policy.
How we differ from a typical generative AI development company.
Most GenAI pages promise "endless possibilities". Here is what you actually get.
| What matters | Typical GenAI vendor | Hoop Interactive |
|---|---|---|
| Proof | Concept videos and stock "AI" imagery. | Voice agents running in production for insurance clients, with case studies. |
| Accuracy | Judged by how the demo feels. | An agreed evaluation set and pass mark before launch. |
| Models | Locked to one provider, often an outdated model. | Model-agnostic routing, re-tested as better models ship. |
| Running cost | Unknown until the first invoice. | Cost per request forecast in discovery and tracked live. |
| Data | Vague about where your data goes. | Written data flow, PII redaction and private hosting options. |
| Adoption | Handover and hope people use it. | Training, in-app feedback and, if you need it, our marketing team to launch it. |
From idea to production in six steps.
You can stop after any step and keep everything we've built so far. Total time to production is usually 8 to 14 weeks.
Discovery sprint
We rank your use cases by value and feasibility, audit your data and forecast running costs.
1–2 weeks · Costed plan & ROIBuild the test set
Together we collect real questions and correct answers. This becomes the definition of "good enough".
Week 2 · Evaluation setPilot on your data
A working prototype with your documents and a small group of real users, compared across models.
3–5 weeks · Pilot resultsProduction build
Integrations, guardrails, user interface, permissions, monitoring and load testing.
4–8 weeks · Staging systemLaunch
Phased rollout with human review on early conversations and alerts on cost and quality.
1–2 weeks · Live system & runbookImprove
Monthly review of failures and feedback, prompt and retrieval tuning, and model upgrades.
Ongoing · Monthly quality reportGenerative AI we've put into production.
A realtime voice agent on a 3CX phone system for a UAE insurance platform.
VOICE AI · TELEPHONY · INSURTECH
A Python and FastAPI service bridging 3CX and GPT-4o Realtime
Connects OpenAI's GPT-4o Realtime API to the client's 3CX PBX over persistent WebSockets, so an AI agent can hold live conversations for vehicle insurance sales callbacks.
Read the case studyGenerative AI development cost and timelines.
Start small, prove value, then scale. Each stage has a fixed price. Model API and hosting fees are paid directly to the provider — we estimate them in discovery using your expected volume, then track them live.
- Investment
- from $4,000
- Timeline
- 1–2 weeks
- Output
- Use-case workshop & ranking
- Included
- Data readiness audit, cost forecast
- Yours to keep
- Costed roadmap
- Investment
- from $12,000
- Timeline
- 3–5 weeks
- Build
- Working app on your real data
- Evaluation
- Scoring across 2–3 models
- Output
- Go / no-go report
- Investment
- from $25,000
- Timeline
- 8–14 weeks total
- Integrations
- CRM, phone or APIs
- Guardrails
- Full, with PII redaction & audit logs
- Included
- Load testing & runbook
Generative AI solutions by industry.
Same engineering, tuned to the rules and language of each industry.
Insurance
Voice agents for lead follow-up, renewals and policy questions.
Ecommerce & Retail
Product content, shopping assistants and order support.
Health & Wellness
Booking assistants and member support, with no medical advice.
Real Estate & Home Services
Enquiry handling, quote drafting and listing descriptions.
Professional Services
Document review, proposal drafting and internal knowledge search.
SaaS & Startups
AI features and copilots that set your product apart.
Explore more AI services.
Generative AI development pairs naturally with these services from Hoop Interactive.
AI Development
The broader practice this service sits under.
ExploreAI Agent Development
Agents that act, not just answer questions.
ExploreAI Chatbots
Grounded, document-aware chat assistants.
ExploreChatGPT Integration
OpenAI's models wired into your existing tools.
ExploreCustom AI Assistants
Agentic assistants with guardrails and approvals.
ExploreMachine Learning
Prediction problems an LLM was never built for.
ExploreAPI & Integrations
The connective layer behind every AI feature.
ExploreSaaS Development
Where most AI copilots end up living.
ExploreGenerative AI development questions
About our generative AI development services. Ask us anything else.
A two-week discovery sprint starts from $4,000, a working pilot on your data from $12,000, and a production system from $25,000. Model and hosting costs are separate, and we forecast them per request before you approve the build.
Discovery takes 1 to 2 weeks, a pilot 3 to 5 weeks, and a production launch 8 to 14 weeks in total. Our InstantAlfred voice agent for a UAE insurance platform was built in one month.
We are model-agnostic. We test OpenAI GPT models, Anthropic Claude, Google Gemini and open-source models such as Llama against your own examples, then pick the one with the best accuracy, speed and cost for each task.
No. We use enterprise API settings that do not train on your data, can deploy through private cloud options such as Azure OpenAI or AWS Bedrock, and can host open-source models on your own infrastructure when data must not leave it.
We ground answers in your documents with retrieval-augmented generation, show the sources behind each answer, test every release against a set of real questions with known answers, and route low-confidence cases to a person.
Most business use cases start with RAG because it uses your latest data and is cheaper to update. Fine-tuning helps when you need a consistent style, format or a smaller, faster model for one narrow task. We often combine both.
Yes. We have connected LLMs to 3CX and Twilio telephony, CRMs, WhatsApp, databases and internal APIs, so the AI can read context and take actions such as logging a lead or booking a call.
Generative AI development builds systems that create text, speech, images or code using large language and diffusion models. Our broader AI development service also covers predictive machine learning, analytics and computer vision.
Yes. You own the code, prompts, evaluation datasets and configuration, and model API accounts sit in your name, so you are never locked in to us or to one model provider.