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AI Development Services

AI Development Services - LLM, RAG & Agentic AI that ships.

We build custom AI systems - LLM integration, RAG (Retrieval-Augmented Generation) pipelines, AI agents, fine-tuning, and MLOps - that run in production for real users, not just pass demos in a meeting.

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Trusted by businesses worldwide
LLM + RAGCore architecture
Agentic AIMulti-step workflows
MLOpsMonitor + retrain
$1.77TAI market by 2032
Overview

AI built to run in production, not just in a demo.

77% of companies are using or actively testing AI technologies in 2026. The gap between companies that extract real value from AI and those that don't comes down to one thing: whether the system was engineered for production. A proof of concept that works in a demo fails in production when users ask unexpected questions, data changes, prompts drift, and costs spike.

At Hoop Interactive, we build AI systems engineered from the start for production: LLM integration with proper prompt management and guardrails, RAG pipelines with vector databases and retrieval evaluation, AI agents with tool-use and error handling, fine-tuned models with quantised weights for lower inference cost, and MLOps monitoring so you know when your model starts degrading before your users do.

We combine AI development with product engineering - the frontend, API, and infrastructure that lets users actually interact with the AI system. Every AI feature we ship has latency targets, cost-per-query budgets, hallucination guardrails, and a monitoring dashboard from day one.

01
LLM-powered features
Chat, search, summarisation, classification, and content generation.
02
RAG systems
AI that answers from your private documents, knowledge base, or database.
03
AI agents
Autonomous workflows that call APIs, make decisions, and complete multi-step tasks.
04
Custom ML models
Trained on your data for prediction, recommendation, and anomaly detection.

4 types of AI we build.

Each requires different architecture, we recommend the right approach for your use case.

Most In-Demand · 2026

RAG Systems

RAG connects an LLM to your private data - documents, PDFs, databases, knowledge bases. Instead of relying on the LLM's training data, it retrieves relevant chunks at query time and passes them as context. The result is accurate, cited answers grounded in your actual content, not hallucinated ones. We build the full pipeline: chunking strategy, embeddings, vector DB, retrieval evaluation, and response monitoring.

Vector DBEmbeddingsHybrid searchRerankerHallucination guardLangChain / LlamaIndex
Fastest Growing · 2026

Agentic AI Systems

AI agents don't just answer questions - they take actions: calling APIs, browsing the web, running code, querying databases, and orchestrating multi-step workflows. The agent decides which tools to call, in what order, and how to handle failures. We build with LangGraph, AutoGen, and the OpenAI Agents SDK with proper tool definitions, error handling, human-in-the-loop checkpoints, and AgentOps monitoring.

LangGraphAutoGenTool useMCPAgentOpsHuman-in-the-loop
Custom AI Assistants
Domain Specialisation

LLM Fine-Tuning

Fine-tuning trains an existing LLM on your data to specialise its behaviour, tone, format, or domain knowledge. It's the right choice when prompt engineering can't produce consistent output, when you need specific domain vocabulary, or when inference cost at scale makes a smaller fine-tuned model more economical. We run LoRA and QLoRA fine-tuning on open models (Llama 3, Mistral, Phi) with SFT, held-out evaluation, and vLLM deployment.

LoRA / QLoRALlama 3MistralSFTvLLMPEFT
Prediction & Automation

Custom ML Models

Traditional ML models outperform LLMs for structured prediction: churn prediction, demand forecasting, fraud detection, recommendation engines, and anomaly detection. We train, evaluate, and deploy custom models with scikit-learn, XGBoost, and PyTorch - with feature engineering, cross-validation, hyperparameter tuning, and FastAPI deployment with versioning and drift monitoring in MLflow or Weights & Biases.

scikit-learnXGBoostPyTorchMLflowFeature engineeringDrift monitoring
Machine Learning Development

AI, specialised.

Ten deeper AI services under this area, each solving a different class of problem.

01

AI Chatbot Development

LLM chatbots grounded in your own documents through RAG, with source citations, escalation logic, and accuracy evaluation before launch.

02

ChatGPT Integration

OpenAI's GPT models wired into your website, app, or CRM through function calling, so it answers from your data and completes real tasks.

03

Custom AI Assistant Development

Agentic assistants that plan multi-step tasks and act inside your systems, with guardrails and human approval on high-stakes steps.

04

Machine Learning

Custom models trained on your historical data for fraud detection, churn, and recommendations, prediction problems an LLM was never built for.

05

Predictive Analytics

Churn, demand, and revenue forecasts validated against real outcomes and delivered into the dashboards your team already uses.

06

Custom AI Tools Development

Purpose-built internal copilots and automation engines designed around your exact workflow, not a generic SaaS template.

07

Generative AI Development

Custom LLM applications, RAG knowledge assistants, voice AI agents and in-product copilots, tested before they reach a customer.

08

AI Agent Development

Agents that answer calls, qualify leads and clear back-office tasks, with autonomy set per action and approval gates in code.

09

Computer Vision Development

Facial analysis, object detection, OCR and visual search, tested on your own images before the full build.

10

RPA & Process Automation

Robotic process automation for systems with no API, combined with API integration, workflow tools and AI agents for the steps that need judgement.

9 AI services we deliver.

Every type of AI development work, from integration to custom model training.

LLM integration & chatbot development

Connect GPT-4o, Claude 3.5, Gemini, or Llama 3 to your product - system prompt engineering, conversation memory, streaming responses, context management, and cost-per-conversation tracking.

RAG pipeline development

End-to-end RAG: document ingestion, chunking strategy, embedding pipeline, vector database, hybrid search (dense + sparse), reranking, and response evaluation with hallucination detection.

AI agent development

Autonomous agents with tool use, multi-step planning, and API calling - built with LangGraph or AutoGen, with error handling, retry logic, human approval checkpoints, and AgentOps observability.

LLM fine-tuning

LoRA and QLoRA fine-tuning on Llama 3, Mistral, or Phi using your labelled data. SFT, held-out evaluation, quantisation for lower inference cost, and deployment via vLLM.

Predictive ML models

Custom models for churn prediction, demand forecasting, fraud detection, recommendation, and anomaly detection, trained on your structured data with proper metrics and cross-validation.

Document AI & NLP

Extract structured data from PDFs, invoices, contracts, and forms. Named entity recognition, document classification, sentiment analysis, and summarisation pipelines for unstructured data.

AI feature integration

Add AI - smart search, content generation, auto-classification, intelligent recommendations - to your existing web app, mobile app, or SaaS platform without rebuilding the product.

AI automation & workflow

Automate document processing, data extraction, email classification, content moderation, and repetitive knowledge work using LLMs and AI pipelines, replacing manual steps in workflows.

MLOps & LLMOps

Model deployment pipelines, A/B testing, drift detection, latency and cost dashboards, prompt versioning, LLM evaluation frameworks, and automated retraining triggers.

RAG vs fine-tuning vs prompting - when to use each.

The most common AI architecture choices, and the concrete decision criteria for each.

Prompt Engineering

When the LLM already has the knowledge it needs, you want fast iteration, and consistent output comes from examples and instructions rather than new data. Zero-shot, few-shot, and chain-of-thought prompting solve most general-purpose tasks. Start here - 80% of business AI use cases are solved before needing RAG or fine-tuning.

Cost: Low · Speed: Fast

RAG

When the AI must answer from private or frequently-updated data - internal documents, knowledge bases, customer records - that wasn't in the LLM's training. RAG retrieves relevant content at query time, so answers stay current without retraining. Right for AI search, support bots, internal assistants, and docs Q&A.

Data: private/live · Cites sources

Fine-Tuning

When you need consistent format, tone, or domain vocabulary that prompting cannot reliably achieve, or when inference cost at scale makes a smaller specialised model more economical. Right for code generation in proprietary frameworks, or medical/legal document processing. Requires 500–10,000+ labelled examples.

High upfront · Lower per-query

Agentic AI

When the task needs multiple sequential actions, external tool calls (APIs, databases, web search), conditional logic across steps, or decisions that depend on intermediate results. Right for automated research, onboarding, and invoice workflows. 96% of IT leaders view agents as a security risk without proper guardrails.

High per-run · Multi-step actions

Traditional ML

When the problem is structured prediction - churn, fraud, forecasting, recommendation, anomaly detection - on tabular or time-series data. LLMs are slower, costlier, and less accurate than a well-trained XGBoost or neural net here. Gives faster inference, explainable predictions, and lower cost-per-prediction at scale.

Low inference · <10ms · Explainable

RAG + Fine-Tuning Hybrid

When you need both private-data access (RAG) and specialised output format or domain behaviour (fine-tuning). The fine-tuned model handles tone and vocabulary; RAG provides current private knowledge. The architecture for production assistants in regulated industries - healthcare, legal, financial services.

Highest cost · Enterprise-grade

Proof, not promises.

A full platform rebuild powered by AI-driven automation for a multi-vendor marketplace.

All Case Studies
  • Full-Stack Development
  • FastAPI
  • AI Automation
  • Multi-vendor Marketplace
Client
BeesApp
Industry
Retail Tech
Location
Saudi Arabia
Timeline
6 Months

99.9%

AI-Powered Inventory Management

BeesApp operated on a broken legacy stack - PHP, Django, Vue - with no automated logic. We rebuilt the entire platform on Next.js, FastAPI, and Flutter with 120+ API endpoints, automated inventory management, multi-vendor payout processing, and intelligent product matching. AI-driven automation replaced manual catalogue management and order routing, reducing operational overhead while the platform scaled across Saudi Arabia at 99.9% uptime and 40% lower server costs.

99.9%

Platform uptime

74%

Faster load time

40%

Lower server costs

BeesApp platform rebuild
Read Full Case Study
Why Build With Hoop

Production AI, not impressive demos.

Many agencies build a PoC that impresses in a meeting and breaks in production. We build AI systems with latency budgets, hallucination guardrails, cost monitoring, and the fallback logic that keeps the product working when the model behaves unexpectedly.

  • 01

    Architecture decision before code

    We determine whether your use case needs prompt engineering, RAG, fine-tuning, or an agentic system before any code starts. The wrong architecture wastes 3–6 months and real budget, we've seen it happen at other agencies.

  • 02

    Hallucination guardrails from day one

    Every LLM system we build has hallucination detection, confidence scoring, and graceful fallbacks for low-confidence responses. AI that confidently gives wrong answers is worse than no AI.

  • 03

    Cost-per-query budget set in the brief

    A poorly designed system can spend $10,000/month on queries that should cost $200. We set token budgets, implement caching, and choose model sizes based on your expected query volume before deployment.

  • 04

    MLOps monitoring from launch

    Model drift, latency regressions, cost spikes, and accuracy degradation all happen in production. We wire up monitoring dashboards, alerting, and evaluation pipelines so you detect these before users report them.

How we build your AI system.

A 5-phase process from use case definition to a monitored production AI.

01

Use case & data audit

Define the specific problem AI solves, audit data quality and volume, and choose the right architecture - RAG, fine-tuning, agent, or ML model - before any code.

Architecture set here
02

Prototype & evaluation

Build a minimal working prototype with an evaluation framework - precision, recall, and human eval on 50–100 real test cases - before full development.

Measured before scaling
03

Production development

Full pipeline build: data ingestion, model integration, API layer, guardrails, cost controls, and the frontend that lets users interact with the AI feature.

Full-stack, no handoffs
04

Testing & red-teaming

Adversarial testing for prompt injection, jailbreak attempts, edge cases, off-topic queries, and data privacy leakage, before any user touches the system.

Security & safety first
05

Deploy & monitor

Production deployment with LLMOps monitoring - latency, cost-per-query, hallucination rate, and model drift tracked from day one with automated alerts.

LLMOps from launch
Our Stack

The tools we build AI with.

Every LLM provider, framework, vector database, and MLOps tool we use in production.

LLM Providers
OpenAI GPT-4oAnthropic Claude 3.5Google GeminiMeta Llama 3MistralGroq
RAG & Orchestration
LangChainLlamaIndexLangGraphOpenAI Assistants APIHaystack
Vector Databases
PineconeWeaviatepgvector (PostgreSQL)ChromaDBQdrant
Fine-Tuning & Training
Hugging FaceLoRA / QLoRAPyTorchAxolotlUnslothvLLM
MLOps / LLMOps
MLflowWeights & BiasesLangSmithSentryPrometheusGrafana
Infrastructure
AWS SageMakerGoogle Vertex AIFastAPIDockerKubernetesTerraform

Ways to work with us.

4 engagement structures that fit your AI project stage and budget.

AI PoC & prototype

Validate a specific AI use case with a working prototype and evaluation metrics, before committing to a full build. 2–4 weeks.

Best for validating feasibility

Production AI build

Full-stack AI system: data pipeline, model integration, API, frontend, guardrails, and MLOps monitoring shipped to production.

Best for committed AI features

AI feature integration

Add one or more AI capabilities - smart search, content generation, auto-classification - to your existing product without a full rebuild.

Best for adding AI to SaaS

AI consulting & roadmap

A technical audit of your AI use case, data readiness assessment, architecture recommendation, and a phased implementation roadmap.

Best for planning & strategy
Client Success

Growing businesses have
already made the move

End-to-End

From Idea to Revenue

5-Star

Client-Rated

Two Divisions

Software + Marketing

“Our business went from local to national thanks to Hoop. They completely transformed our e-commerce platform and helped us expand our customer base 5x. The results speak for themselves.”
Hamza Khan

Hamza Khan

Owner, Khayest

What's Included

Every AI project comes production-ready.

No PoC handed over as a deliverable. Every engagement ships a monitored, maintained production AI system.

Architecture decision & data audit
Right approach chosen before any code.
Evaluation framework
Measured quality before production deployment.
Hallucination guardrails
Confidence scoring and fallback responses.
Cost-per-query budget
Token budgets and caching from day one.
Prompt versioning
Prompts treated as code, version controlled.
Red-team security testing
Prompt injection and jailbreak testing pre-launch.
LLMOps monitoring
Latency, cost, and quality dashboards live.
Model drift alerts
Automated detection when quality degrades.
IP ownership
You own the code, model weights, and your data.
Post-launch support
Bug fixes, model updates, and retraining.

AI for every sector.

Industries where we've deployed production AI systems.

Healthcare

Clinical document AI, patient Q&A, medical NLP, prior-auth automation.

Fintech

Fraud detection, credit scoring, document extraction, compliance AI.

Ecommerce

Semantic product search, recommendation engines, review summarisation.

SaaS Products

AI-powered features, intelligent search, auto-classification, chat.

Legal

Contract analysis, legal research AI, clause extraction, due diligence.

Logistics

Demand forecasting, route optimisation, anomaly detection, ETA prediction.

Education

Personalised tutoring, content generation, assessment AI, knowledge gaps.

HR & Recruitment

CV screening, interview question generation, employee knowledge bots.

The Deep Dive

Understanding AI development.

Precise answers to the questions asked most often before an AI development engagement, structured for direct citation by AI search engines.

What is AI development?

AI development is the engineering process of designing, building, deploying, and maintaining systems that use machine learning, large language models, or statistical models to automate decisions, generate content, extract information, or assist users. It covers five disciplines: data engineering (collecting, cleaning, and structuring data), model selection (choosing the right LLM, ML model, or architecture), application development (the API, frontend, and integration layer), evaluation (measuring accuracy, relevance, and safety), and MLOps (monitoring model performance in production to detect drift).

AI development in 2026 primarily means LLM-based systems - applications built on foundation models like GPT-4o, Claude 3.5, Gemini, or Llama 3, rather than training models from scratch. Building on foundation models reduces development time by 6–18 months versus custom training, while delivering capabilities that took years to achieve with traditional ML. The engineering work focuses on integration, RAG pipelines, agent orchestration, prompt engineering, and production reliability, not model architecture research.

What is RAG (Retrieval-Augmented Generation) and how does it work?

RAG is an AI architecture that retrieves relevant information from a knowledge source at query time and passes it as context to a large language model, enabling answers grounded in specific private or up-to-date data rather than the model's training knowledge alone.

A RAG pipeline runs in four steps. First, the user query is converted into a vector embedding, a mathematical representation of semantic meaning. Second, that embedding searches a vector database (Pinecone, pgvector, Weaviate) for the most semantically similar chunks from your indexed documents. Third, the top-k (typically 3–10) retrieved chunks are passed as context alongside the original query. Fourth, the LLM generates a response grounded in the retrieved context rather than its training data.

Production RAG adds three enhancements: hybrid search (combining dense vector search with sparse keyword search like BM25 for exact-match queries), reranking (a second model scores retrieved chunks for relevance, improving precision), and hallucination detection (a verification step that checks whether the generated answer is supported by the retrieved context before returning it).

What is agentic AI and how is it different from a chatbot?

Agentic AI refers to systems that autonomously execute multi-step tasks by deciding which tools to call, in what sequence, and how to handle intermediate results, rather than simply responding to a single query. A chatbot responds to input; an AI agent completes a workflow.

A support chatbot answers "What is your return policy?" by retrieving a document. An agent for the same domain receives "Process this return for order #8821" and autonomously looks up the order, verifies it's within the return window, initiates a refund via the payments API, sends a confirmation email, and updates the CRM, completing the workflow without human intervention.

Agentic systems require four components beyond basic LLM integration: tool definitions (structured descriptions of APIs and functions the agent can call), planning logic (how the LLM decides which tools to use - via ReAct, LangGraph, or AutoGen), error handling (retry logic, fallbacks, and human escalation), and AgentOps monitoring (observability into decisions, tool calls, costs, and failure rates). 96% of IT leaders view agents as a security risk - guardrails, permission scoping, and audit logging are required before production.

What is MLOps and why does every production AI system need it?

MLOps (Machine Learning Operations) is the engineering discipline that keeps AI models reliable, accurate, and cost-effective after they deploy. Without it, AI systems degrade silently: accuracy drops as real-world data shifts from training data (model drift), costs spike from token overuse, latency rises with volume, and hallucination rates climb as prompts meet new data patterns.

A complete LLMOps stack covers six areas: latency monitoring (p50, p95, p99 tracked continuously), cost-per-query tracking (token usage and API spend per endpoint), quality evaluation (automated relevance scoring and human-eval sampling), prompt versioning (prompts as code with A/B testing and rollback), drift detection (statistical tests that flag output divergence from baseline), and automated retraining triggers (pipelines that update fine-tuning or RAG indexes when quality falls below thresholds).

How much does AI development cost and how long does it take?

A focused AI feature - LLM integration with RAG pipeline, API, and basic monitoring - costs roughly $15,000–$50,000 and takes 6–10 weeks. A production agent system with tool use, full LLMOps monitoring, and enterprise guardrails costs $50,000–$150,000 and takes 12–20 weeks. A custom fine-tuned model with a training-data pipeline costs $30,000–$100,000 and takes 8–14 weeks depending on data readiness.

Cost drivers differ from standard software: data quality and volume (poorly structured data multiplies engineering time 2–3×), evaluation rigour (meaningful evaluation needs 200–500 labelled test cases), LLM API cost (GPT-4o at ~$15/million tokens versus self-hosted Llama 3 at ~$0.50/million - a 30× difference at scale), and MLOps infrastructure (often 20–30% of total cost but required for reliability). We provide phased cost breakdowns after a discovery session, with the PoC priced separately so you can validate before the full investment.

Related services.

Services that pair directly with AI development.

AI App Rescue & Vibe Coding

Senior engineering review for apps built on Lovable, Bolt.new, or Base44 - build, rescue, audit, and migration.

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API Development

REST APIs that serve AI features to web and mobile clients.

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Mobile App Development

iOS and Android apps with LLM-powered features.

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Web Application Development

Full-stack web apps with AI capabilities integrated.

Explore

Ecommerce Development

AI-powered search, recommendations, and personalisation.

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Web Design

UI/UX design for AI-powered interfaces and chat products.

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SaaS Development

SaaS platforms with AI features built in from the start.

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Cloud Infrastructure

AWS and GCP infrastructure for AI model hosting and scaling.

SEO & GEO Services

Optimise for Google Search and AI answer engines simultaneously.

More services in this area
Custom SoftwareCloud & DevOpsData & AnalyticsSecurity, QA & MaintenanceGame Development
FAQ

AI Development Questions

The things clients ask us most before every AI project, answered directly.

RAG connects an LLM to external data at query time; fine-tuning trains the LLM's weights on your data to change its behaviour permanently. RAG is better when answers must come from private, frequently updated data - documents, databases, product catalogues - because the knowledge stays current without retraining. Fine-tuning is better when you need consistent tone, format, or domain-specific vocabulary that prompt engineering alone can't produce reliably. Most production AI systems start with RAG; fine-tuning is added only when RAG plus prompt engineering doesn't produce sufficient quality on a specific task.

Yes. Adding AI features to an existing product is one of the most common engagement types we handle. It typically involves building an AI API layer alongside your existing backend - the LLM integration, RAG pipeline, or ML model runs as its own service, and your existing product calls it via API. We connect the AI output to your existing frontend, CMS, or database without a full platform rebuild. Common additions include semantic search, auto-classification, document summarisation, AI chat assistants, and recommendation engines.

Four techniques reduce hallucinations: RAG grounding, confidence scoring, constitutional AI constraints, and output validation. RAG grounds responses in retrieved documents, if the document doesn't say it, the system prompt instructs the model not to fabricate it. Confidence scoring assigns a probability to each response; low-confidence answers trigger a fallback. Constitutional constraints prevent specific categories of incorrect output, and output validation checks factual claims against structured data where possible. None of these eliminates hallucinations completely, every production AI system has a residual error rate that must be disclosed and monitored continuously.

The right LLM depends on four factors: task requirements, data privacy, inference cost, and latency. GPT-4o and Claude 3.5 Sonnet are strong general-purpose choices with excellent reasoning; Claude performs particularly well on document analysis and long-context tasks. Where data privacy prohibits sending data to external APIs, open-source models (Llama 3 70B, Mistral Large) on your own infrastructure are the right choice, at roughly 10–30× lower inference cost at scale. For latency-critical applications, smaller models (GPT-4o-mini, Claude 3 Haiku, Llama 3 8B) are preferable. We test 2–4 models on your specific task during the prototype phase before committing to one for production.

A focused RAG-based AI feature takes 6–10 weeks from discovery to production. An agentic AI system with multi-step workflows takes 12–20 weeks. A fine-tuned custom model takes 8–14 weeks depending on data readiness. Data quality is the most common schedule risk, poorly structured or insufficient labelled data adds 3–6 weeks to any AI project. We run a 1–2 week discovery phase that audits data readiness, defines the evaluation framework, and produces a phased timeline before any build commitment.

Yes. You own 100% of the code, fine-tuned model weights, training data, and any custom evaluation datasets produced during the engagement. Your data is never used to train models for other clients. For projects using proprietary LLM APIs, you hold the API key and control your data under the provider's terms; for fine-tuned open-source models, you own the weights outright and can deploy on any infrastructure. We document the full ownership transfer in the project agreement before work starts.

Model drift occurs when an AI system's output quality degrades over time because real-world input data diverges from the data used during training or evaluation. Three types affect production AI: data drift (the distribution of user queries changes), concept drift (the correct answer changes because underlying facts change), and pipeline drift (upstream changes to the knowledge base or retrieval system alter the context the LLM receives). We detect drift using automated evaluation pipelines that score a held-out test set weekly, a drop in relevance or accuracy above a threshold triggers an alert and a review cycle. Prompt versioning lets us roll back if a change causes a regression.

Yes. GEO (Generative Engine Optimisation) and AEO (Answer Engine Optimisation) require structuring content so AI search engines - ChatGPT, Perplexity, Google AI Overviews, Claude - can accurately extract and cite it. We build web content with direct-answer structure: questions as headings, bold answers immediately following questions, specific numeric values, named entities, and clear attribution, exactly the format LLMs prefer when generating search answers. Our marketing team applies GEO/AEO principles to every service page, and our AI team can build structured knowledge bases and schema-rich content systems that feed AI crawlers with consistently citable information.